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1、ANALYTICS IN FINANCE AND ACCOUNTANCY 2020 Association of Chartered Certified Accountants and Chartered Accountants ANZ September 2020About ACCA ACCA is the Association of Chartered Certified Accountants.Were a thriving global community of 227,000 members and 544,000 future members based in 176 count
2、ries that upholds the highest professional and ethical values.We believe that accountancy is a cornerstone profession of society that supports both public and private sectors.Thats why were committed to the development of a strong global accountancy profession and the many benefits that this brings
3、to society and individuals.Since 1904 being a force for public good has been embedded in our purpose.And because were a not-for-profit organisation,we build a sustainable global profession by re-investing our surplus to deliver member value and develop the profession for the next generation.Through
4、our world leading ACCA Qualification,we offer everyone everywhere the opportunity to experience a rewarding career in accountancy,finance and management.And using our respected research,we lead the profession by answering todays questions and preparing us for tomorrow.Find out more about us at About
5、 Chartered Accountants Australia and New Zealand Chartered Accountants Australia and New Zealand(Chartered Accountants ANZ)represents more than 125,000 financial professionals,supporting them to build value and make a difference to the businesses,organisations and communities in which they work and
6、live.Around the world,Chartered Accountants are known for their integrity,financial skills,adaptability and the rigour of their professional education and training.Chartered Accountants ANZ promotes the Chartered Accountant(CA)designation and high ethical standards,delivers world-class services and
7、life-long education to members and advocates for the public good.We protect the reputation of the designation by ensuring members continue to comply with a code of ethics,backed by a robust discipline process.We also monitor Chartered Accountants who offer services directly to the public.Our flagshi
8、p CA Program,the pathway to becoming a chartered accountant,combines rigorous education with practical experience.Ongoing professional development helps members shape business decisions and remain relevant in a changing world.We actively engage with governments,regulators and standard-setters on beh
9、alf of members and the profession to advocate in the public interest.Our thought leadership promotes prosperity in Australia and New Zealand.Our support of the profession extends to affiliations with international accounting organisations.We are a member of the International Federation of Accountant
10、s and are connected globally through Chartered Accountants Worldwide and the Global Accounting Alliance.Chartered Accountants Worldwide brings together members of 13 chartered accounting institutes to create a community of more than 1.8 million Chartered Accountants and students in more than 190 cou
11、ntries.Chartered Accountants ANZ is a founding member of the Global Accounting Alliance,which is made up of 10 leading accounting bodies that together promote quality services,share information and collaborate on important international issues.ANALYTICS IN FINANCE AND ACCOUNTANCYThis global research
12、 explores how analytics affects finance and accountancy teams within organisations,as well as the roles and skills of professional accountants.The insights in this report are based upon over 30 in-depth interviews with finance leaders,financial analysts and data specialists across the globe,represen
13、ting a range of industries,and from a range of industries and organisation types.The report draws on these interviews to demonstrate at first hand the natural evolution from finance departments that handle just financial information to those dealing with a diverse array of non-financial information,
14、generating value and using business intelligence to produce tangible results.Insights have also been drawn from a survey of 1,150 accountancy and finance professionals,including ACCA and Chartered Accountants ANZ members and future members.The survey was conducted in October 2019.Locationn United Ki
15、ngdom,17%n Malaysia,7%n Pakistan,6%n Australia,5%n Accountancy,20%n Financial services small/medium sized,6%n Financial services large,8%Sectorn Hong Kong SAR,4%n United Arab Emirates,4%n Republic of Ireland,4%n Other,54%n Corporate sector,46%n Not-for-profit,5%n Public sector,11%n Other,4%Rolen Pro
16、fessional services,15%n Professional services leadership,3%n Finance and accounting,25%n Risk management and compliance,19%n Finance leadership,20%n Education,0%n Other,18%ForewordThe technological revolution of which we are a part has vastly increased the amount of data and information that is avai
17、lable to us.From that data we can generate insights and support effective decision making.Finance and accountancy professionals need to be at the forefront of this analytics revolution.The COVID-19 pandemic has heightened the need for organisations to be agile and responsive,developing plans to cope
18、 with a range of scenarios and opportunities as they continue to evolve.The agile and the nimble have exploited their data assets and developed responses that can be implemented readily and used to support business survival.Yet the findings of this report are that many accountancy and finance profes
19、sionals are still using rearward-looking analytics:reporting on past performance and telling their stakeholders what has happened.There has never been a greater need to invest in forward-looking analytics that help decision makers explore the potential range of options for the future and match this
20、to the monitoring of current actions.If accountancy and finance professionals fail to develop our skills in the appropriate directions,to provide our stakeholders with forward-looking insights,they run the risk of being marginalised.Both ACCA and Chartered Accountants ANZ continue to evolve their qu
21、alifications and continuing education programmes to ensure that members and future members can develop the relevant skills.In this report we offer insight as to the skills needed and reflect upon the need for accountants to understand the problem and articulate the analysis,as much as their need for
22、 skills relevant to data and applications use.This is a developing area,and continued advances in technology and how we choose to apply it in our everyday lives will mean that the opportunities for forward-looking insights will continue to grow.ACCAs partner NTT DATA offers some insightful perspecti
23、ves on this in the report.Helen Brand Chief executive,ACCAAinslie van Onselen Chief Executive Officer,Chartered Accountants Australia and New Zealand4ContentsExecutive summary 81.The opportunity for analytics in finance and accountancy 14 1.1 Analytics the story so far 14 1.2 What is the opportunity
24、?15 1.3 The analytics project 17 1.4 The biggest opportunities 18 1.5 Four Vs of big data 18 1.6 Challenges in maximising analytics 22 1.7 Data strategy 232.The current state of play 26 2.1 Technologies supporting data analytics 26 2.2 Where is analytics being used?30 2.3 Who is responsible?31 2.4 R
25、esponsibilities for implementing data analytics in the medium term 33 2.5 Skills needed to derive value from analytics 33 Core technical and softer skills 34 Data and analytics skills needed 34 A new analytics model?36 Developing the skills 36 What did students learn?373.Types and uses of analytics
26、41 3.1 Four types of analytics 41 Descriptive analytics 42 Diagnostic analytics 43 Predictive analytics 43 Prescriptive analytics 43 3.2 The future of analytics 464.Leveraging analytics in your business 48 4.1 The business case 48 Steps for implementation 49 4.2 Data insight and reporting 52 4.3 Dev
27、eloping analytics capability in the finance team 525.Ethical and legal considerations 54 5.1 Ethical considerations 54 5.2 Data governance 55 5.3 Privacy 55 5.4 GDPR and other regulations 56 5.5 Security cyber and other threats 5656.Lessons learned 587.Emerging trends in analytics 60 7.1 Integrated
28、technology framework 60 7.2 Unstructured and structured data in the future 60 7.3 Big data mindset 61 7.4 Artificial intelligence(AI),machine learning and tools 628.Actions to consider 64 8.1 Governance and data management 64 8.2 Big data is a reality 65 8.3 Hybridisation of talent 65 8.4 Decision-m
29、aking enablement 66 8.5 Predictive and prescriptive analytics 66Acknowledgements 68References 696THERE HAS NEVER BEEN A GREATER NEED TO INVEST IN FORWARD-LOOKING ANALYTICS THAT HELP DECISION MAKERS EXPLORE THE POTENTIAL RANGE OF OPTIONS FOR THE FUTURE AND MATCH THIS TO THE MONITORING OF CURRENT ACTI
30、ONS.7ANALYTICS IN FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARYThe role of the chief financial officer(CFO),and of finance and accountancy has moved beyond traditional financial and accounting oversight into working increasingly as key advisers to business,where the critical need is for making near real
31、-time,data-enabled decisions.Executive summaryFinance teams have long used descriptive analytics,presenting information as dashboards and reports describing past events.Even today,it is rare to find extensive use of predictive and prescriptive analytics.We interviewed the chief executive officers(CE
32、Os),CFOs and finance team members of over 30 organisations globally from a range of companies,including retail,telecommunications and utilities.The data accessible to finance teams has grown rapidly over the last decade and in line with the exponential growth of data from the Internet of Things(IoT)
33、,eg sources of data from sensors and equipment.This trend shows no sign of slowing down.Two roles exist for the CFO and finance teams,the first involving,as always,addressing finance-related problems.The other,an emergent role as seen from the survey responses and in-depth interviews,is being the en
34、abler of decision making for the entire organisation by taking on the championing of analytics use beyond the traditional finance boundaries.All the interviewees expressed a desire to achieve better and faster decision making for their organisations.This is the primary focus of analytics.For example
35、,a European utility finance function no longer focuses on just financial data.But through leveraging analytics champions and blending technical data from electricity distribution equipment and tools,the finance team helps make decisions on service provisioning and grid maintenance that go well beyon
36、d the finance issues.In a North American organisation providing services to contact centres,the CFO makes strong use of analytics to help make decisions on pricing and technical engineering and support requirements,thereby directly contributing to the global growth of the organisation.The partner of
37、 an Australasian Big Four professional services firm now sees broader engagement with the use of big data to help clients establish where internal controls have broken down for entire organisations and understand every single transaction,going well beyond the purview of traditional audit.The turning
38、 point for CFOs and finance teams is to move beyond improvements in financial decision making to driving analytics across entire organisations.A changing role for the CFO and finance teamsACCA and Chartered Accountants ANZ jointly conducted the interviews with finance and accountancy professionals a
39、cross the world to explore the impacts of analytics on CFOs and finance team members.Five key areas of focus and action emerged from the conversations,highlighting the journey commencement in 2020 over the mid-term to 2023.These were:ngovernance and data management nbig data is a reality nhybridisat
40、ion of talent ndecision-making enablement and npredictive/prescriptive analytics.Furthermore,our survey shows that analytics is key to driving business efficiencies,improving planning,budgeting and forecasting,improving risk management.Therefore,during this unprecedentedly volatile time,leadership n
41、eeds to show vision,understanding,courage and adaptability(George 2017).CFOs and their finance teams are at the forefront of guiding organisations through unforeseen challenges,using data and analytics to generate the right information at the right time to enable appropriate decisions.8ANALYTICS IN
42、FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARYThe analytics opportunity for finance teamsAnalytics is an essential business skill set,and analytics technology is a critical mechanism helping extract value from data in organisations.But the emergence of big data as a reality beyond financial information t
43、o include a diverse array of non-financial information sources from the so-called Industry 4.01(Figure ES1)technologies of machines,customer relationship management(CRM)systems,mobiles,sensors and external data,including environmental data,places analytics at a critical juncture.Data complexity is i
44、ncreasing significantly,large volumes of data,as well as a variety of data sets,are available but not limited to the business departments of sales,marketing,finance and manufacturing.Among this flood of data,executives find difficulties in making decisions about what actions to take.The CFO who is a
45、ble to assess the disruption COVID-19 is creating to both the business model and operations has the opportunity to take analytics capability and data well beyond the finance function to assist and support the enterprise holistically during difficult times and beyond.Along with developing new talent
46、and skill sets,and through investment in technology,using analytics in finance enables the CFO and finance team to generate competitive advantage and growth for the entire enterprise.This report highlights the role of the CFO and finance team in supporting and expanding the analytics paradigm beyond
47、 the boundaries of the finance function.The message from finance leaders,professionals and data specialists is clear.Better,faster data-driven decision making across organisations will arise from accountancy and finance professionals led by bold CFOs who embrace analytics.Furthermore,the analytics c
48、apabilities providing rear-view insights are giving way to analytics providing future-looking scenarios and predictions while supporting management,operational and strategic decision making.Yet our survey shows a reluctance among respondents to move from looking backwards to looking ahead.Traditiona
49、l reports are in the comfort zone for the majority of finance teams.Such reports are institutionalised and their relevance to decision making is often not questioned.Often these reports go unread and modern visual techniques are not used to highlight key insights.Today,report readers are looking for
50、 quick,30-seconds-to-one-minute reads,they want to decide quickly and move on so the businesses can remain agile and keep up with rapidly changing global markets.Finance teams need to use data to look ahead to remain relevant in organisations.They need to address the weighting of time applied to his
51、toric analysis and balance this between historic and forward-looking analysis.These skills are already in the tool kit of the finance professional;the need is to increase the use of those skills relevant to todays business needs.Skill sets needed to derive value from analyticsIt is important to ensu
52、re that you invest in the relevant skills to support maximising the value from analytics(Figure ES2).These skills are a combination of technical skills,application proficiency and softer skills such as critical thinking and problem solving.The ability to relate the analytics to the business issue is
53、 fundamental.It is not just a technical issue it is a business issue.FIGURE ES1:Industry 4.01 A reference to the term the fourth industrial revolution created by computerised technology and artificial intelligence and coined by Klaus Schwab,the Founder and Executive Chairman of the World Economic Fo
54、rum(Schwab 2017).Internet of ThingsSmart SensorBig Data AnalyticsAugmented realityAdvanced RoboticsCloud ComputingLocation Direction3D PrintingIndustry 4.0Finance and accountancyData literacyData extractionApplication knowledgeProblem solving and critical thinkingVisualisationStorytellingFIGURE ES2:
55、Skills needed to derive value from analytics9ANALYTICS IN FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARYSo,in summary,what should the CFO and finance team do?Consolidating insights from the in-depth interviews supporting this study and the analytics in finance survey,five key areas of focus and action em
56、erge as essential to ensure the creation of truly data-centric,analytics-driven organisations in 2020 and beyond(Figure ES3).They are governance and data management,big data is a reality,hybridisation of talent,decision-making enablement,and predictive/prescriptive analytics.FIGURE ES3:Five areas of
57、 focus and action for the future development of analytics in finance teamsGovernance and data managementBig data is a realityHybridisation of talentDecision-making enablement Predictive and prescriptive analytics The trends have applicability not only across different sectors but for small businesse
58、s and large enterprises alike.An overarching strategy is to embrace and encourage transdisciplinary analytics crossing traditional boundaries,eg finance,analytics and cybersecurity.To ensure proper data governance the CFO and inance team should:nensure that you have appropriate data governance proce
59、dures in place to classify data correctly and align its use to strategic objectives nensure that data ownership across the organisation is clearly established nalign data collection with needs for data-driven insight and avoid collecting data for which no users exist nminimise security risks by avoi
60、ding use of personally identifiable information(PII)in analyses nensure that data cleaning and preparation do not remove important data that could be used to identify outliers and anomalies2 nchampion the development of a playbook for the correct use of data in conjunction with other functions and t
61、eams.Given the emergence of big data is a reality for the CFO and finance team,they should:ndevelop and execute a strategy for the finance function that focuses on appropriate technologies,such as Cloud-based services,that support the development of a real-time analytics capability nconsider how dat
62、a extraction can be best managed from legacy systems to ensure that analytics capabilities can be developed ngenerate a catalogue of certified external data sources complementing internal data,refreshed on a regular basis to ensure that new sources are captured.Finance should own the catalogue and u
63、pdate process as the use of external data leads to a direct impact on financial decisions and beyond nensure that analytics projects support accessibility to more diverse structured and unstructured data,recognising the need to collaborate across the organisation in data collection nevaluate activit
64、ies that lead to descriptive analytics to determine whether they continue to add value to the organisation.2 Anomalies have information value,as seen from the work of Alan Turing and his team at Bletchley Park during the Second World War.They used anomalies to decrypt the Enigma cipher(Hamer 1997).1
65、0ANALYTICS IN FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARYSo,in summary,what should the CFO and finance team do?Beyond this,to move towards hybridisation of talent the CFO and finance team should:nroll out a data literacy programme for everyone,using examples ranging from improvements to in-house finan
66、cial decision making to automation of activities by any other areas of the organisation nextend analytic capabilities to a wider group of users;this will require investment in the right mix of skills and capabilities to ensure that you adjust the appropriate balance for changing business needs ncura
67、te self-service resources for the organisation,including podcasts and online communities nhost regular educational events,hackathons and an analytics book club,alternating between external guest speakers and those from different teams in the organisation to ensure that skills remain current.Decision
68、-making enablement for the CFO and finance team will enable them to:ncommunicate with internal stakeholders about how data analytics,in particular predictive analytics,can assist in the strategic decision-making process nready the finance team to help enable better and faster decisions across the en
69、tire organisation by developing a model for using real-time data to make decisions ncategorise decision making for strategic(C-level),managerial(functional),operational(business process),development(new product or service)and customer differentiation(external customers)nrevise business-case document
70、ation to identify projects that focus on predictive and ideally prescriptive analytics nkeep track of any disruptive innovations to help achieve better,faster decision making.Use of predictive and prescriptive analytics across the organisation will enable the CFO and finance team to:nstandardise the
71、 master data(the most valuable information shared across the organisation)in the organisation nbegin to identify outliers across the organisation on a continuous basis,to give a potential early warning of an emerging problem or opportunity ndevise Excel templates for the decision-making model to hel
72、p operationalise thinking about decision making nexplore the potential for using open source components for the entire analytics value chain a structured query language(SQL)database,machine learning and end-to-end processing;this is especially important if you are providing analytics software and se
73、rvices either as a business serving practices and SMEs or a BI Competency Centre servicing internal clients npilot collaboration between experts and business users using open-source software such as RapidMiner or KNIME.11A perspective from NTT DATABusinesses of all sizes have easy,low-cost access to
74、 powerful tools for the analysis and manipulation of data.There are still certain skills in data engineering and data science that are required,but exploiting data is no longer a question of overcoming technical challenges;rather a challenge of managing people.There must be buy-in at all levels of a
75、 business that what the data shows is worth knowing,that the data is clean and well curated,and that analytics is undertaken with high levels of rigour and accuracy.If all of these things can be achieved,business leadership will trust data.They will trust analytics.And they will trust the insights t
76、hat are generated.There is undeniably a skills shortage in the fields of data engineering and data science.Gartner estimate that half of businesses lack the data literacy and artificial intelligence skills that they require(Panetta 2019).So how can this be overcome?There are some realities that need
77、 to be accepted:nThe perfect data scientist,who understands finance,sales,marketing,and machine learning does not exist.Analytics should be a dialogue between business stakeholders and technologists.By creating blended teams,insights can be generated more quickly and to the exact requirements of the
78、 business.nMaintaining data quality is hard.It is essential to be transparent about the quality of the data that exists and is in use,so informed decisions can be taken on how much weight to give the analytics.To enhance the quality of the data,organisations need to work to embed the principles of d
79、ata as a shared asset and shared responsibility.nGetting good insight from your data is going to take a concerted effort and will cost money,but the potential returns on investment are significant and can make the difference between a surviving business and a thriving one.If a business can accept th
80、ese realities and work to overcome them,they will have gone a long way to building a culture of data trust.Once this trust has been built,a world of analytical opportunity will open up;the move from examining historical data to see what has happened,will change to using data to predict what may happ
81、en.This shift to forwards looking analytics,will enable businesses to see the impacts of changes before they are taken and will let them run smarter,leaner and more profitably.We will continue to need to look backwards,in part to validate the accuracy of our new predictive models.Building this trust
82、 is a core principle of successful analytics programmes and finance teams are excellently placed within modern businesses to be the champions of this trust.Finance professionals hold many of the skills to bridge the gap between data scientists and businesses.They already hold a position of trust in
83、regards to financial data and can easily apply their knowledge to data in other domains.This report sets out some clear steps that the CFO and finance team can take to begin this journey to embracing data and analytics and being the agents of change to allow businesses to make the most of this preci
84、ous asset.In the past,technology has been a barrier between businesses and the exploitation of data.The cost and complexity of the technology required made it hard to foresee a return on investment.This,however,is no longer the case.ANALYTICS IN FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARYSimon William
85、s,Chief Executive Officer,NTT DATA UK12THE ABILITY TO RELATE THE ANALYTICS TO THE BUSINESS ISSUE IS FUNDAMENTAL.IT IS NOT JUST A TECHNICAL ISSUE IT IS A BUSINESS ISSUE.ANALYTICS IN FINANCE AND ACCOUNTANCY|EXECUTIVE SUMMARY13ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINA
86、NCE AND ACCOUNTANCY1.The opportunity for analytics in finance and accountancy1.1 Analytics the story so farThe history of analytics,as for much of the technological and business environment,is a story of continuous evolution.Analytics can be traced back to the 19th century,when Frederick Winslow Tay
87、lor first started undertaking time-management exercises.In the 1960s(Figure 1.1)the increasing use of computers allowed the application of statistical techniques to data on a consistent basis.Relational databases3 were invented by Edgar F.Codd in in the 1970s(Codd 1970)and became popular in the 1980
88、s with the increasing use of SQL to mine the data.Relational databases and SQL allowed data to be queried as it was generated and are still used today.The term business intelligence was first used in 1865 but it was popularised in 1989 by Gartner(Power 2007),who used it to describe decision making b
89、ased on searching,gathering and analysing data.The large-scale enterprise resource planning(ERP)software of the time included business intelligence(BI)modules(so-called BI 1.0)that allowed users to interrogate large-scale databases.Data mining started in the 1990s(BI 2.0),allowing the discovery of p
90、atterns of data in ways that had not previously been possible.Businesses started to predict future sales on the basis of historic trends.Big data(BI 3.0)was first defined by Roger Magoulas in 2005(Halvi and Moed 2012).He was defining large amounts of data that the computing resources of the time fou
91、nd hard to manipulate and analyse.In the same year,Hadoop was developed,allowing the manipulation of such data.The advent of Cloud-based computing has increased our ability to analyse the large volumes of data that we,as a society,now create.It provides us access to open-source tools that can be app
92、lied to vast sets of data that are stored in the Cloud.The time of analytics,or advanced analytics,has arrived.FIGURE 1.1:Analytics the story so far3 A database structured to recognise relations between stored items of information.1950s1960s1970s1980s1990s2000s2010sStructuredHighly complexComplexity
93、 of dataStatistical techniquesData miningBusiness intelligence or BI 1.0Business analytics or BI 2.0Big data analytics or BI 3.014ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCY1.2 What is the opportunity?Analytics helps extract valuable insights from
94、data and make better decisions.In this light,the role of the finance department is changing to help inform all business decisions,including those beyond the finance function.With analytics,the finance function no longer serves only as a data keeper or provider of financial reporting.The move is away
95、 from transactions alone to the support of decision making,using both financial and non-financial information.As bookkeeping and financial statement preparation require less human intervention,there is a shift towards spending more time on management and less time on accountancy.ACCA has produced a
96、series of reports,including this one,that focus on the evolution of the finance function(Figure 1.2).This changing role of the finance function in organisations to one of providing insights based on data sources is increasingly understood,as referenced by ACCA and PwC in their report Finance Insight
97、s Reimagined(ACCA/PwC 2020)but the way forward remains elusive.The design of the future finance team over the next 510 years is coming into focus(as considered in ACCA/PwCs report Finance:A Journey to the Future?(ACCA/PwC 2019).There is an opportunity for the CFO and finance team to assist organisat
98、ions in making better,faster,more appropriate and more soundly based decisions.The use of data that represents a broader view of organisational activities in achieving its purpose,such as the data relevant to the capitals as defined in the International Integrated Reporting Councils six capitals fra
99、mework4(ACCA/PwC 2020),provides an approach beyond financial reporting to allow the telling of data stories connecting the purpose of the business directly to decision making resulting from the use of analytics.These stories probably encourage more funding and investment than would be otherwise avai
100、lable.FIGURE 1.2:ACCAs finance function reports4 The six capitals were developed by the International Integrated Reporting Council(IIRC)in 2013 as a representation of the of the resources and relationships used and affected by an organisation(IIRC 2013).The six capitals are financial,manufactured,in
101、tellectual,human,social and responsible,and natural.Purpose and strategyOperation and insightData and analysisFinance:a Journey to the Future?Analytics in Finance and AccountingFinance Insights Reimagined15ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANC
102、YThe effective use of analytics is essential to the continued transformation of the finance function Figure 1.3).As finance functions increasingly adopt three roles,transactional efficiency,compliance and control and business insight,the use of analytics,particularly forward-looking analytics that u
103、ses both financial and non-financial information,is essential.No longer can finance functions ignore this opportunity:a failure to do so will increasingly see them marginalised in favour of those who can address the business needs.For this research,we not only interviewed CFOs and finance team membe
104、rs in a variety of industries but also spoke with policymakers who interact with social enterprises.Possibilities exist not only to fund projects to help economic growth but also to improve the lives of people,linking to organisational purpose and creating social benefits representing a many-fold re
105、turn on the capital.Thinking about decision making in these terms helps foster an evangelism not normally seen in the workplace.There was a consensus across all the in-depth interviews as that we carried out as to the future opportunity for analytics use by CFOs and finance teams,enabling unified de
106、cision making across entire enterprises.Building on the historical financial data focus,and the reliance on evidence-based decisions made using analytics,the future position is clear.The CFO and finance team will leverage the use of analytics for the improvement of decision making across the entire
107、enterprise,using not only financial information but also the deluge of non-financial information and new data sources available.These decisions include not only strategic ones but those at the operational level,down to the allocation of resources.Analytics provides a fulcrum for making decisions tha
108、t connect business strategy with operations.One interviewee,the CFO of a European organisation,made it clear that the use of analytics over the next five years will drive business strategy,allowing better decisions,planning and forecasting.Similarly,a North American CFO said that their CEO and leade
109、rship team already look to the CFO,not just for numbers but also for all decision making with an analytic aspect.The same CFO interfaces with all the different areas of the business,including strategy and sales teams,and assists with the work allocation of technical teams.But the ability to make dec
110、isions as fast as data becomes available(so called real time decision making)is still some way off and for most businesses getting close to this is good enough.FIGURE 1.3:Analytics and the transformed finance functionSource:adapted from ACCA/PwC 2020Business insightEffective ways of working with bus
111、inessCompliance and controlHow to balance sustainable costs without constraining the businessTransitional efficiencyImproving task performance in a timely and cost effective mannerAnalytics deriving insight to ensure that compliance and control are maintained Analytics providing the information to s
112、upport business insight and driven by transactional efficiencyPeopleTechnologyProcessDataAnalytics ensuring transactional efficiency while maintaining compliance and control16ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCYThe perspective from the CEO o
113、f a UK service provider reinforces how the context of a business determines the extent to which it needs access to real-time data.Yet another European CFO reminds us that the drive for analytics is about making the right business decisions.The data available is no longer just financial data but also
114、 non-financial data capable of supporting service delivery and infrastructure maintenance.The tendency to emphasise only the accuracy of the numbers when making decisions is giving way to the increasing use of non-financial information and looking beyond mere description of transactions.This non-fin
115、ancial data helps provide a broader understanding of impacts on an organisation,further extending the reach of the finance function across enterprises to support management decisions.Public sector leadership participants from the Australasian region recognise the opportunity to make better decisions
116、 but within the context of investing public money.The importance of decision making and supporting processes cannot be overstated and directly affects the bottom line of a company.A McKinsey study reveals the return on investment(ROI)of decisions improves by 6.9%when raising a companys decision-maki
117、ng process from bottom to the top quartile(Lovallo and Sibony 2010).To ensure superior decisions,the CFO and finance team need to infuse analytics into the decision-making processes across the organisation.During our research interviews,there was much discussion about the different types of analytic
118、s,tools and algorithms,but processes and frameworks are equally important.A well-proven framework for blending business and data understanding that is available to organisations of all sizes is CRISP-DM(Cross-industry Standard Process for Data Mining).The CRISP-DM framework breaks the process of dat
119、a mining into six phases,as discussed in the box below and as illustrated in Figure 1.4.Source:IBM SPSS Modeler CRISP-DM Guide(IBM nd)FIGURE 1.4:CRISP-DM process frameworkCRISP-DM frameworkHow do we get started on analytics projects?How do we organise an analytics project?What do we need to do to en
120、sure that the analytics project being undertaken is ethical?How do we know we have achieved the successful completion of an advanced analytics project?All these questions have one thing in common:they require an approach that will help organise the multiple steps that constitute analytics projects.N
121、otably,analytics projects vary in their duration and the skills they require.Thus,an approach,framework or methodology acts as an ordering mechanism for multi-step analytics projects of varying durations.Additionally,a common approach allows projects to be replicated by any team member or by those o
122、utside the delivery team.CRISP-DM is the most widely used analytics process standard(Chapman et al.2000).Thus,it is the closest thing we have to a standard model for implementing predictive and prescriptive analytics projects.All other contemporary methodologies appear to be variations of the initia
123、l CRISP-DM framework.The CRISP-DM framework can be used to build ethical guardrails into the stages of an analytics project(Cunningham 2020)ensuring biases in the data are known and safety measures implemented to ensure that the model is free of extraneous influences.1.3 The analytics projectHow do
124、we get started on analytics projects?How do we organise an analytics project?What do we need to do to ensure that the analytics project being undertaken is ethical?How do we know we have achieved the successful completion of an advanced analytics project?All these questions have one thing in common.
125、The answer to them,and more,is to have a framework to help organise analytics projects.Such a framework needs to cover activities beyond the development of the analytic models and should help avoid the too-hasty application of analytic techniques while dealing with missing data,or producing Data pre
126、parationDeploymentDATAModellingBusiness understandingData understandingEvaluation17ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCYan answer to a problem that has not been properly defined.What is needed is a framework that has stood the test of time fo
127、r nearly two decades.You may believe that with the pace of technological change this framework must surely be superseded.But CRISP-DM still survives;it is the standard model for predictive analytics and is being adopted for both the development and audit of machine-learning projects(Clark 2018).1.4
128、The biggest opportunitiesThe survey respondents(Figure 1.5)support the notion that for analytics in finance,faster and better decision making(51%)will be the key benefit and overarching opportunity to emerge over the next five years.Furthermore,the aim of supporting strategy development(supported by
129、 31%)emerges as significant in underpinning respondents desire to use analytics to inform strategic decision making.Better planning and forecasting(49%)as well as understanding and management of risks(40%)are of similar importance and require access to similar data sets.The support for decision maki
130、ng and risk taking by leveraging real-time analytics is apparent.It reflects the desire for real-time insights into performance(expressed by 38%)and improvement in data quality(38%).There is a need for clearer visualisation of trends and predictions(expressed by 37%)and recognition of the exciting n
131、ature of the predictive capability of data analytics and the opportunity for finance teams to move from the reporting of finance and accountancy measures to signposting the future.The testing of complete data sets is considered the least important of the opportunities offered by analytics(15%).1.5 F
132、our Vs of big dataKey executives and business owners,in organisations of all sizes,are making decisions using big data.Numerous businesses generate or accumulate a significant amount of non-financial unstructured information,including global positioning system(GPS)data,data from mobile phones,temper
133、atures from sensors,free-form text in questionnaires,website customer interactions,social media conversations and video from security cameras.The hallmark signature of this big data in our context is that it represents the non-financial information(NFI)generated between transactions.Since the Renais
134、sance,we have been diligently capturing and recording financial transactions using double entry bookkeeping and accounting systems,but the NFI is often not collected or used.Current business is represented through transactions(sales and operational indicators),but the future situation depends on hum
135、an empathy and relationships.Examples of related information include user-generated content about the use of branded products,ratings,reviews,one-to-one conversations,distress signals from customers and geolocation of customer activities.But how do these affect the financial situation?Consider for o
136、ne moment a hotelier.She pays attention when TripAdvisor rankings change as this affects revenue in the near future.But it is probably insufficient and inefficient for the business owner simply to await the financial statements prepared by the accountant on a regular basis before taking action on th
137、e discrepancy between revenues projected earlier and those that will reflect the change in demand for rooms.FIGURE 1.5:What do you see as the biggest opportunities for your team in using analytics over the next five years?Faster and better decision-makingBetter planning and forecasting processesBett
138、er understanding and management of risksReal-time insight into performanceImproved data qualityClearer visualisation of trends and predictionsSupporting strategy developmentSupporting business growth and scale-upGreater assurance on controlsEnd of month-end reporting/a move towards lean and agile ac
139、countingGreater collaboration across internal teams to solve business problemsMore space for ideas and innovationsMore time for stakeholder engagement and business partneringTesting complete data sets0%10%20%30%40%50%60%Total%of responses18ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR A
140、NALYTICS IN FINANCE AND ACCOUNTANCYTABLE 1.1:Business sources of big data non-financial informationBUSINESS PROCESSNFI SOURCES OF BIG DATA WITHIN ORGANISATIONSProperty,plant and equipment Online databases complementing historic valueMarketingSocial media,email,Google search,website analytics and eve
141、n health data from wristband devices and smartphonesAccounts receivableFull textual description(unstructured data)of goods or services Purchases and salesRadio frequency identification(RFID),GPS and Bluetooth beacon CashMobile payment,electronic credit and Apple Pay or Android via near-field communi
142、cations(NFC)Customer serviceEmail,social media,call centre records(CCR)Supply chain RFID,GPS,video(logistics centre)and temperatureInventory RFID,GPS and video(stocking warehouse)FIGURE 1.6:Four Vs of big data There are four recognised components of big data(Figure 1.6),as explained in the following
143、 paragraphs with reference to the experiences of the interviewees.One supermarket chain CFO uses big data built on customer purchases and weather data to forward-order ice cream;another CFO works with electricity grid data and smart meter data not only to help pinpoint faults in the network but also
144、 to service consumer demand proactively;still another builds business models on car exhaust-pipe data.One CFO considers behavioural data to be the Table 1.1 provides some suggestions about the types of non-financial information that could comprise the big data an organisation might use.Thinking abou
145、t the different NFI sources available to a business,a wine business example helps illustrate the importance of this non-traditional information.When Orley Ashenfelter,an economist,ran the numbers,he discovered wine quality=12.145/0.00117 x Winter Rainfall+0.0614 average growing season temp 0.00386 h
146、arvest rainfall(Marland 2014).Remarkably,this equation helped predict the wines of the century for 1989 and 1990.Predicting the price of an Australian Grange Hermitage uses similar techniques.The website Liquid Assets(Ashenfelter and Quandt 2020)provides a repository for both wine articles and data.
147、Interestingly,the data for the predictions in hotel revenue and price of wine is to be found outside these existing businesses and accounting information.In New Zealand,the ANZ Truckometer(Zollner 2020)connects the non-financial information of road traffic density,with the future state of the NZ eco
148、nomy.While counterintuitive,the ANZ light traffic indicator provides a six-month lead on GDP activity.According to Zollner,Traffic flows are a real time and real-world proxy for economic activity.But the lockdown owing to COVID-19 did sever the relationship between traffic and GDP.Increasingly,this
149、practice of using data held outside businesses to provide predictive capability in advance of actual events or publication of the financials reflects the democratisation of data(Handler 2013).This requires professionals and consumers alike to gain access to data freely to help make data-driven decis
150、ions.VARIETYVELOCITYVERACITYVOLUME19ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCYDNA of the business,and captures data from customer interactions in telecommunications,healthcare and insurance.Similarly,the insights manager at a government agency wor
151、ks with depersonalised behavioural data.The CFOs and executives interviewed across various business sizes and industries are making decisions using big data,especially that on VARIETY.Several interviewees from the UK and Asia already look at organisational website data as ripe for creating value.In
152、the COVID-19 pandemic,e-commerce has been continuously transforming retailing and food commerce from in-store purchases to stay-at-home orders.Thus,it is natural for CFOs and finance teams to leverage website data to create value.Whether the CFO and financial team can achieve competitive advantage o
153、ver other professionals will only be seen when they step forward and integrate the big data acquired into the financial performance measures they regularly provide to businesses.The website of any organisation is a natural starting point.A decrease in online customer visits compared with a previous
154、period is a potential indicator of a future drop in sales.Analysis of the Google analytics reporting on website views helps provide a view of sales potential well before a sales transaction entry is recorded by the business or lost to a competitor.The task at hand for the finance team is connecting
155、the non-financial information with financial data to create predictive capability(see Chapter 3,section 3.4).In the utility,manufacturing and retail sectors,CFOs handle data generated by smart meters,sensors on technical equipment or consumer interactions on social media.The velocity of this data ca
156、n be extremely high at times and results in the accumulation of large volumes of data when testing is taking place or special offers are being made to consumers.Some of this data may require monitoring in real time and thus require an infrastructure that can handle big data.The VELOCITY of data cons
157、titutes the second V of Figure 1.6.In the utility and manufacturing sectors,the value of the data is dependent on its veracity(its accuracy).But the sensor data is prone to equipment faults and noise.The cleaning of such data is an important step in extracting value.VERACITY constitutes the third V
158、of Figure 1.6.For all interviewees,the volume of data is large and this precludes using a single machine for storing,processing and analysing it.The examples of big data among those providing research inputs are Industry 4.0 applications:social media data,website clickstream data from e-commerce app
159、lications,behaviour data from call centres,machine sensor data from electricity utility systems,data on cargo ships transporting food around the globe,credit risk models and,of course,transactional data from banking and financial applications.VOLUME constitutes the fourth characteristic of big data
160、represented in Figure 1.6.One of the more unusual big data sets brought to attention by the Australasian public sector leadership interviewees is data combined with the lived experience stories of families.This data set builds on a government-published microdata research database about people and ho
161、useholds.The linked data combines interactions with government,including life events(eg education),income,benefits,migration,justice,and health.Interviews with audit professionals,including partners and managers with the Big Four from North America and Australasia,show that internal and external aud
162、iting practices are using big data.Historically,audits use small samples taken at a point in time,but auditors using big data and analytics now take up and review entire data populations.Taking a big data approach results in audit quality improvement as well as benefits from a client perspective,pro
163、viding insights on areas for improvement,especially for internal controls.In Chapter 3 we will explore the various types of analytics used to extract value from big data,such as descriptive,diagnostic,predictive or prescriptive analytics.THE VOLUME OF DATA IS LARGE AND THIS PRECLUDES USING A SINGLE
164、MACHINE FOR STORING,PROCESSING AND ANALYSING IT.20As an example,Clara,the new audit platform from KPMG,gives external audit an ability to generate rich actionable insights and visualisations,use predictive analytics,and leverage external and unstructured data.This intelligent platform helps with:1.b
165、ringing efficiencies and enabling auditors to derive to better informed judgements because of the insights from analysing more transactions.If used in risk assessment,it can also help with the identification of areas that are more susceptible to fraud2.creating value for the client from the audit in
166、vestment by providing insights on control breakdowns3.automation,as a number one priority for achieving efficiency(this is not about cost cutting).In fact,client executives want to use analytics in a similar manner to the auditor,with access to dashboards,benchmarks,control issues,actions taken and
167、exceptions.But the real value goes beyond that of the tools and comes from the actual insights from the auditor and use of professional judgement.In terms of the newest analytical procedures in the big data and analytics the undertaking of the audit is the process mining of event logs.This procedure
168、 allows extraction of transactions from processes.Business process issues are picked up through comparison matching with the original intent of the process.This technique provides the potential for identifying transactions not detected through traditional audit and that probably violate internal pro
169、cedures,bypassing approval or even violating segregation of duties.Through process mining anything overridden by the internal controls but appearing as an anomaly requires further investigation.The implication for skills and talent development in the new data-rich analytics audit platform environmen
170、t is that graduates with a STEM(science,technology,engineering and mathematics)background will need to be recruited,along with data specialists.For graduates,the level of training now covers a comprehensive understanding of internal controls.Another area meriting attention is testing,a high-risk cri
171、terion and as such the testing team cannot be automated.But historically,the audit has been the training ground for the next generation of CFOs.This might indeed change,as in future CFOs may need to be great data scientists and may well enter the profession as trained data specialists conversant wit
172、h advanced analytics.Much training is on the job,making use of LinkedIns on-demand training where appropriate.Some tools of choice for external audit are Alteryx(an analytics process automation tool),Microsoft Power BI and the SQL database.Data specialists are expected to have an understanding of Py
173、thon programming language.Owing to the nature and sensitivities of the work only approved technologies from a global listing in audit use with identified flows qualify for use in audit engagements.This ensures quality control over the tools and model development.Furthermore,the use of listed tools e
174、nsures that any challenge regarding the use of certain tools or models on an audit are defensible.Scepticism,judgement,and ethics remain crucial aspects of finance professionals development and training as they need to look beyond the records and seek to understand the management-control processes,t
175、o provide audit evidence.The costliest aspect is the collection of detailed evidence.Challenges exist in integrating the analytics platform with client ERP platforms,owing to the variations even within the same major vendor platforms of SAP,Oracle,or Microsoft.The minimum requirement is journal entr
176、y testing for the different ERP platforms.The situation and access to data is improving,with clients building data lakes with operational and external data.The use of big data and variety of non-financial information,including social media data,pictures from pdf files,and data from video,audio,GPS a
177、nd sensors provides the means for conducting advanced analytics,and combining internal with external data not only yields audit evidence but also enables risk assessment.The key lessons are to start with core processes such as revenue and follow the sales transaction order from order initiation to d
178、ispatch/provision and invoicing.Above all,the focus is on standard repeatable processes.The biggest opportunity emerging for the future is the use of more machine learning in audit,and technologies to help automation.Additionally,the audit in a big data analytics environment can undergo reconceptual
179、isation to a progressive or real-time audit concentrating on risk and management controls.Audit will move away from being a backward-looking regulatory annual or quarterly obligation and validation exercise ensuring that external transactions are recorded accurately.The judgement of the auditor is m
180、ore,not less,important in a big data analytics audit.Big data and auditThe organisational trend of using big data has emerged and a move to embracing advanced analytics(predictive and prescriptive)is now found in external audit engagements:the big data and analytics audit.Further,the auditors themse
181、lves witness at first hand their clients adoption of Cloud technologies,Industry 4.0 sensors and use of external data,including social media.ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCY21ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANA
182、LYTICS IN FINANCE AND ACCOUNTANCY1.6 Challenges in maximising analyticsWhat is obstructing organisations when making decisions today?The CFOs interviewed suggest that problems are caused by the omission of foundational elements,namely data governance and data quality.But the finance team remain resp
183、onsible as custodians of accurate data while the CFOs role is changing.The CEO of a UK service provider argued that the business analyst role is pivotal and requires the finance professional to be conversant with decision-making processes and,with support and empowerment,be able to document fully an
184、d subsequently automate them,which in turn impacts the quality of decision making.The survey respondents were also asked to select from a range of challenges for their teams in the use of analytics(Figure 1.7).For small and medium-sized enterprises(SMEs)(Figure 1.8),the picture is different from tha
185、t in larger enterprises.SMEs may receive part-time CFO services or be one of a number of clients of a portfolio CFO,according to the UK CEO of a software provider to the SME community.But the same Cloud-based analytic tools for large enterprises are available to SMEs.The availability of these tools
186、helps spawn opportunities for accountants in practice.FIGURE 1.7:What do you see as the biggest challenges in using analytics for your team?FIGURE 1.8:What do you see as the biggest challenges in using analytics for your team?SMEs compared with all respondentsLack of knowledge around existing techno
187、logies and solutionsData in poor formatsAccess to the right skills for analysing dataDifficultly acquiring data from other teams/poor inter-departmental sharingAnalysing unstructured dataEstablishing a data-driven cultureToo much data to deal withIntegrating data technologies into the existing techn
188、ology landscapeAccess to the relevant dataIdentifying which data to collect for analysis/which data to useUnclear ownership and governance structure in place over dataNo central data strategyLegal issues in relation to data accessExecuting business decisions from the data insights providedNo company
189、 leadership buy-in to data analyticsNot sure0%5%10%15%20%25%30%35%40%45%Total%of responsesLack of knowledge around existing technologies and solutionsData in poor formatsAccess to the right skills for analysing dataDifficultly acquiring data from other teams/poor inter-departmental sharingAnalysing
190、unstructured dataEstablishing a data-driven cultureToo much data to deal withIntegrating data technologies into the existing technology landscapeAccess to the relevant dataIdentifying which data to collect for analysis/which data to useUnclear ownership and governance structure in place over dataNo
191、central data strategyLegal issues in relation to data accessExecuting business decisions from the data insights providedNo company leadership buy-in to data analytics0%5%10%15%20%25%30%35%40%45%Total%of responses SMEs All responses22ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTIC
192、S IN FINANCE AND ACCOUNTANCYFor one Australasian provider of SME analytics software,data-driven decision making becomes possible,helping businesses uncouple decision making from gut feelings,a phenomenon linked to poor outcomes.As organisations attempt to increase their use of data,several areas hol
193、d companies back.The key challenges are a lack of knowledge of available technologies(40%),poor data formats(39%),gaining access to the right skills for data analysis(37%),analysis of unstructured data(32%)and establishing a data-driven culture(30%).1.7 Data strategyThe CEO of a UK software and serv
194、ice provider argued that the development of any analytics project must begin with a data strategy completely aligned with the organisations business strategy and business model.The data strategy will then meet the real business needs.Since a data strategy framework sits at the convergence of analysi
195、s,data control and management,this concept ideally suits a CFO who sees data as a valuable asset rather than a liability or cost centre.The data strategy provides for organising,governing,analysing and using the information assets of an organisation(DalleMule and Davenport 2017).Two distinct strateg
196、ic components exist and require a suitable balance between data defence and data offence(Figure 1.9).nThe data defence focus is on risk minimisation and ensuring regulatory compliance using analytics.nData offence directly supports business objectives of growth using activities to support decision m
197、aking through dashboards and generating customer insights from data models and analysis.Organisations interviewed for this research follow a mix of these data strategies.The financial service providers and retailers follow an offensive strategy,while an automotive parts supplier operating within veh
198、icle emission standards and a public sector organisation are both pursuing a more defensive data strategy.Companies focusing on security and governance tend to follow a defensive strategy while predictive analytics is the hallmark of an offensive strategy.CFOs want to develop data and insights with
199、an eye on the future but struggle with the availability of suitable data.But when focusing on a business problem,a finance-led analytics approach leads to unlocking significant value for the organisation.Most organisations providing input for this research work primarily with internal data and a sca
200、rcity of companies and case studies exist that exploit external data sources.Nonetheless,one North American CFO of a multi-billion-dollar business made it clear that the entire business model and revenue stream for that organisation builds on the analysis of external customer FIGURE 1.9:Data strateg
201、y data offence vs data defenceSource:Leandro Dallemule&Thomas Davenport May 2017 The Financial BrandDEFENCEOFFENCEThe Data-Strategy SpectrumA companys industry,competitive and regulatory environment,and overall strategy will inform its data strategy.HOSPITALS operate in highly regulated environments
202、 where data quality and protection are paramount.They emphasise defence over offence.BANKS are heavily regulated and require strong data defence.But they operate in dynamic markets and so typically devote equal attention to data offence.RETAILERS are less regulated,work with limited sensitive person
203、al data,and must react rapidly to competition and market changes.They typically emphasise offence.23ANALYTICS IN FINANCE AND ACCOUNTANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCYdata from external systems across the world.Another North American manager with one of the Big Four profe
204、ssional service firms mentioned the case study of a logging company delivering timber with a fleet of trucks to a national chain of building supplies outlets.Here,the GPS data from trucks blends with traffic incidents,predicted time duration on route and weather.Both cases provide inspiration for or
205、ganisations to make more use of external data.(Also,see the vintage wine and ANZ Truckometer examples for use of external data for value creation(box)(Chapter 1,section 1.5)and enhancing decision making.)This data from external sources,referred to as orthogonal data by Henke et al.(2016)because it i
206、s independent of the internal data,has the potential to change the business model for an organisation.Integrating external data from several sources and combining into an already running in-house BI system will help generate descriptive and predictive analytics,as well as prescriptive analytics (see
207、 Chapter 3).Figure 1.10 shows how respondents viewed the characteristics of data used by their organisations.Well over 50%of respondents said that their data was secure,trustable,up to date and easy to access,while fewer than 50%found their data to be easy to analyse,easy to use and clean.The least
208、amount of agreement occurred when they were asked whether the data was always used in the correct way(only 38%claimed it was).Accountancy and finance professionals typically want totally trusted data but there is a suspicion that we over-emphasise that at the expense of timely decision making.Would
209、we make a different decision using 100%accurate data than if it were only 80%accurate?So,we need to think about what we really need.Over one-third of the survey respondents suggested that they were comfortable with the way in which they used their data.If,as accountancy and finance professionals,we
210、are complacent then we will not recognise the opportunities that data and analytics give us.FIGURE 1.10:Thinking about your own organisation,to what extent do the following characteristics describe your data?0%20%40%60%80%100%n Dont know/not suren Strongly disagreen Disagreen Neutraln Agreen Strongl
211、y agreeSecureTrustableUp to dateEasy to accessLogically structuredEasy to analyseEasy to useCleanAlways used in the correct way24IF,AS ACCOUNTANCY AND FINANCE PROFESSIONALS,WE ARE COMPLACENT THEN WE WILL NOT RECOGNISE THE OPPORTUNITIES THAT DATA AND ANALYTICS GIVE US.ANALYTICS IN FINANCE AND ACCOUNT
212、ANCY|1.THE OPPORTUNITY FOR ANALYTICS IN FINANCE AND ACCOUNTANCY25ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAY2.The current state of play2.1 Technologies supporting data analyticsIn spite of the increasing impact of the Fourth Industrial Revolution,which has caused large and small
213、 companies in every industry to consider how to leverage new technologies to reimagine the business,Excel continues to remain an analytics tool of choice among CFOs and finance teams.Throughout the discussions,interviewees spoke about the need for systems capable of handling big data and presenting
214、visualisations.For the purposes of this research,we designate these systems as BI tools.Often the term analytics will be used as shorthand to mean a particular BI tool and not the technique.Figure 2.1 highlights the generation of data stories from using the BI tool.The tools listed are not an exhaus
215、tive list but highlight interesting,new and innovative open-source analytics.Since the data science community prefers open-source languages such as R and Python over commercial software,the tools from key vendors including Microsoft are designed for use with open-source tools and languages.The yello
216、w caption in Figure 2.1 highlights the functionality that BI tools such as Microsoft Power BI highlight on the futures roadmap.In the interviews,the most often-mentioned tool by far was Microsoft Power BI.Tableau and Alteryx received some mentions.These tools all incorporate advanced statistical or
217、machine learning techniques to gain insights from data.Alteryx is a data-blending tool enabling a business user,or more usually an analyst,to blend,cleanse and transform data from multiple sources and combine it into a single dataset for analysis.To analyse the output,an expert and a business user c
218、an review it in graphical form in Tableau.FIGURE 2.1:Finance data-stories generation from BI analysisAnalytics helps provide inputs for best possible data-storiesFinancialOpen dataOperationalMake decisions,discover and exploit market opportunities with actionable plansExcelGoogle SheetsPower BITable
219、auGoogle Data StudioPlotly*Apache Superset*Metabase*Redash*1.Ask questions in natural language2.Automatically generate data story3.Obtain context of operational data4.Auto discovery and catalogue data source5.Automatically model data6.Analyse data and recommendations7.Deliver insights and execute de
220、cision8.Drive workflow from data reportBig dataData reportingValue creationCompetitive advantageBusiness Intelligence via Data VisualisationBI tools*open source visualisation dashboards26ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYA new way of working with data is well under way;
221、the frequent mention of these tools and training by the interviewees suggests adoption across a wide range of industries and organisations.5The analytic tools available as an integral part of financial systems SAP and Oracle financials,although being used,did not appear to receive as much interest.M
222、any interviewees were using older versions of these platforms with less functionality in this area.This contrasts with the finance manager at a specialist funds provider who recognised the specialist nature of that industry and relied on the analytics capabilities available with the new specialised
223、systems.Small business users can access a specially designed range of analytic tools purpose-built for owners and advisers in small organisations.According to an Australasian SME consultant,the most important tools are:nFathom for financial reporting and analysis nSpotlight,to help visualise account
224、ing data and use this for powerful reporting and forecasting,and nFuturli,which makes predictions about a business after accessing all the accounting data available.To use such software,SMEs must use the online accounting packages of Xero or QuickBooks.Figure 2.2 shows that our respondents continued
225、 to see Excel as the work horse for analytics across their organisations(81%)with data visualisation tools a far second(47%)among technologies used.Robotic process automation(used by 17%),machine learning(13%),artificial intelligence(12%)and deep learning(6%)cluster together and the level of interes
226、t suggests that respondents are at the early stages of implementing these technologies.FIGURE 2.2:Which technologies are currently in use in your team to support data analytics activities/strategies/work with clients?5 ACCA and Chartered Accountants ANZ reviewed the use of technology in ACCA/Charter
227、ed Accountants ANZ 2019.ExcelData visualisationData aggregation toolsCloud computingRobotic process automationMachine learningArtificial intelligenceDeep learningOther(please specify)Dont know0%10%20%30%40%50%60%70%80%90%Total%of responses27This includes Microsoft Power BI,which is suitable for comp
228、lex modelling,and machine learning using Azure Cloud services and the Salesforce Tableau analytics platform.As SMEs experiment with technology and become data literate,SMPs are having to respond and even develop SME analytics solutions more suited to small businesses than the commercial enterprise a
229、nalytics tools.During COVID-19,SMPs have no longer had to convince members of the practice or clients of the benefit of analytics in generating information.The accelerated use of analytics by SMPs for SMEs during the pandemic has not only helped them gain an appreciation of analytics in finance but
230、also enabled access to government assistance wage subsidies and tax stimulus.Access to that assistance requires a demonstration of significant decline in income compared with the same period in the previous year.For SMEs to demonstrate eligibility requires a real understanding of the numbers and an
231、analysis by the SMP.One Australasian consultant and SMP argued that businesses might actually make more money by recognising revenue,sorting through staffing and making more use of their data than would otherwise be possible.This allows them to shake up the business and get rid of fat.Rather than pi
232、voting,business is vaulting to a business model change.Locating the assets and exploring how they can be reworked are the key issues.Exemplars of this shift are gin companies that are now making their own sanitiser brand,a change seen globally.Local cafs are selling picnics and focusing on their tak
233、e-away service.According to the Australian consultant interviewed,even the SMP accountancy professionals are changing.During COVID-19,a tax accountant who was used to hiding behind a computer has been providing online advisory services using Zoom videoconferencing and the Skype communication tool.Th
234、e same consultant and practitioner runs an online support group for SMEs,with each session running into two hours.This Australasian support group covers the globe with attenders from Malta,Ireland,the UK and US.To support SMEs and help them gain familiarity with analytics(particularly predictive ana
235、lytics)and off the shelf software,the Australasian consultant made several suggestions.1.Within the accountancy tools such as Xero,MYOB or QuickBooks ensure that you have a good chart of accounts with few clear expense lines and different jobs,and use a suitable tool for each project.2.The next step
236、 is extraction of the transactions and data from the accounts.DataDear(https:/ extract data into Excel and generates reports using pivot tables for a rapid analysis.3.With an understanding of the accountancy data,cash flow forecasting and scenario planning are achievable using Float(https:/ provides
237、 an automated two-year forecast and scenarios built on the current cash position.The scenarios might include the loss of a major client,late payments or a new hire.4.The use of financial prediction software is the core of Futrlis Predict.Automatically pulling all the financial data sitting in the Cl
238、oud accountancy system,Predict acts like a GPS,providing accurate cash flow forecasting and the future business position inclusive of(but not limited to)cash,sales,spend,profit and tax.The SMESMP perspective of the founder of a UK accounting,consulting and data analytics business has led this interv
239、iewee to follow a different trajectory when engaging clients.The first step of the methodology is to gain an understanding of the early stage business A small and medium-sized business and practice perspectiveThe most advanced of Cloud-based analytics tools available for large organisations are read
240、ily accessible for SMEs and small-to-medium-sized practices(SMPs).ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAY28ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYthrough the business strategy and by segmenting the customer markets to help inform and align the financ
241、ials,and reporting from the ground up,with the essence of the business.It involves understanding the key stakeholders in the business and the metrics and information that support decision making.This understanding allows the founders company to gain sufficient knowledge to run the finance function f
242、or a client.From an analytics perspective,the choice of tool such as Looker(Google),PowerBI(Microsoft),Klipfolio(from a Canadian software company)or Tableau(Salesforce)is secondary to understanding the sources of not only financial data but also the non-financial data and the best approach to ingest
243、ing the data.The sources include electronic point of sale systems,e-commerce websites,customer relationship management(CRM)and warehouse systems.Bringing all this together provides greater confidence in decision making for the business owners.It requires a hybrid of skills covering accountancy and f
244、inance,basic technology,and data skills.The development of such skills is achievable by giving data specialists and data scientists training in accountancy and management information.Challenges for the founder include the necessity of communicating to SMEs the power and value of analytics that they
245、may never have used before.The key person providing a mandate for analytics in one SME business in Asia is typically the CEO.The business owner or CEO ensures collaboration takes place for data sharing.Thus,the focus is not on financial information but on actionable insights.This is important owing
246、to the high cost,for an SME,of implementing the analytical tools.The cost is dependent on the requirement for bespoke visualisations aligning with the business strategy and carefully generated charts from key data sources.The end-user experience is built on alignment with the clients business strate
247、gy.The CEO of an Asia-based data technology applications provider sees analytics technology as helping analyse and cut through much more data than using only Excel and mental skills.A variety of software is available to generate analytics and this CEOs finance team is actively involved in the busine
248、ss,helping to understand client requirements and acting as a bridge between the customer and software development team.The finance team use predictive analytics internally to help understand customer behaviour and proactively manage delivery resources.Within the business itself,dashboards help the f
249、inance team present the company performance.A technical team works on coding analytics programs while finance understands the requirements.When the technical team struggles with storytelling,the finance teams collaboration helps to generate the right visualisation of the information.Importantly,the
250、finance team members can help the client to understand the business case and ROI.The finance team helps monitor the ROI on a continuous basis.The CEO runs an internal academy for training in relevant skills.This does not preclude attendance at public conferences to help close any gaps in knowledge o
251、f the tools and how to use them.The soft skills requiring development are those needed for selecting visualisation methods for different data sets or problems and using storytelling to support the chosen visualisation.These soft skills complement the existing finance and accountancy skills,so a tech
252、(analytics)aware finance professional needs them all.A small and medium-sized business and practice perspective292.2 Where is analytics being used?Predictive sales analytics,client profitability,product profitability and cash flow analytics are well established applications of analytics among the or
253、ganisations interviewed.A UK data specialist goes beyond using analytics to help predict areas of improvement,instead focusing analytics on finding improvements in the business model.Most notably,business-to-business models are facing immense pressure to understand customer requirements.This use cas
254、e arises from the need to bypass the traditional failing channels such as bricks and mortar retailing.Analytics are being applied beyond finance and marketing to embrace all the functional areas of an organisation.The rationale for using analytics has several elements:nextracting value from data nac
255、hieving competitive advantage nsupporting strategic and tactical goals nachieving better decision outcomes nachieving better organisational performance and nproducing knowledge(Holsapple et al.2014).The use of analytics for workforce planning,HR or people analytics has seen an acceleration during th
256、e COVID-19 pandemic.A North American manager with a Big Four professional services firm and ACCA network panel member is helping government with demographics planning and resourcing,moving the front-line workers to places with lower instances of COVID-19 out of harms way.Data analytics helps build r
257、eporting on take-up of federal government incentives.Focusing on people who have lost their jobs allows government executives and public sector decision makers to make refinements to policy and update the incentives in the longer term.Similar reporting to identify small businesses damaged by COVID-1
258、9 gives government the ability to plan incentives for businesses during the time of COVID-19.COVID-19 has changed ways of working,including more flexible arrangements,fully working from home,spending time with children during the day and making more time available by reducing or eliminating commutin
259、g.Such changes have resulted in the need for rapid implementation of remote working strategies and deployment of new technologies for business continuity,where needed.Analytics tools are playing a considerable role in helping to provide resources wherever staff may be,where previously only the one o
260、ffice had to be so resourced.Since the onset of COVID-19,and in some cases a reduction of work,analytics has helped to use resources efficiently.Previously,a simple pipeline tool and group discussion provided sufficient input for two-to-three-month resource planning.Today,a COVID-19 resourcing tool
261、helps a Big Four professional services firm with balancing demand for engagements with resource availability in different parts of a country or even across borders.The processing of small business government incentives for businesses coping with COVID-19 in North America needed 400 people in a week.
262、Previously,recruiting this group would have been an almost impossible task and recruitment required vetting of the people applying with a knowledge of business accountancy.The project met the objectives,requiring the completion of paperwork for each application to support the accounting information
263、available.ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYCASE STUDY:The challenges of master dataOne of our challenges is having good data to work with.This is a problem of discipline and this is one of the issues in that we have with master data.For years we have been exerting pres
264、sure on the top management to make sure that this problem is resolved.We have good infrastructure.We invest a lot of money.We train people.But when it comes to managing our master data this is one of our biggest issues.We know that we have a problem in for example with our sales and marketing depart
265、ments.They are late with the master data maintenance in the system or they provide the wrong master data.They make mistakes and they outsource the process.So,it becomes even more of a mess.Yet it becomes a stereotype when you automatically say this is the problem,and you know we live with this.You n
266、eed to explain that if we want to have good decision making this needs robust analytics and that requires good quality data otherwise your analyses become false.30ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYFIGURE 2.3:What do you think are the main reasons why your team is explor
267、ing or using data analytics?To drive business efficienciesTo improve planning,budgeting and forecastingTo improve risk managementTo test and improve controlsTo explore new revenue opportunitiesTo support new product or service developmentTo manage customer relationshipsTo detect fraudTo meet regulat
268、ory requirementsTo understand competitorsTo identify and manage talent in the organisationOther(please specify)0%10%20%30%40%50%60%70%80%Total%of responsesn Yesn No n Not sure 62%22%16%FIGURE 2.4:Are responsibilities clearly allocated when implementing data analytics in your team?As the CFO moves be
269、yond the finance function,it becomes essential to apply analytics to uncover value and go beyond achieving cost reduction and process efficiencies.This is less about transactions and more about deriving value from data to help drive the growth of organisations.The ability for analytics to transform
270、business processes is beginning to emerge.For example,take the collections process.The traditional approach is to prioritise accounts on the basis of the size of the account or days since invoicing.With analytics,an organisation can prioritise on the basis of factors that help pinpoint the likelihoo
271、d of payment within a given time frame.Using proactive email,a collection request can be made to the customer.So the use of analytics completely changes the process.Organisations are not just exploring or using analytics to focus on existing operations(Figure 2.3).According to our survey respondents
272、,the top two benefits of using analytics are improving efficiencies(67%)and improving planning,budgeting,and forecasting(also 67%).Beyond these reasons,improvements in risk management(48%)and testing as well as improving controls(45%)are both helping derive value from data.But the advantages go deep
273、er:improving revenue(37%)and supporting new product or service development(33%)detecting fraud(27%)and meeting regulatory requirements(26%).In summary,respondents are using data for far more than just gaining insights for managing customer relationships(30%).Almost the least-experienced benefits tha
274、t respondents derive from their data are meeting regulatory requirements(26%)and understanding competitors(17%).For the finance team,the least important reason for their use of analytics is the identification and management of talent(14%).2.3 Who is responsible?Most respondents(62%)to the survey cle
275、arly understand their responsibilities for implementing data analytics(Figure 2.4).Most often,the responsibility for implementing analytics across the organisation falls on the CFO(40%)with the CEO well behind(28%),as shown in Figure 2.5.Other roles do not have anywhere near the same responsibility
276、or authority from the leadership team.The appearance of the audit partner suggests that,notwithstanding the independence challenges,the increasing use of big data analytics in audits is attractive to businesses.Furthermore,the business will use data analysis in a similar manner to the audit,hence th
277、e relatively strong influence(15%)of the audit partner on implementing data analytics.31The less-than-expected influence of the chief analytics officer(CAO)(5%)on implementing data analytics reflects the nascent stage of such roles and their inheritance of previously implemented analytical systems.T
278、his reflects the many ways in which organisations establish analytic functions.Whatever the structure,the responsibilities for governance and integrity need to be clear.It should be noted that the audit partner scores only 15%as the survey responses include all respondents.When the responses to this
279、 question are broken down by respondents industries,those in professional services score 49%for the audit partner,those in professional services leadership 81%,while those in finance roles score only 1%.Figure 2.6 shows a further analysis of the respondents who identified themselves as working in sm
280、all and medium-sized enterprises(ie having fewer than 250 employees)in comparison with all respondents.This shows that the CEO in a smaller enterprise has a greater role than the CFO.This suggests that the finance team is not as advanced in this area but,with the increased availability of Cloud-base
281、d applications developed for this market,the opportunity is there.ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYFIGURE 2.5:Who is currently responsible for taking the decision to implement data analytics across your organisation?FIGURE 2.6:Who is currently responsible for taking th
282、e decision to implement data analytics across your organisation?SME respondents vs.all respondents0%5%10%15%20%25%30%35%40%45%Total%of responsesChief financial officer(CFO)or finance directorChief executive officer(CEO)Audit partnerChief information officer(CIO)or head of ITHead of business intellig
283、enceChief compliance officer(CCO)or head of internal auditHead of strategyHead of risk managementConsulting partnerChief data officer(CDO)Chief analytics officer(CAO)Head of salesChief marketing Officer(CMO)or directorOther,please specifyDont know0%5%10%15%20%25%30%35%40%45%Total%of responsesChief f
284、inancial officer(CFO)or finance directorChief executive officer(CEO)Audit partnerChief information officer(CIO)or head of ITHead of business intelligenceHead of strategyChief compliance officer(CCO)or head of internal auditHead of risk managementConsulting partnerChief data officer(CDO)Chief analyti
285、cs officer(CAO)Head of salesChief marketing Officer(CMO)or directorOther,please specifyDont know SMEs All responses32ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAY2.4 Responsibilities for implementing data analytics in the medium termOver the period up to 202325(Figure 2.7),the res
286、ponsibility for implementing analytics across the organisation will lie with the CFO(40%)and CEO(34%).This picture is a natural evolution from the current situation,in which the same leadership roles drive analytics(Figure 2.5).Surprisingly,the role of chief data officer(8%)was expected to have grea
287、ter responsibility than the chief marketing officer(CMO)(4%)in the future.The implementation involves not only the tools selection but the building and structuring of teams to enable insights from the data.The CFO role is pivotal in making data and analytics available across the organisation.2.5 Ski
288、lls needed to derive value from analyticsWhile analytics technology,process and data are essential ingredients for analytics projects,the need for analytics talent is a critical success factor.Both choosing a suitable method for an analytics project(eg CRISP-DM)and defining the right data and analys
289、is method are human tasks.CFOs and CEOs need to be flexible enough to re-engineer thinking and decisions to fit with the analysis and insights resulting from the business intelligence tools and visualisations.But analytical tools are lowering the barrier for the required level of statistical and dat
290、a skills and providing existing talent with opportunities to expand beyond traditional financial and accountancy work.Whatever the internal resources,generating insights will require recruiting external talent on occasions.Since this talent is uncommon and analysts with specific industry experience
291、can be hard to find,talent sourcing practices need revising.This is certainly the view across several of the organisations consulted.An Eastern European CFO wants to have a champion in each team.Such champions not only work on problems but are also responsible for helping the other team members to d
292、evelop their own analytic skills.For some,this builds on their existing experiences and knowledge of the organisation.Both ACCA and Chartered Accountants ANZ have mentoring programmes and following these processes builds on a proven track record of developing technical skills while maintaining overs
293、ight of soft skills relating to ethics and maintaining privacy.FIGURE 2.7:Who is most likely to be responsible for taking the decision to implement data analytics in the next three to five years across your organisation?0%5%10%15%20%25%30%35%40%45%Total%of responsesChief financial officer(CFO)or fin
294、ance directorChief executive officer(CEO)Audit partnerChief information officer(CIO)or head of ITHead of business intelligenceHead of strategyHead of risk managementChief compliance officer(CCO)or head of internal auditChief analytics officer(CAO)Chief data officer(CDO)Consulting partnerHead of sale
295、sChief marketing Officer(CMO)or directorOther,please specifyDont know33The skills required are a mix of softer skills and technical skills,as shown in summary in Figure 2.8 and discussed in the paragraphs below.The key ability is to be able to frame the problem and understand and interpret the resul
296、ts.Tools can help find patterns but always need interpretation and presentation.Core technical and softer skillsThe combination of technical skills and ethical skills is supported by additional skills collectively making up the ACCA professional quotients(Figure 2.9a as defined in Professional Accou
297、ntants the Future:Drivers of Change and Future Skills(ACCA 2016)or Chartered Accountants ANZs Capability Model(Chartered Accountants ANZ 2020)(Figure 2.9b).What is important for analytics is the use of the quotient when conducting the analysis and communicating the data story.Data and analytics skil
298、ls neededEven where BI Centres of Excellence have been implemented,interviewees want to upskill the finance team and bring analytic skills and use of the technology into the mainstream of the finance function.In some cases,CFOs argued that it is not unreasonable for team members to pick up some deep
299、er programming skills in Microsoft Power BI or the Python programming language.The incentive for individuals to learn stems from understanding the rarity of people qualified in accounting who are tinkering in their own time with Python and machine learning.Such professionals will not come walking th
300、rough the door.But the skills and knowledge must permeate the leadership.At the C-level,CFOs and CEOs understand what an analytics first mindset brings.One North American CFO interviewee gained deep knowledge of ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYFinance and accountancyD
301、ata literacyData extractionApplication knowledgeProblem solving and critical thinkingVisualisationStorytellingFIGURE 2.8:Skills needed to derive value from analyticsFIGURE 2.9a:ACCA Professional quotientsFIGURE 2.9b:Chartered Accountants ANZ Capability ModelSource:ACCA 2016Source:Chartered Accountan
302、ts ANZ 2020 These are the six non-technical capabilities considered essential for future employability of a professional accountant irrespective of their level of work,career stage,location or job role.The other non-technical capabilities while critical,will vary in importance according the job role
303、.34ANALYTICS IN FINANCE AND ACCOUNTANCY|2.THE CURRENT STATE OF PLAYanalytics training by working as a data scientist before moving on to finance.While rare,this is not unknown among companies born data-centric and offers a broadening of the membership base of finance and accountancy bodies.Whatever
304、the level of data literacy of CFOs,attending training or teaser workshops on contemporary technologies and tools is valuable for leaders and helps show the way to the existing workforce for participation in learning on the fly,as a UK CFO suggested.The leadership requirement is not for in-depth know
305、ledge but for an understanding of the types of business ideas suitable for certain types of big data processing techniques,and knowledge of relevant technologies.This is sufficient for enabling an ability to question proposed analytics business cases and ensure their integrity,and not being seen as
306、a soft touch for approving such projects.In practice,there remains a question as to the extent to which finance and accountancy professionals need to become data scientists and understand the data collection approaches and even data structures.According to at least one CFO from Europe,the imperative
307、 must be continual learning to recognise the context in which all these digital skills,the analytics,the data,their governance and quality fit together.This helps ensure that the finance professional will remain relevant in tomorrows business.The UK head of an accountancy practice and software vendo
308、r tackles the skills challenge by putting data engineers through accountancy modules and finance training.Since CFOs and the finance team are making decisions on analytics and implementations,the benefit of taking such a transdisciplinary approach allows better conversations between data specialists
309、 and members of the finance team.Taking this further for the finance team,analytics training needs to include how to access data sources,understand an application programming interface and extract data from,eg,sales into a finance system,so as to include forecasting and reporting.Much of this work o
310、f integrating systems on demand is made possible through low code,whereby the designer is not coding in traditional lines of computer code but instead draws on an understanding of the underpinning business processes.The low code environment converts the flow chart of the process and high-level abstr
311、actions of the data structures into an application.In fact,the Salesforce platform includes the low code Lightning platform.Finance team members conversant with spreadsheets benefit from this environment by being able to develop a program that reduces multiple spreadsheets into a single app.So,this
312、is not just a matter of acquiring technology skills,but is really about the finance team helping the business through the right data,right tools,and right skills to achieve the right partnership.For audit practice,the selection of tools needs to appeal to a large base of talent within the practice a
313、nd among clients.This helps lower the learning hurdle.An Australasian audit practice partner and manager helps constantly to improve the skills of employees,not only in the areas of reporting,control and analytics but more recently and explicitly in IT.Since the beginning of 2020,the audit practice
314、has been optimising and automating a lot of the analytical procedures using advanced analytics in Excel,SAP,and robotic process automation.The Australasian CEO of a software organisation servicing SMPs with SME customers operates a Microsoft Power BI boot camp to familiarise participants with BI and
315、 visualisation techniques.DAX(Data Analysis Expressions)is the formula language used throughout Power BI and forms a key part of the boot camp.Dashboard champions attending the course gain access to pre-built templates driven through drag and drop and pivot tables.Data champions focus on bringing ex
316、ternal data sources into the system and possess DAX skills.The skills shortage in analytics is further exacerbated by a lack of skills in spreadsheets available to organisations and finance teams.The situation is no different for SMEs or smaller accounting and advisory practices.The tools skills in
317、short supply include Microsoft Power Pivot and Power Query and their supporting languages M and DAX.The capabilities of both these tools are available in Microsoft Power BI.What should on-boarding for new talent include?A refresher for the latest spreadsheet versions and Microsoft Power BI is genera
318、lly provided along with a basic training in finance and accountancy fundamentals.Beyond this,most companies consulted for this report ensure that each employee has access to a considerable number of educational resources(eg L now LinkedIn Learning).This micro learning,which takes place on the job or
319、 while working in between projects,helps the employees stay abreast of data technology and techniques.Some workplaces use this approach to Integrate learning into the overall employee experience.Thus,learning while doing helps embed the relevant knowledge by connecting the analytics paradigm to work
320、 activities.35A new analytics model?A UK CEO envisages entire organisations moving away from their traditional structures and establishing a new way of working in an analytics business model.This represents an ability to visualise and analyse data in new ways.To achieve this,a playbook of ideas and
321、actions addressing data-centric work helps employees to establish a new way of working,leading not only to increased productivity but also to finding innovations from discoveries in data.This picture of the future of work helps address the impatient nature of the millennial workforce while promoting
322、 self-service analytics.Such a playbook well rewards the effort taken to produce it,especially for work on future business requirements that are data centric but may not be clearly understood and are ambiguous or lacking an in-depth briefing.For the CEO of an Asian data specialist,soft skills are an
323、 essential part of the skills mix.Establishing a futures academy in-house and training at a rate of 50 staff per month helps build the firms analytics capability.Attendance at public training sessions still takes place,ensuring that staff are taught the latest tools and knowledge.Two key skills esse
324、ntial for supporting analytics projects are visualisation techniques and being able to select the best visualisation method for different data sets or problems.Once the visualisation is available,storytelling skills are needed to create the right messaging and story for each client.Finance teams wit
325、h an understanding of analytics represent the ideal people to present the data story.Developing the skillsSeveral CFO interviewees from Europe,Asia and North America were pragmatic,realising that existing team members possess skills and knowledge of existing systems and processes.Combining the old w
326、ith the new skills avoids the case of getting caught up with something new and shiny.Thus,the future skills are a hybrid of those necessary to do todays work and newer skills,including analytics.A hybridisation of old and new skills develops from upskilling the existing workforce as well as by filli
327、ng gaps in analytic skills not available in-house but essential for the progression of analytics projects and thinking.Taking such an approach helps achieve competitive advantage while leveraging the existing talent within the organisation.A new breed of employee is grounded in a business domain but
328、 also has a breadth of analytical and personal skills.Analytics starts with alignment of the business and the identification of relevant internal and external data sources.Three key characteristics help distinguish the ideal analytically competent employee from others:nanalytical skills understandin
329、g data and its manipulation(including Excel pivot tables)nability to repurpose the business problem into an analytics workflow na curious mindset curiosity helps drive problem-solving using data exploration,visualisation,and predictive models.A data literacy programme helps drive insights from indiv
330、idual cases to wide application and enables people to be both confident about and willing to use data.No longer can organisations of any size drop analytics tools on users and hope for the best.Establishing a data culture that thrives on participation helps,but no data and analytics technology or pr
331、ocess can function if people do not support the data initiatives.Getting people confident in using data is critical for adopting the use of BI tools.The clear solution is seen in:The emergence of data literacy programs.Data literacy is the ability to identify,locate,interpret and evaluate informatio
332、n and then communicate key insights effectively(Australian Public Service Commission 2018).A Data Literacy for Everyone type of programme covers a broad range of important business skills,including:nthe language of data nunderstanding uncertainty(probabilities)and complexity,and nthe interpretation
333、of relationships in data and visual representations.The CFO and finance team are well positioned for coordinating a data literacy programme and have an opportunity to infuse the language of finance and operational data across the organisation.At the same time,a data literacy programme is essential to engaging the curiosity of individuals and thinking about what data is available in the business,wh