1、The data-powered enterprise Why organizations must strengthen their data mastery $ $ $ $ Introduction The already-rich seam of global data shows no sign of letting up volumes of digital data are expected to reach 40 ZB this year, up from 1.8 ZB in 2011.1 To put this into some sort of perspective thi

2、s is 40 trillion gigabytes, equivalent not just to every grain of sand on all the worlds beaches, but 57 times that amount. However, the sheer volume of data in our hands today in no way translates into equivalent levels of actionable and credible insights. This is a problem, as data has never been

3、more important. The uncertainty unleashed by the ongoing COVID-19 crisis has heightened the need for forward-looking, real- time insights and scenario modelling, such as pandemic modeling. Actionable data is the most critical factor in digital transformation. But aside from a few high-performers wha

4、t we call the Data Masters our research shows that data capability is patchy among many organizations today. So, what is it that separates those leading firms that do have a sophisticated capability? What are the unique attributes of their business and technical teams and how can others emulate them

5、 to become data-powered enterprises? To answer these questions, we surveyed 500 technology executives and 500 business executives from a range of industries, and conducted detailed interviews with more than 15 executives. We also looked at the relationship between data mastery and the financial perf

6、ormance of organizations. Based on our research, we look at the following key areas in this report: 1. Where are organizations today in their data journey? 2. What gaps exist between data executives perceptions and the expectations of business executives on the data mastery of their organizations? 3

7、. Who are the data masters? 4. What are the benefits they achieve? 5. How can we build a data-powered enterprise and what can we learn from the masters in this field? 2The data-powered enterprise Organizations have made headway in data-driven decision making and actioning, but the journey is far fro

8、m over: Today, half of organizations say their decision making is completely data-driven, a significant increase on those that said they promoted data-driven decision making in 2018. Decision-making approach in organizations, however, is majorly reactive looking at what happened in the past or why i

9、t happened. Further, fewer than half (43%) are able to monetize their data and insights through products and services. Only a minority (39%) are able to turn data-driven insights into a sustained competitive advantage. Business executives do not trust their data Major gaps exist between the trust th

10、at business executives have in the data they receive and how technical executives perceive trust levels. Only 20% of business executives trust the data, but 62% of technical executives believe their business users do trust it. Poor data quality is a major contributor to this lack of trust: just 27%

11、of business executives say they are happy with data quality (but, again, there is a mismatch 54% of technical executives think that their business users are happy with data quality). Trusted data is critical for organizational agility and an organizations ability to monetize data. While 56% of techn

12、ical executives believe their data/ analytics strategy is aligned with the business strategy, only 38% of business users share that view. Similar gaps between technology executives and business executives also exist in areas such as data collection, visibility, and data access policies. Data masters

13、 enjoy significant financial benefits compared to the rest of the sample When we assess organizations against critical dimensions of data mastery, only about one in six organizations can be categorized as “data-powered” (we call these the “data masters”). These data masters significantly outperform

14、the rest of the cohorts on financial parameters such as revenue generation and profitability. Looking at average performance for FY 201920, data masters realized a significant performance advantage compared to the rest: 70% higher Revenue per Employee 245% higher Fixed Asset Turnover 22% higher prof

15、itability. Data masters enjoy a performance advantage of between 30% and 90% in various metrics across customer engagement, top-line benefits, operational efficiency, and cost savings. How can an organization become a data master? Organizations should focus on three priorities: Data activation: this

16、 refers to the use of data in end-to- end business processes to secure business outcomes i.e., transforming data and insights into actions. To achieve this, organizations need to: Align their data and analytics strategy with the business strategy Build business users trust in data Establish an AI an

17、d analytics CoE to assist business teams Foster a data-powered culture by strengthening “data citizenship.” Data enablers: these are the key capabilities necessary for building data-powered enterprises and include infrastructure, governance, and operations. Organizations need to: Strengthen data col

18、lection processes and improve data quality Invest in data landscape modernization to get agility in data activation Operationalize data and analytics through DataOps and MLOps Adapt their data governance as data mastery evolves. Data advantage: this is about creating a competitive advantage, particu

19、larly by leveraging external data, including data from hyperscalers. To realize this, organizations need to be able to leverage external data to enhance their insights. Executive summary key takeaways 3 Defining the “data- powered enterprise” “Data” is the digital representation of an organizations

20、past and present, encompassing its processes and interactions with customers, ecosystem, and market.i We define a data-powered enterprise as an organization that can create, process and leverage data proactively to fulfill its corporate purpose, achieve its business objectives, and drive innovation.

21、 A data-powered enterprise is able to: Figure 1 iData yet the approach to using data is still reactive Given the rapid pace of change in technology, our hyper- competitive business environment, and increasing customer expectations, organizations today need to adapt and reinvent quickly to deal with

22、and even thrive on uncertainty. In this unforgiving environment, harnessing and applying data and analytics is becoming a prerequisite for success and innovation. In our 2018 research, “Understanding digital mastery today”2 we found this capability to be rare only 38% of organizations based their de

23、cisions on data rather than on intuition or observation. Organizations have since made some progress on this decision-making aspect. In 2020, 50% of organizations put data at the heart of decision making (see Figure 2). Beatrice Sablone, CDO of Arbetsfrmedlingen, a Swedish public employment service,

24、 says, “We want to be much more data driven in our decision making. We want to make more accurate predictions and analysis as an input to political actions that would best benefit the public employment sector. Our clients are our people basically citizens looking for jobs and we want to make sure th

25、at we help them in the best way recommending education or jobs or anything that get them out of unemployment as fast as possible. These are our business objectives.” Organizations are accelerating data-driven decision making Figure 2 Note: 38% of organizations agreed to the statement “We actively pr

26、omote data-driven decision making” in 2018; 50% of organizations agreed to the statement, “Decision making in our organization is completely data-driven” in 2020. Source: Capgemini Research Institute, Digital Mastery Survey; AprilMay 2018, N=1,338 respondents, 757 organizations; Capgemini Research I

27、nstitute, Data-powered enterprises survey, August 2020, N=1,004 organizations. Decision making in our organization is data-driven 38% 20182020 50% We want to be much more data driven in our decision making. We want to make more accurate predictions and analysis as an input to political actions that

28、would best benefit the public employment sector.” Beatrice Sablone CDO of Arbetsfrmedlingen, a Swedish public employment service 5 This data-driven capability has never been more important than today, where people need forward-looking insight to steer through the uncharted waters of a pandemic envir

29、onment. During this crisis, a number of organizations have adopted innovative ways to make the most of external and internal data: Dell Technologies has created a real-time dashboard to help its executives make sense of the multitude of data about the COVID-19 crisis and safely guide employees retur

30、n to the workplace. John Scimone, senior vice- president and chief security officer at Dell Technologies, says, “The dashboard enables our joint team, with representation from HR, sales, government affairs, security and every part of the company, to look at the same data using the same tool, backed

31、by hard data science and medical intelligence. This ensures our decisions are consistent, data- driven, and informed.” 3 A UK-based hospital trust (University Hospitals of Morecambe Bay Trust) has developed an analytical command center that enables its emergency department to see, at a glance, the i

32、nformation they need to make data-informed decisions. This ranges from the number of ambulances en route to bed capacity.4 Indonesias largest bank Bank Mandiri has built a big- data platform to track transactions and monitor the health of its workforce. During the pandemic, faster access to data and

33、 data visualization tools has allowed the banks management and other stakeholders to be armed with timely information to take informed decisions.5 However, while progress has been made on leveraging data for decision making, larger part of the decision-making in organizations still remains reactive

34、that looks at what happened in the past (descriptive) or why it happened (diagnostic). In contrast: Only 23% of the time, organizations use predictive approaches (what might happen in the future) Only 18% of the time, they use prescriptive approaches (providing recommendations to improve outcomes) A

35、nd just 8% of the time, organizations use an autonomous or self-optimizing approach (systems and processes that help business users make decisions with the objective of achieving a pre-established goal) Majority of the decision-making in an organization remains reactive Figure 3 Note: Respondents an

36、swered to the question, “Please indicate the proportion of each of the given decision-making approaches in your organization”. Reactive decision-making includes descriptive and diagnostic approaches; while proactive decision- making includes predictive, prescriptive and autonomous/self-optimizing ap

37、proaches. Source: Capgemini Research Institute, Data-powered enterprises survey, August 2020, N=262 technology executives who agreed to the statement “decision making in our organization is completely data-driven.” 18% 8% Autonomous/- Self-optimizing Reactive decision-making approaches Proactive dec

38、ision-making approaches Descriptive Diagnostic Predictive Prescriptive 26% 25% 23% In 2020, 50% of organizations put data at the heart of decision making. 50% 6The data-powered enterprise Data-driven decision making: The sector and geographic view As Figure 4 shows, financial services and telecom se

39、ctors lead in using a data-driven approach towards decision making. Banking, insurance, and telecom sectors lead in data-driven decision making Figure 4 50% Overall Banking Insurance Telecom Life Sciences and Healthcare Automotive Energy and Utilities Public services Consumer Products Mfg Retail Ind

40、ustrial Manufacturing 65% 55% 54% 53% 51% 48% 47% 44% 43%43% Decision making in our organization is completely data-driven Source: Capgemini Research Institute, Data-powered enterprises survey, August 2020, N=1,004 organizations. 7 US, Germany and UK top the charts in data-driven decision making Fig

41、ure 5 Source: Capgemini Research Institute, Data-powered enterprises survey, August 2020, N=1,004 organizations. 50% Overall United States Germany United Kingdom Australia Sweden France Singapore China Netherlands India Spain Italy 77% 69%69% 61% 48% 46% 44% 41% 40% 34% 30% 26% Decision making in ou

42、r organization is completely data-driven In terms of countries, the US, Germany, and the UK lead the charts, while India, Spain, and Italy trail behind (see Figure 5). By functions, finance and accounting (67%), risk and compliance (54%), and IT/digital (52%) lead in taking a data- driven approach t

43、owards decision making, while functions such as HR (38%) and production/manufacturing (40%) lag behind. 8The data-powered enterprise Few have the ability to monetize data or quantify its value Business and technology executives are almost unanimous in believing that data is one of their most critica

44、l strategic assets. Sixty-two percent of the technology and business executives say that data is as an enterprise asset. If data is an asset, it is natural to ask, “how much that data is worth?” and “how can it be monetized?” However, our research reveals that many organizations are not finding answ

45、ers to those questions. As Figure 6 shows: Only 22% of organizations are able to quantify the value of data in their accounting processes. While it may be difficult to do, it is still crucial. The overall approach is about seeing value in terms of how data is used only when data is applied to a spec

46、ific use case can organizations determine its value. Christina Ho, former US deputy secretary of commerce, has said, “I often refer to a simple formula that data plus use equals value.” 6 The revenue generated by using the data or the costs that are saved determine its specific value. For instance,

47、Siemens Mobility understands that scheduled maintenance is cheaper than dealing with an unforeseen breakdown. The company uses an analytical model to predict maintenance needs, which eliminates costly, unscheduled downtime. That cost of unscheduled downtime becomes the starting point for the price (

48、and value) of the insights service.7 Only 43% are able to monetize data through their products or services (externally or internally). In terms of data monetization, banking and automotive sectors lead: For instance, banks are tying up with retail to monetize their huge data troves. Retail banking c

49、ustomers of Lloyds and Santander can get special offers from a range of retailers. This facility was a results of the two banks joining a digital loyalty scheme run by US-based data advertising firm Cardlytics. The scheme uses spending data to give customers targeted discounts at shops they frequently visit. The banks get a percentage of the fee charged by Cardlytics for running the campaign and Cardlytics gets insights on consumer behavior, which help the retailers to tailor and fund the offers and discounts.8