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A ROUGH ROAD TO DATAMATURITYBy Elias Baltassis, Anne-Douce Coulin, Antoine Gourévitch, Yassine Khendek, andLucas QuartaGSteady Progress Lifts MaturityLevelsAmong the eight industries included in oursurvey, the technology, media, and telecommunications (TMT) sector outpaced thefield in terms of data maturity, followed byfinancial institutions and retail and consumer companies. Geographically, NorthAmerica led the way in 2019 thanks to astrong showing by both the US and Canada. And although Europe leads Asia on aregional basis, China comes in second tothe US in terms of data capabilities at thenational level.Given the challenges that companies experienced in attempting to reach their 2016goals, it’s not surprising that they dialedback their ambitions somewhat in the 2019survey. But they still have substantial aspirations. On average, respondents expect tolift their capability index scores 25%, to 398by 2021, which would put them at thethreshold of the “state of the art” data maturity category.lobally, companies are makingprogress in their efforts to mature theirdata capabilities, but the transformationto data-driven organizations is provingarduous for many of them—and it’s nothappening as rapidly as they’d hoped.According to BCG’s Data Capability Maturity Survey, the companies we surveyed in2016 had hoped to raise their data maturityindex score by 53% by 2019. But they fellwell short of their ambitions, improvingtheir score by 19% according to this year’ssurvey. (For more on the survey and index,see the “Study Methodology” sidebar.)Although the companies we surveyed didnot achieve their very high ambitions forprogress, their 19% improvement from2016 to 2019 is noteworthy. In 2016, theaverage maturity index score was 268,which fell into what we define as the“developing” category on the data maturity spectrum. But by 2019 the averagemoved to 318, placing it in the “mainstream” category. Moreover, the percentage of companies scoring 300 to 500 points(qualifying them as mainstream or better)more than doubled from 30% to 62%. (SeeExhibit 1.)

STUDY METHODOLOGYAfter conducting our Data CapabilityMaturity Survey, we used the results tocreate a performance index. The indexestablishes benchmarks that helpcompanies more precisely assess thematurity of their data capabilities compared with that of their peers. Thisknowledge can help companies designbetter digital strategies to compete moreeffectively as data-driven organizations.The index is based on the seven datacapabilities in BCG’s Data CapabilitiesModel. (See the exhibit.)Across these seven capabilities, weweighted 19 dimensions and 55 criteriaand then created an aggregate scorefrom 100 to 500. That score placed eachcompany somewhere on the five-stagedata maturity spectrum: Lagging. Has made little to noprogress across the seven datacapabilities. Developing. Understands its datacapability challenges and has startedto work on them in earnest. Mainstream. Possesses average datacapabilities for its industry. State of the Art. Has several areas ofexcellence among the seven datacapabilities but may not managethem cohesively. Best Practice. Is advanced in all dimensions of data-capability buildingand manages them cohesively.The survey results included in this article are just a sampling of the data wecollected. The full data has been reserved for internal BCG analysis andclient cases.BCG’s Comprehensive Data Capabilities ModelExamples of key issues to addressChange managementVisionUsecasesAnalyticssetupData governanceData infrastructureData ecosystem Why are we interested in data? What are we trying to achieve? Do we want to improve our current practices or undertake aradical transformation? Which are the main macro use cases? How much value do weexpect from each? Do we have the right end-to-end ownership and accountabilityfor our use cases? Do we have the right talent pool? Have we implemented theright analytics functions? Do we have a thorough analytics process, leading to concreteresults? Do we have a data management organization in place? Have we launched basic data hygiene actions (e.g., definingquality and key quality indicators)? Is our infrastructure suitable to our vision and future use cases(e.g., technologies and operating model)? Is it end to end—from modeling to industrialization? Do we understand and leverage the data ecosystem around us? How should we manage the ecosystem? What role should weplay in it? How do we handle change management from legacy to adata-driven company? Do we leverage new ways of working?Source: BCG.Boston Consulting Group A Rough Road to Data Maturity 2

Exhibit 1 Distribution of Companies by Data Maturity2016 survey (%)LaggingDevelopingMainstream(100 points)(200 points)(300 points)11State of the artBest practice(400 points)5927(500 points)3Average maturityindex score (268)2019 survey (%)LaggingDevelopingMainstream(100 points)(200 points)(300 points)4State of the artBest practice(400 points)3451(500 points)11Average maturityindex score (318)Sources: BCG Data Capability Maturity (DACAMA) Surveys, 2016 and 2019; BCG analysis.All Data Capabilities Improvedin the New SurveyAs part of our analysis to better understand strengths and weaknesses, we examined how companies have performedacross seven data capabilities: vision, usecases, analytics setup, data governance,data infrastructure, data ecosystems, andchange management. Back in 2016, thestrongest data capability among thosesurveyed was vision, which helped to seta solid foundation for data strategy. Yet,somewhat surprisingly given this strength,companies also said they struggled to applythat vision and develop concrete use cases.Data infrastructure and data ecosystemswere even weaker, probably because theyrequire more enterprisewide transformation than developing a vision and identifying use cases.Since 2016, all capabilities have improved.(See Exhibit 2.) But the 2019 results revealsome surprises. For example, sdata infrastructure and data ecosystems improveddramatically—although they grew from acomparatively low base, which made thosebig gains easier to achieve. Driving thegrowth in these capabilities was the vastimprovement in open-source technologiesand the increasing number of AnalyticsBoston Consulting Group A Rough Road to Data Maturity as-a-Service (AaaS) and Platform-as-aService (PaaS) solutions.Less obvious in these results, but very telling, is the relative underperformance ofanalytics setup (in other words, creatingvalue and having the right analytics processes and people in place), which hasgone from the second most developed capability to one of the least mature. Thatcame despite very high ambitions. On average, companies had hoped to raise the maturity score for analytics setup from 293 to435—a dramatic 48% increase. Instead, itrose just 7% to 314.This trend was consistent across regions,industries, and company size, suggestingthat most companies are struggling withorganizational issues as they try to deliveron their business strategy (such as aligningvarious stakeholders). Interestingly, theyare having trouble with their analytics setup despite strong support from top management.In the face of these challenges, companieshave pulled backed on their 2021 ambitions for analytics setup and are aiming fora maturity score of 397. Although that’s farbelow the ambition they set in 2016, it3

Exhibit 2 Average Data Maturity Index Score by Data 259251213VisionUsecases2016 data maturity indexAnalyticssetupDatagovernance2019 data maturity ent1Growth (%)Sources: BCG Data Capability Maturity (DACAMA) Surveys, 2016 and 2019; BCG analysis.Note: The maximum number of maturity index points is 500, indicating best practice.1Change management is a new dimension introduced in BCG’s latest data capabilities framework; hence, it was not evaluated in 2016.would still amount to a 26% increase overtheir actual 2019 score.In fact, the companies in the survey recalibrated their 2021 ambitions across theboard. In all cases—except data ecosystems—their 2021 maturity score targets arebelow their 2019 targets.We found that, despite the many challenges that these organizations face, state-ofthe-art companies tend to mature all sevendata capabilities consistently and cohesively, thus maximizing the value of their data.For example, the analytics setup and dataecosystems of these companies are bothmore than 40% more mature than the restof the market. This creates significant competitive advantages.TMT Leads Industries in DataMaturityWe also took a close look at the maturityof data capabilities across industries. (SeeExhibit 3.) All industries have improvedtheir data maturity from 2016 to 2019 butat varying rates. TMT continues to lead indata maturity and improved its index scoreby 15%, but energy and industrial goodsdemonstrated the highest upswing, possibly driven by the growth of Internet ofBoston Consulting Group A Rough Road to Data Maturity Things (IoT) data in those sectors. Unlikethe 2016 survey, which found that midsizecompanies had more mature data capabilities than small and large companies, the2019 survey shows that company sizemakes virtually no difference in data capability maturity.It’s no coincidence that leading industriesfocus on improving each of the seven datacapabilities. Indeed, TMT and financial institutions, the first and second most mature industries, scored above average in allseven capabilities. Meanwhile, industrialgoods, transport and logistics, health care,energy, and the public sector all scoredbelow average for most or all the components.This tells us that a company in any of theindustries in our survey that is lookingto advance needs a high level of maturityacross all capabilities. That finding is inline with what we learned about individual state-of-the-art companies. Being verygood at just a few capabilities is notenough. The silver lining, particularly forenergy and the public sector, is that theirindustries have so few advanced playersthat investing in these capabilities couldgive them a pronounced competitiveadvantage.4