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AI for DataData Capital Management @ Scale with AIExecutive Summary
Data has become a crucial component forhelping companies grow and reinvent theirbusinesses. Organizations now find themselvesin a new position where they must use data andAI to responsibly fuel their innovation, businessmodels, and partnerships.As many foundational data capabilities are typically human-led andexpensive to scale, AI can be applied to tasks to increase automation andprecision. We are helping our clients to be more data-driven and datanative by aligning their data and business strategies.AI for Data: Data Capital Management @ Scale with AI2
Background:Data as a new form of CapitalData as an asset is not a new idea, but more and more organizations arerecognizing how critical data is to their present and future success. Many arestarting to embrace the idea that data should be treated as another form ofcapital, just like human capital, financial capital, or intellectual capital.Optimizing data capital is essential forbusinesses to survive and thrive in thedigital age and reinvent their businesses tobe more data-driven enterprises. It forcesCEOs to be more strategic about acquiring,growing, refining, safeguarding, anddeploying their data as they would be forother forms of enterprise capital.Data must be high-quality, trusted, easy-touse, and secure to maximize its use as anasset and minimize its potential liability. Datacapital management requires strength in thefollowing areas:Data Supply ChainEnsuring you have the right data repositories anddata pipelines to meet business needs. Raw andcurated data is available for data scientists, whileshaped data is available for business consumersand operational systems.Data ManagementSustaining high-quality, trusted data at scale.Requires excellence in data quality management,master data management, and data lineagetracking.Data GovernanceRequires competency in managing businessmetadata (e.g. data definitions) and the designof cross-functional models of collaborationaround data.AI for Data: Data Capital Management @ Scale with AI3
The need to scale Data Capital ManagementThe way companies have traditionally operated their data organizations data ishuman-led and not easily scalable. This is where artificial intelligence (AI) comes intoplay. AI increases scale in data capital management—lowering costs while improvingquality overall investments in data and AI.These foundational data capabilities are traditionallyhuman-led and expensive to scaleData Stewards Catalog data & definitionsData EngineersAnalysts & SMEs Define business terms Configure tools to supportmovement of data Determine business relationshipsbetween terms (data models) Write code to implementbusiness rules (e.g.,transformation, quality) Define business rules fordata transformation Configure purpose-builtdata repositoriesQuality Experts Identify & maintain data lineage Monitor & respond to dataprocessing issues withData Engineers Participate in data remediationefforts Monitor & respond to dataquality issues with BusinessSMEs & Data Stewards Assist in creation & maintenanceof data assets Define Data Quality rules& thresholdsAI for Data: Data Capital Management @ Scale with AI4
Scaling Data Capital Management with AIAI is being used throughout all three pillars of Data Capital Management:Data Supply ChainData ManagementData GovernanceUnstructured Data InterpretationSelf-Healing DataAuto-labeling Data FingerprintingData AcquisitionImprove the business value and accuracyof data science models by analyzing text,images, voice, and other unstructureddata types.Automatic Data LinkingData ArchitectureLinking of data elements and assetsautomatically, accelerating Data Vault2.0 and other leading data architecturalpatterns.AI for Data: Data Capital Management @ Scale with AIData Quality ManagementSense data quality issues and recommendchanges based on pattern recognition, reducecost of data operations and improve dataquality.Autonomous DatabaseDatabase ManagementNext-gen database technology leveraging AIto automate manual, administrative tasks, andreduce time and cost.Metadata ManagementAutomatically tag data sets and dataelements with relevant business metadatato provide a richer context.Responsible Data & AIData Ethics & ComplianceIdentifies bias in the data underlyingmachine learning models to preventunbalanced representation. Increaseresponsibility in AI and data, mitigatereputational issues.5
Guiding your AI for Data journeyAI increases scale in data capitalmanagement—lowering costs whileimproving quality. Reducing time of data buildReducing cost of data build and operationsIncreasing quality of dataThe AI for Data journeyInitial use case, oftendriven by businessurgency for a timeconsuming capability.Accelerate your intelligent data journeyScale up governmentand managementwith automationand precision.Operationalize andmature automateddata quality andmapping capability.AI can transform your data capital management. We tendto see organizations follow a journey of AI for Data similarto what is illustrated below.Create learningsystems that optimizedata workloadsand quality.6
Case studyThe Challenge:A global entertainment company sought to reduceduplicate customer records due to impact on campaigncosts and precision.The Solution:We helped the client build ML & NLP models to checkdata for consistency and flag duplicates/issues forremediation.The Outcome:The client garnered improved customer data—loweringits marketing costs and enhancing overall experience.AI for Data: Data Capital Management @ Scale with AI7
How pre-built AI for Data solutionscan accelerate your journeyCompanies don’t need tobuild these capabilitiesthemselves. Accenture hasits own AI for Data Suite forsupporting and acceleratingdata engineering quality,master data management,metadata tagging, andgovernance acrossthe stack. And, leadingthird-party vendors areincorporating AI into theircommercial products—including Informatica, Tamr,Alation, and Collibra.AI for Data Products and AcceleratorsData Supply ChainData Quality ManagementMaster Data ManagementMetadata ManagementData GovernanceAI for Data: Data Capital Management @ Scale with AI8
How Accenture can helpAccenture guides our clients to realize their full potential byintegrating data capital management, AI, and automationon cloud through transformative technologies, adaptiveData is a new form of strategic capital. The time is now to introduceintelligent machine technology—using AI to accelerate the speed,decrease the cost, and create systems that optimize dataworkloads and quality.AuthorsShail JainGlobal Data & AI Lead,TechnologyAI for Data: Data Capital Management @ Scale with AIPrateek Peres da SilvaGlobal Growth & Strategy,Data & AI9
About AccentureAbout Accenture ResearchAccenture is a leading global professional services company, providinga broad range of services and solutions in strategy, consulting, digital,technology and operations. Combining unmatched experience andspecialized skills across more than 40 industries and all businessfunctions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to helpclients improve their performance and create sustainable value for theirstakeholders. With 505,000 people serving clients in more than 120countries, Accenture drives innovation to improve the way the worldworks and lives.Accenture Research shapes trends and creates data driveninsights about the most pressing issues global organizations face.Combining the power of innovative research techniques with adeep understanding of our clients’ industries, our team of 300researchers and analysts spans 20 countries and publishes hundredsof reports, articles and points of view every year. Our thoughtprovoking research—supported by proprietary data and partnershipswith leading organizations, such as MIT and Harvard—guides ourinnovations and allows us to transform theories and fresh ideas intoreal-world solutions for our clients.Visit us at www.accenture.comFor more information, visit www.accenture.com/researchCopyright 2021 Accenture.All rights reserved. Accenture and its logo are registered trademarks of Accenture.
master data management, metadata tagging, and governance across the stack. And, leading third-party vendors are incorporating AI into their commercial products— including Informatica, Tamr, Alation, and Collibra. AI for Data Products and Accelerators Data Supply Chain Data Quality Management Master Data Management Metadata Management Data .
