Data Value Chain: Enterprise Data Management (EDM) Framework (Lego Version)

Data driven organizations are innovating faster than ever. Data Science program maturity and processes enablement is one of the reasons why we see solutions like asset allocation, campaign management, agent effectiveness, customer churn, price forecasting and predictive equipment maintenance as some recent go to market “data products or data services”.

In this column, I will share my passion for Legos and enterprise data management, combining both to portray dependencies and maturity needed for “accelerated data driven” innovation in any organization.

We will review a sample “enterprise data management” Legos block framework. This will allow us to visualize the journey of a data driven organization towards sustainable competitive advantage. Data as an Asset (DaaA) allows for growth, productivity, profitability, market differentiation, and customer experience as some of the key business outcomes.

The recommended Lego blocks on enterprise data management visual are stacked 7 blocks high beginning with Organization Mission, Data Mission, Data Foundation, Data Enablement, Data Decisions, Data ROI and ending with Data as an Asset at the top.

The goal for the organization in this sample “enterprise data management” Lego’s block framework is to be a data driven company with competitive advantage and sustainable profit. Organization Mission, level 1, is the foundational sample Lego’s block framework. This are comprised of Operating Model, Company Culture, Talent, Business Strategy, Organization Readiness and Customer Journey as the foundational building blocks. These 6 blocks act as foundation for Data Driven Culture and enable data-centric thinking at all levels and across all business lines within the organization.   

Data Mission, level 2 of sample Lego blocks, brings in Domain Knowledge, Business Processes, Data Leaders & Organization, Data Strategy, Data Monetization and Data Architecture as next 6 level strategic processes to enable the organization mission. Business strategy alignment to data strategy is paramount in being data driven and so data literacy must be enabled across the enterprise. Business strategy for operational excellence may require data strategy from demand forecasting, profitability analysis and lead time optimization in contracts to business growth strategy that may involve social media monitoring, customer churn, campaign management as strategic data initiatives.

Enterprise Data Management Framework – Lego Example
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Data Foundation and Data Enablement, level 3 and 4 of sample Lego blocks, are the most critical levels and are very closely integrated in terms of creation and data assets governance. Data Platform, Data Privacy, Data Security, Data Engineering, Data Governance, Master Data Management and data quality are the 7 key pieces of the data to the asset puzzle.

Enterprise data management maturity for these levels drive and enable the speed of data maturity & data decisions. DCAM™ – the Data Management Capability Assessment Model is the industry standard framework for Data Management that is leveraged towards maturity for end-to-end processes.

Data Decisions, level 5 of sample Lego blocks, brings in the needed foundations of Reporting Cechnology, Data Science, Insights Generation, Data Metrics and Data Program Management. Data science business cases of optimization, simulation, machine learning, audio analytics, clickstream analytics, network analysis, natural language processing or image/video processing should be part of data program management with emphasis on insights generation and data KPI’s.

Data Return on Investment (Data ROI), level 6 of sample Lego blocks, provides direct value to businesses and allows for trust to be built within the organization towards being data enabled. This is the level where business, product and or data owners have direct visibility into top line growth or bottom-line operational efficiencies achieved with data innovation, data products, data services, and regulatory or compliance needs or services. The intention at this level is to build data enabled outcomes towards key business goals.

It is at this level that information consumers get agility and decision intelligence to create innovative opportunities for internal or external data products. Deep involvement of data owners and information consumers at this level allows for feedback to be provided into the technical eco-system towards changing data strategy needs.

Data as an Asset (DaaA), level 7 of sample Lego blocks, is the top level with goal of data driven company with competitive advantage & sustainable profit. Data driven performance management, value realization from data, consistent value tracking builds and enables data driven business model.

All the Lego block levels below enforce strategic and technical alignment to key performance indicators for data driven culture. Data driven culture expects information consumer autonomy, collaboration, and resilience. Business strategies change and Lego block processes in this tier are expected and allow for updates to enterprise data strategies. Organization readiness builds on “data culture” via data storytelling and data monetization is tracked using objectives and key results and data strategy updates are aligned with business strategy updates.


Enterprise Data Management maturity and the sample Lego view of data blocks allows us to visualize the data ecosystem towards a data driven organization that has competitive advantage and sustainable profitability. The sample “enterprise data management blocks” with 27 recommended Lego blocks stacked 7 blocks high provides a sample visual to build accelerated creation and a go-to market for innovative data products and solutions. Business opportunity size and impact, strategic alignment, investment needs and ability to execute key considerations are all enabled for the Data Assets Steering Committee and data assets working groups as the data value moves up the enterprise data management Legos blocks tiers.

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Karan Dhawal

Karan Dhawal

Karan is a recent graduate of the executive certificate program for Chief Data Officers from Carnegie Mellon University. He is an innovative, trustworthy, customer-centric data & transformation thought leader with a strong record of building and managing "cross-functional, high-performance global” teams. Engineer at heart he brings “logical” solutions to complex business problems leveraging his expertise in enterprise transformation & data management towards strategic value creation. Graduate of MIT Sloan School of Management with certification on “Artificial intelligence: Implications for Business Strategy” his passion leverages “enterprise data” to enable data monetization.

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