Data Value Chain: EDM Block Framework – Levels 3 & 4

In 2022, we saw that Data Strategy played key role in the success of top performing companies globally. One of the reasons for the continued success of companies like Walmart, Amazon, Apple, Exxon Mobil, Google, CVS health, Toyota, Samsung etc. is being “data driven”.

This article is continuation of the proposed enterprise data management block framework as published in April 2022 that visualizes the main building blocks to be data driven.

There are 26 proposed Blocks within the framework with 7 levels defined as: Organization Mission, Data Mission, Data Foundation, Data Enablement, Data Decisions and Data ROI.

The goal of these defined levels is to help set any organization to be data driven for competitive advantage by enabling growth, productivity, profitability, differentiation in products, services, customer experience or being compliance savvy.

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Last quarter article in this column focused on Data Mission- Level 2 of the proposed organization.

Data as an Asset (DaaA) is achievable by having data mission as a key component in the Enterprise Data Management blocks framework. There are 6 critical blocks: Data Architecture, Data Monetization, Data Strategy, Data Leaders & Organization, Business Processes and Domain Knowledge that integrates Data Mission. Data mission drives and provides acceleration as needed to set up data driven culture. It focuses on the Why and the What from a data point of view to accomplish organization mission.

Let’s review Level 3 Data Foundation, Level 4 Data Enablement, and key relationships to the other proposed levels of Organization Mission, Data Mission, Data Decisions and Data ROI in this article.

Data Engineering at core is the building of systems, applications, and processes that enable collection, access, and usage of data across multiple types and multiple sources of data. Data in many cases is the output of the business processes, informational, and operational technology. Collection and sourcing of data, transformation from raw to organized, storage and access for faster and as needed for overall organization goals are all part of this foundational process.

Technology selection and decisions for data ingestion, processing, storage, and access can all become a constraint or a differentiator for any organization. In many cases, these decisions are high in initial capital costs and tend to be multiyear programs with business ROI attached to speed of access or speed of data decisions enabled within the organization.

Data Security is the driving force behind being a data driven company. Unauthorized data access brings in multitude of issues for any organization including regulatory fines, loss of trust with customers, and above all, loss of revenue. Data security as a practice is an organization mindset to protect digital information across entire lifecycle from unauthorized access. Data security domain includes physical, logical, hardware storage, administrative access controls, policies, and procedures needed to manage data eco system within the organization.

Data Privacy is the guiding path for being a data driven company. Collection and usage of personal information for customers is regulated and hence needs to be managed as a capability. The proposed framework has a foundational block at this layer to ensure that data privacy is built as part of data ingestion, collection, and tracking in data pipelines before enabling usage. Data privacy strategy is built on data minimization, purpose definition and limitation, and compliance and transparency. Technology selection and decisions for data privacy along with enterprise-wide policies and procedures make this data foundation block very key to the overall success of any organization.

Data Platform is the infrastructure that brings integrated technologies and operational processes to acquire, store, transform, and deliver as a data eco system for the organization. Selection of the right data platform and enabling scalability and stability is often the most critical limiting or accelerating factor towards a data driven organization. A best-in-class data platform provides agility, automation, and accuracy across all types of data ingested, stored, and shared in the data eco system of the company. 

Data Governance in context to enterprise data management is the key capability of the organization in understanding and managing the data. The relationship between people, processes, technology, and data can all come together in terms of data value chains by activated data governance. Activated data governance allows to manage the availability, sharing, usability, and integrity of the data by a set of policies and procedures. Effective data governance is NOT compliance focused on data’s return of investment and IS focused on data ownership and joint accountability.

Master Data Management is an important block as defined in the Proposed Data Enablement level of the above framework. Master data in cumulative sum of core entities like customer, product, prospects, contacts, vendors, hierarchies, chart of accounts, etc., are shared across enterprise. The capability is technology enabled with vision of data governance to ensure uniform, accurate, and consistent universal identifiers for context-based master data in the organization’s business processes.

Data Quality is the sum of experiences as the users of data bring insights towards strategic business goals. This capability is technology enabled towards defined maturity to measure data trust based on multiple dimensions of data, including completeness, consistency, availability, accuracy, and fit for purpose. Data quality as part of the Proposed Data Enablement level in the framework accelerates data health towards making the organization data driven.


Data Foundation and Data Enablement are critical enabling processes as proposed in the Enterprise Data Management Block Framework. There 7 proposed blocks: Data Engineering, Data Security, Data Privacy, Data Platform, Data Governance, Master Data Management and Data Quality that answer the “How” for any organization can move towards being data driven. The proposed layers of Data Foundation and Data Enablement provide infrastructure, processes, and technology to any organization for data decisions. In many cases, data foundation and data enablement are considered the highway that needs to be built before business leaders can drive on it towards making data driven decisions. 

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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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