Defining The Data-Driven Enterprise

There has been much ado lately about the need to make data-driven decisions, to bring the best information to bear in the decision making process. The idea is that better information will lead to better decisions. A data-driven enterprise is one in which decision makers seek, and then consider relevant data when making their decisions.  This blog explores what that means and what might prevent you from becoming such an enterprise.

The Data-Driven Enterprise

McKinsey’s The Data-Driven Enterprise of 2025 defines data-driven enterprises as having 7 characteristics:

  1. Embedded data. Data should be embedded in every decision, every interaction, and every process within your organization.
  2. Real-time data. Given the increasing pace, and rate of change, faced by organizations it tell you that data must be processed and delivered in real-time so that decision makers may take advantage of it.
  3. Flexible data stores. Our data stores must be sufficiently flexible, and of high quality, to enable integrated, ready-to-use data.One such way of doing so it to build adopt the DataVault2 methodology to build an enterprise-class data warehouse (DW).
  4. Data as product. Your operating model should treat data like a product, instead of as part of the deliverable of an application development project.  Build cross-functional teams dedicated to the long-term evolution and support of a data source, adopting agile database techniques to support full-fledged data DevOps (also called DataOps).
  5. Data as value. The chief data officer’s role is expanded to focus on generating value for your organization, not just operating data sources.
  6. Freed data. Data-ecosystems, rather than siloed data sources, must become the norm.  Data siloes within your organization must be torn down, and ecosystems of shared data between organizations must emerge to improve our ability to make better decisions. This is particularly true when it comes to decisions pertaining to environmental and societal issues.
  7. Automated data. Data management is prioritized and automated for privacy, security, and resilience. Existing and emerging data privacy regulations, ongoing security threats, and the need for high-quality timely data requires greater levels of data automation.

Are You Ready?

Although McKinsey’s 7 characteristics are straightforward, your organization likely faces several difficult impediments:

  1. Cultural inertia. Many people within your organization may still be making decisions based solely on their gut feel. It’s convenient and seem to work out… except when it doesn’t. Saying that you’re a data-driven enterprise is easy to say, actually acting in this manner requires discipline.
  2. Inadequate data skills (decision makers). Do your decision makers have the skills to access and manipulate data appropriately? Do they understand the data that they’re being presented? Do they know what questions to ask?  What questions not to ask?  See my blog Are Your Decision Makers Capable of Making Data-Driven Decisions? for a detailed discussion of this issue.
  3. Data technical debt. Data technical debt (DTD) refers to quality challenges with existing data sources. A fundamental challenge with data-driven decisions is “garbage in, garbage out (GIGO)” – your decisions will only be as good as the information they’re based on. Many organizations are struggling with high-levels of DTD, preventing them from benefitting from a range of modern techniques, including data-driven decision making and artificial intelligence (AI)-enabled technologies.
  4. Inadequate data skills (technical practitioners). Many organizations struggle to attract and retain data professionals, let alone agile data professionals. More on this in future blog postings too.

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