Data Management as a Business Discipline – Part 3: Enabling Frameworks

I have written two blogs claiming that data management is most important Commercial Discipline for the modern organization. In the first blog, “Why data management is today’s most important business discipline”, I laid out my logic, including:

  1. Artificial Intelligence (AI) is the most powerful commercial discipline of our generation, with the ability of AI to continuously learn and adapt to create new sources of customer, product, service and operational value.
  2. The ability of AI to make effective and responsible decisions largely depends on high quality, accurate, complete and unbiased datasets.
  3. Data management’s focus on manufacturing high-quality, accurate, comprehensive, and unbiased datasets is critical to economic growth in the 21st century.
  4. Thus, data management is the most important business discipline of the 21st century.

In my second blog, “Data Management as a Business Discipline – Part 2: Theorems and Principles”, I formalized the definition of a Business Discipline.

A Company Discipline consists of systematic research, observation, measurement and experimentation resulting in the assimilation of learning into laws, theorems, concepts, principles, practices, frameworks and formulas to enable consistent application and continuous improvement from application real of this discipline.

And as part of this blog, I’ve also presented the key “theorems and principles” of data management (in the form of playing cards) that underpin the “data management as a business discipline” argument. including Economic Value Curve, Nanoeconomics and Analytical Profiles.

In this third blog, I will review real-world data management implementation frameworks that support the “Data Management as a Business Discipline” argument.

The Data & Analytics Business Model Maturity Index (originally called Big Data Business Model Maturity Index) was the first data management framework I developed. I created this framework to help organizations benchmark and guide their journey to becoming more efficient at leveraging data and analytics to power their business and operating models. It is a measure of the business model efficiency, not technical skill, with data and analysis (Figure 1).

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Figure 1: Data and Analytics Business Models Maturity Index

I have also provided details or verified items on the Data Management, Data Science, Business Management and Cultural Capabilities needed to navigate the Data & Analytics Business Model Maturity Index (Figure 2).

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Figure 2: Data and Analytics Business Model Maturity – Stages Maturity

Perhaps my most powerful data management framework, the value engineering framework, ensures that organizations focus their data and analytics capabilities on optimizing and accelerating value creation capabilities of the organization (Figure 3).

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Figure 3: Value Engineering Framework

I created and orchestrated a series of design canvases to support collaborative processes of ideation, validation, evaluation and prioritization of business value engineering and data science (Figure 4).

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Figure 4: Value Engineering Framework Design Canvas

The last data management framework that I will cover (in this blog) is the “Think like a data scientistMethodology (TLADS). The methodology drives collaboration and alignment of business and data science teams in designing, leveraging, and detailing data engineering and data science development requirements to support key cases use of the organization (Figure 5).

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Figure 5: “The Art of Thinking Like a Data Scientist” methodology

The framework comes with design canvases, worksheets, examples, and hands-on exercises, all designed to reinforce the fundamental concepts of the TLADS data science development methodology based on collaborative ideation and centered design. on humans (Figure 6).

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Figure 6: “Think like a data scientist” Design Canvases

If our goal is to transform data management into a business discipline, we need a series of pragmatic and interdependent frameworks that proactively guide the organization in driving cross-organizational collaboration and alignment on where and how to apply data and analytics to feed into the organization’s business and operational models. And these frameworks should be paired with design outlines, worksheets, examples, and hands-on exercises so that we can evolve training and operationalization around data management as the most important business discipline of our generation.

Ramon J. Espinoza