Data Management as a Business Discipline – Part 2: Theorems and Principles

In the blog Why Data Management is Today’s Most Important Business Discipline, I challenged the business and IT communities to reshape the way data management is discussed. Transform data management from an IT practice to one business discipline focuses on using data (and analytics) to deliver business and operational performance Results.

If data is “the world’s most valuable resource” (thanks The Economist), then corporate leadership must do so frame their expectations for data management as a business discipline to guide the organization in using data to derive and drive new customer, resource, product, service and operational values.

But what is a business discipline and what do we need to create to fulfill this vision of data management as a business discipline?

A discipline consists of systematic research, observation, measurement and experimentation leading to the assimilation of what is learned into laws, theorems, concepts, principles, practices, frameworks and formulas to enable the consistent application and continuous improvement of the real-world application of this discipline.

According to the article “6 Attributes of an Academic Discipline” by Joshua Ki, an academic discipline consists of the following features:

1 – subject of research. “Disciplines have a special feature subject of research (e.g. finance, economics, mathematics, computer science), although the subject of research may be shared with another discipline.”

2 – Concentrated expertise. “Disciplines have a body of accumulated expertise Regarding their research subject, which is specific to them and not usually shared with any other discipline.”

3 – theories and concepts. “Having disciplines theories and concepts who can effectively organize the accumulated expertise.”

4 – terminology. “Disciplines use specific terminologies or a specific technical language adapted to your research subject.”

5 – research methods. “The disciplines have developed specifically research methods according to their specific research needs.”

6 – Institutional manifestation. “Disciplines must have some institutional manifestation in the form of subjects taught at universities or colleges, the respective departments and affiliated professional associations.”

I think Data Management ticks every one of those boxes academic discipline Perspective. And I will add #7 of a business discipline Perspective:

7 – “Disciplines have documented best practices, insights, and stories associated with the successful and unsuccessful application of that discipline real situations to create a quantifiable and sustainable business and operational value.”

For a business discipline To be relevant and to ensure engagement of business and IT leaders, this discipline must create value. And how can we conduct these value creation discussions?

Yes, we can create a series Playing cards summarizing the value creation theories and concepts (Discipline Requirement #2) that our Data Management Business Discipline must encompass.

Also, think how fun it would be to whip out these playing cards in meetings with your business stakeholders or your data and analytics teams as a reminder that the focus of these data management efforts must be value creation.

Let’s look at these cards and start imagining how you might use these cards to create a value-based culture (through data and analytics)!

Figure 1: The Royal Court of Data & Analytics driven value creation

  • Value engineering framework decomposes the organization’s strategic business initiative into its supporting business components (stakeholders, use cases, decisions, KPIs) and data and analytics requirements.
  • Nanoeconomics is an economic theory of individualized entities (human or device) predicted behavioral and performance propensities.
  • Analytical profiles codify, share, reuse, and continuously refine the predicted propensities, patterns, trends, and relationships for the organization’s key human and technical resources or entities.
  • Use cases are a group of decisions around a common KPI or metric in support of specific business initiatives that have quantifiable business or operational value.

Figure 2: The lieutenants of data and analytics-driven value creation

  • data economic multiplier effect, formulates the calculation of the cumulative attributable value of a dataset from reuse of the same dataset across multiple use cases, based on the economic multiplier effect.
  • Economies of Learning measures the effectiveness of an organization’s value creation through continuous learning and adaptation to ongoing changes in the business and market environment and ecosystem.
  • The Schmarzo Economic Digital Asset Valuation Theorem highlights three effects that result from sharing, reusing, and continually refining an organization’s data and analytics assets.
  • The Think Like a Data Scientist method is a collaborative ideation, value-based, human-supported “scientific method” that aims to unleash the prescient intuition of subject matter experts.
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Figure 3: The foot soldiers of data and analytics-driven value creation

  • prioritization matrix Facilitates collaboration between business and data science stakeholders to identify use cases with both reasonable business value and reasonable feasibility of successful implementation
  • Canvas for developing hypotheses captures data science requirements, including business goals, KPIs and metrics against which to measure success, key decisions by key stakeholders, potential ML capabilities, and costs of false positives and false negatives
  • AI utility function evaluates multiple variables and KPIs across multiple dimensions to drive the actions or decisions the AI ​​model can take or make to achieve desired goals.
  • Key Performance Indicators (KPIs) are quantifiable measures used to assess an organization’s progress and ultimate success is in meeting its business or operational goals and objectives.
Slide 4-2

Figure 4: The jokers of data and analysis-driven value creation

  • features are the mathematically transformed variables that AI/ML models use during training and inference to make predictions and guide actions or decisions
  • Analytical Results are the results of analytical models that take into account multiple variables and characteristics to predict the likelihood of a behavior or action expressed as a single number. For example, a credit score predicts the likelihood that a person will pay off their loan.

I hope this article has provided both fun and a dose of creativity on how your organization can use the value-added playing cards to reshape the discussions and expectations around data management – that data management is not just an IT practice, but the most important economic discipline in the 21st centurySt Century.

Now I just need to figure out how to cheaply convert these into actual (affordable) playing cards to give out in my workshops (now that would be very cool!!).

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