Business Architects Are Data Scientists

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A 2015 McKinsey report states that advanced analytics will be used three times more often in 2020 than today. That’s just 4 years from now. The report also states that insight-driven companies out-perform peers. But the trick lies in not just distinguishing relevant from irrelevant data or translating these data into insights. Those are simply “table stakes” and companies are expected to do those activities well at a minimum. What separates the achievers from their peers is their ability to translate these insights into “impactful frontline actions.”

We have the same goal as data scientists to provide actionable insights. We often hear this mantra of “actionable insights” from business architects. If not, you should familiarize yourself with this term right away. “Actionable insights,” we shall mutter with every breath we take. 

What is an insight and what does the term “actionable insights” mean? Simoudis (2015) defines insight to be “a novel, interesting, plausible, and understandable relation…derived from a data set.” This can also mean a set of associated relations derived from a larger set of relations. This definition implies the following key properties: actionable, measurable, stable, reproducible, robust, and enduring. For an insight to be actionable, it should lead the decision-maker to develop an action plan. The action plan should be impactful. It should either prevent or produce an outcome. 

Here are a few more questions we need to address when we seek to create actionable insights.


  • How do we create actionable insights or where do we go to look for them?
  • When is the right time to do it?
  • What if we provide actionable insights and they are not acted on? 
  • What are the risks for not acting?


A business architect is a data scientist because we both provide actionable insights.INFORM uses three classifications for analytics that we can compare with the three activities that business architects perform on a daily basis.

  • Descriptive or reporting analytics
  • Predictive analytics
  • Prescriptive or decision analytics

Let’s correlate these activities with what business architects perform on a daily basis.

Characteristics of different forms of analytics   Benefits of understanding causality   Business architecture activities & deliverables  
 Descriptive    Explanation Business capability modeling, business use case development
 Predictive  Prediction Capability-based portfolio analysis, roadmapping
Prescriptive  Intervention  Summary of findings & recommendations, list of prioritized projects








In my daily job as an Enterprise Business Architect at a Fortune 50 company, I provide decision making support to executives who report to the CFO. It is a staff function. My internal clients expect me to do “staff work” where I plan, design, implement, and report on business architecture-related projects to produce business outcomes.

One of the most vexing problems that CFOs face today is investment optimization. More specifically, CFOs typically ask where the organization’s resources are best allocated and if the organization is “putting money where its mouth is.” For example, if the organization wants to be an industry leader in risk management within the next 2 years, the CFO will most probably need help in identifying which technology projects align with this strategy and if these projects have adequate priority in accessing the right resources. In this case, a business architect can help plan, design, implement, and report on a roadmap of technology projects that enhance the organization’s risk management business capability.

This business architect gathers and analyzes available data related to the risk management capabilities of the organization. Internal and external data include the organization chart, job titles and job families, incentive mechanisms, business process static and streaming data, application systems data, vendor and business partner information, implemented technology and related tool information, information related to assets such as buildings and hardware, competitor data, social media data, and even macroeconomic financial information. Business architecture analysis tools include business capability modeling and mapping, capability-based portfolio analysis, strategy mapping, business use case development, and so on. An ideal by-product of this analysis is a summary of findings and recommendations that include a roadmap of prioritized technology projects mapped to the risk management business capability. The CFO can use this set of recommendations to start meaningful conversations with internal and external stakeholders.

In summary, the business architect in your organization today is your data scientist. Train them well and you will reap the benefits of what analytics can produce for the organization. The insights that they will generate will produce positive business outcomes. Isn’t that what actionable insights are supposed to do?


INFORM. (2015 December 20). Accessed at the INFORM web site at 

McKinsey. (2015). Next generation GICs. McKinsey.

Simoudis, E. (2015). Exploring the process of insight generation. Accessed from O’Reilly web site at


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