analytics, data science

The dialectic of analytics

From Gartner’s report The Life of a Chief Analytics Officer:

Analytics leaders today often serve two masters:

  • “Classic constituents,” with maintenance and development of traditional solutions for business performance measurement, reporting, BI, dashboard enhancements and basic analytics.
  • “Emerging constituents,” with new ideas, prototypes, exploratory programs, and advanced analytics opportunities.

I serve these two masters today in my job as VP, Data Science & Data Products at IQNavigator.

In my capacity as data science lead, we’re exploring innovative data-driven features built on data scientific techniques. In my capacity as data products lead, we are mostly still in the traditional business intelligence space, focusing on reporting and dashboards. Eventually the data products IQN offers will encompass both business intelligence (BI) and machine intelligence (MI) approaches but we have to start with what customers demand, and for now that is BI, not MI. I foresee that eventually MI will entirely eclipse BI but we’re not there yet, at least not in the non-employee labor management space.

I’ve come to believe in the importance of basic reporting and analytics capabilities, and that they should be distributed throughout the organization in self-service fashion. I see these capabilities as mainly playing a role in operational, day-to-day use, not in providing the aha! insights that people are so desperate to find and so sure exists if they only turn the right set of advanced analytic tools and personnel loose on their data.

I also foresee that the data science / machine intelligence space will mainly serve to optimize day to day operations, replacing business intelligence approaches, not surfacing wild organizationally transforming possibilities.

Gartner suggests developing a bimodal capability for managing analytics:

A bimodal capability is the marriage of two distinct, but coherent approaches to creating and delivering business change:

  • Mode 1 is a linear approach to change, emphasizing predictability, accuracy, reliability and stability.
  • Mode 2 is a nonlinear approach that involves learning through iteration, emphasizing agility and speed and, above all, the ability to manage uncertainty.

This applies to more than just analytics, of course. Gartner suggests it for a variety of IT management domains.

What would this look like? IQN already has an approach for product development that is bimodal in nature. We use agile development practices for product development. But we layer on top of it linear, time-based roadmapping as well as Balanced Scorecard departmental management. This is not as clumsy as you might imagine. It is more dialectic than synthetic in how it functions, with conflict occurring between the two approaches that is somehow resolved as we iteratively deliver features out into the marketplace, often on the schedule we promised (though not always).

In my own small world of data science and data products we do something similar, combining agile iterative processes with more linear and traditional project management. We use a Kanban-style process for data science projects but also layer on more waterfall-esque management for capabilities we need to deliver at a certain time to meet roadmap commitments.

I’m not sure I like the word “bimodal” to capture this approach. Maybe I will think of it as “dialectic.”

 

 

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