Tag Archives: business intelligence

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.”

 

 

Paradigm shift: From BI to MI

I listened to a Gartner webinar Information 2020: Uncertainty Drives Opportunity given by Frank Buytendijk yesterday and it got me thinking about the evolution (/revolution?) from business intelligence (BI) to machine intelligence (MI). I see this happening but not as fast as I’d like, as jaded as I am about BI. Buytendijk gave me some ideas for understanding this transformation.

From his book Dealing with Dilemmas, here’s Buytendijk’s formulation of S curves that show the uptake of new technologies and approaches over time, and how they are then replaced by newer technologies and approaches.

Screen Shot 2015-01-21 at 11.43.46 AM

From the book:

A trend starts hopefully; with a lot of passion, a small group of people pioneer a technology, test a new business model, or bring a new product to market. This is usually followed by a phase of disappointment. The new development turns out to be something less than a miracle. Reality kicks in. At some point, best practices emerge and a phase of evolution follows. Product functionality improves, market adoption grows, and the profitability increases. Then something else is introduced, usually by someone else. … This replacement then goes through the same steps.

This is where I think we are with machine intelligence for enterprise software. We’ve reached the end of the line for business intelligence, the prior generation of analytics. It has plateaued. There’s not much more it can do to impact business outcomes–a topic that deserves its own post.

What instead? What next? Machine intelligence. MI not BI. Let’s let computers do what they do well–dispassionately crunch numbers. And let humans do what they do well–add context and ongoing insight and the flexibility that enterprise reality demands. Then weave these together into enterprise software applications that feature embedded, pervasive advanced analytics that optimize business micro-decisions and micro-actions continuously.

We’re not quite ready for that yet. While B2C data science has advanced, B2B data science has hardly launched, outside of some predictive modeling of leads in CRM and a bit of HR analytics. BI for B2B doesn’t give us the value we need. But MI for B2B has barely reached toddlerhood.

We are, in Buytendijk’s terms, in the “eye of ambiguity,” that space where one paradigm is plateauing but another has not yet proved itself. It’s very difficult at this point to jump from one S curve to the next–see how far apart they are?–because the new paradigm has not proven itself yet.

It’s almost Kuhnian, isn’t it?

Recently one of the newish data scientists in my group said, “it seems like a lot of people don’t believe in this.” This, meaning data science. I agreed with him that it had yet to prove its worth in enterprise software and that many people did not believe it ever would. But it seems clear to me that sometime–in five years? ten years?–machines will help humans run enterprise processes much more efficiently and effectively than we are running them now.

My colleague’s comment reminded me of some points Peter Sheahan of ChangeLabs made at the Colorado Technology Association’s APEX conference last November. He proposed that we don’t have to predict the future in order to capitalize on future trends because people are already talking about what’s coming. Instead, we need to release ourselves from legacy biases and practices. This was echoed by Buytendijk in his webinar: “best practices are the solutions for yesterday’s problems.”

It’s exciting to be in on the acceleration at the front of the S curve but frustrating sometimes too. It’s hard to communicate that data science and the machine intelligence it can generate are not the same as business intelligence and data storytelling. People don’t get it. Then a few do. And a few more.

I look forward to being around when it really catches on.