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Twenty-first-century students would benefit from 16th-century habits of mind.

Source: How to Think Like Shakespeare – The Chronicle of Higher Education

“the really great discoveries ..have been made by men and women who were driven not by the desire to be useful but merely the desire to satisfy their curiosity”.

‘Working’ Analytics

December 11, 2015 — Leave a comment

‘Working’ Analytics: a useful term that distinguishes building deployable models that solve problems with a minimal amount of cost and complexity. Almost by definition, ‘Big’ is not ‘working’ analytics; it’s something else. When things get big, they get costly and complex. They get impractical to operationalize much less gain useage in day-to-day operations. A foundation principle for data-science that pre-dates ‘BIG’ is parsimony, also known as Occam’s razor.

For data scientists, ask yourself whether you want to be a ‘working’ practitioner or a developer of complex, inexplicable and mostly unused solutions. You can certainly make complex solutions but your job is to make them simple.

For employers, it is temping to believe in ‘unicorns’….a wickedly complex algorithm that creates a discontinuous shift in your industry and crushes the competition for years to come. But think about hiring people with the attitude and habit of contrarian thinking (e.g. putting a camera on a phone). Hire a blend of ‘working’ practitioners with a philosophy of parsimony, and ‘explorers’ who will thrash data and models regardless of where it takes them.

There are many, many working problems to solve while you are looking for your unicorn.

On this subject, a useful (and challenging) concept from Oliver Wendell Holmes:

“I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity.

Edward H. Vandenberg

Analytic Executives should be reading The Race Against the Machine, Brynjolfsson and McAfee. 2011.

I will quote from the book to raise the point that process re-engineering is critical to analytics return on investment.

“The most productive firms reinvented and reorganized rights, incentive systems, information flows, hiring systems, and others aspects of organizational capital to get the most from the technology…..The intangible organizational assets are typically much harder to change, but they are also much more important to the success of the organization.”

This is partly why analytics needs to rise to the level of a corporate function, with staff level executive leadership, so as to be able to move the organization to re-engineer itself for the technology.

Edward H. Vandenberg

Don’t expect the new VP of Advanced Analytics to do it all.  When processes and decision-making have to change to exploit insights from data, the line managers, with executive sponsorship, must carry the weight.  Without that, you have models that work on paper but aren’t used in the operations.  It’s tempting to blame the analytics leader but that is misplaced.  Operations managers actually resist doing things in a new way, despite the math telling them otherwise. And the VP of Analytics likely has no power to change that, short of appealing to the executive staff–not a popular move for the analytics lead that also must evangelize for new projects and problems to solve.

Also, commanding a small specialized team is not normally seen as a position of clout, despite the title and sponsorship.  Managers of large operations (financial and headcount) naturally have more influence in most organizations, even if they have a peer title to the advanced analytics leader.

In defense of the operations level manager, they are rightfully reluctant to be accountable for mathematics that is probablistic and hard to understand.  Likely they are also normally protective over the operational influence that analytics can have within their business units.  This creates tension that makes analytics ROI go sideways.

What’s the answer?  For analytics to truly be exploited, operations management must step up….understand the science more, be ready to believe in it and lead their operations to adopting it.  That means they are hired for it, trained for it and managed for it.  How an organization mobilizes for that is under the leadership of a Chief Analytics Officer and a full program management approach to advanced analytics.  Deploying advanced analytics must be seen as the path to promotion for career operations managers.

Secondly, the advanced analytics effort must include Test and Learn experiments for every model pilot that help prove the in-use value of models beyond validation on historic data.  This is a natural extension of the model development work

Credited to R blogger Drew Conway

A great graphic depicting the interesting mix of qualities in data scientists.

via The Data Science Venn Diagram.

Thanks to Joe Baird for this insight on the fit between traditional CIO’s and the advanced analytics function.   I would extend the argument further.  Today’s CIO and IT organizations are not positioned to build, run and promote advanced analytics inside their organizations.  There are synergies between IT and advanced analytics at the tactical level.  But at the leadership level, most CIO’s don’t know the territory well and their experience is not aligned with data science.  Analytics is not technology. It is technical.  It is convenient to refer to analytics as a science to help make this distinction.  When considered this way, most would agree that IT is not aligned with a scientific effort.

The Chief Insight Officer, suggested by Baird, is a leader hired by senior management to drive analytics forward.

CIO’s and their management will not want to be left out of the excitement and value proposition proferred by advanced analytics.  It will be a challenge for IT executives and managers to ‘bolt-on’ much insight and knowledge, after careers and experience dedicated to the traditional IT function.

Recommended Reading

The CIO in the Age of Analytics: From Infrastructure to Insight | Joe Baird.

In my experience, most data scientists aspire to a career path that includes interesting work, valued by their organization that makes a difference.  They mostly do not grow into managers or expand their scope of authority.  On the one hand, executives don’t necessarily need to worry about creating a full career growth path for these unique employees.  Secondly, there simply aren’t many management roles in this narrow operating area.

On the other hand, it can be problematic in how these individuals fit into the overall human resource model.

Confirm with your own data scientists what their desires and career expectations are.  I submit that most of them do not want to manage but do want a span of control concerning analytics that count.

There is a technical level of leadership you should honor within the team.  More experienced and seasoned scientists naturally have some control over more junior staff. That leadership is important, even if the technical leader does not formally manage his or her team members.