Archives For Enterprise Organization

This is the future state for how analytics work will be organized across your value chain (operating units, and functional units) where analytics is applicable to achieving some already defined business goal (example, speed to market). The enterprise organization must flow from your strategy and be a fit for your companies priorities

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

IoT is your job. Data Science has always been greedy for complex data and has a pretty good handle on how to process it for insights and predictions.

For most of us and most projects, the practically of getting it to model and having it available to execute run-time algorithms has been the barrier. IoT data is meaningless without algorithms to process it and provide information and predictions/optimizations from it.

IoT is exciting and will change the fundamentals of businesses and industries.  The technology is interesting and very dynamic.  All of this has implications for your analytic operation and practice.

The more interesting and challenging future of IoT (and also part of your job): what are the new processes, user roles, use cases, management scenarios and business cases for IoT. Who will manage the IoT function ‘X’ of the future and what does that role look like.

The other important reason for you to pursue IoT is to keep your data scientists engaged and retained.  Many are still working around the same types of projects, methods, tools etc. that have been around for 10 plus years.  All projects are interesting and challenging but some of them are getting bored.

This means research and discussions with your colleagues, sponsors and stakeholders (while you are still working in the pre-IoT world). Enjoy!

Edward H Vandenberg

Over the next few years, advanced analytics will emerge as a separate corporate function in some leading companies.  The activity is a mix of IT, Finance, Process Management and Knowledge Management that simply defies the current corporate functional matrix.  Add to that a highly skilled work force whose ethical behavior is (will be) a matter of corporate ethics and law.  It is a strange prediction, admittedly.   But Finance, HR, and Accounting started somewhere.  And that is where advanced analytics is now.

Astute Analytics Executives will position themselves for this evolution.  It is a positive development for the firm, what ever industry you work in.  Analytics is the customer interface in a mass market environment where the preferences of one is the required service or product model.

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

Advanced Analytics should be an enterprise shared service.  But build that with the end in mind.  What is critical to be shared, either because of availability or quality?  What is critical to be focused or distributed, because of demand, domain knowledge and legacy issues?  You will find that there is more gained around shared aspects than lost when analytics teams are not dedicated to a specific function.

Getting to a shared service enterprise model can be challenging. I will advance this point of view in future posts.

Erik Brynjolfsson: “Big Data should be viewed as a management revolution”

via New Tools Beget Revolutions: Big Data and the 21st Century Information-based Society – The CIO Report – WSJ.

This is why advanced analytics is something different.  What other recent business innovation is so important and ubiquitous as to be labeled a ‘management revolution’ .  As yet, there is very little structure and form to this revolution and few leaders who understand the technical foundation.

That is the subject of this blog.


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.

To some senior executives, analytics is mistaken for an IT function. This may lead to a misalignment of the analytics function or business unit. The alignment question is critical to getting work done, hiring people and communicating to internal stakeholders. Truthfully, most IT executives also probably think analytics is another IT service (or would like it to be). Analytics is a science and does not fit into the IT business model and will likely never perform well within IT. But clearly technology is critical to data science. I propose that IT establish a special service practice, organized and staffed specifically to enable the analytics function. If not that model, then analytics should have its own technology staff, reporting up to the analytics executive.  Either way, just as the organization overall needs to mobilize and re-engineer itself to fully exploit analytics, IT must step up to it’s critical but supporting role for analytics.

Analytics is a Science

November 10, 2012 — Leave a comment

The lately applied nomenclature for advanced analytics creates a useful distinction and a challenge to businesses.  First, analytics is a science: essentially complex hypothesis discovery using quantitative theory and methods.  As a science, it naturally has its own processes, people and tools not like other activities. The challenge to business executives is where does science fit in a non-scientific company?  Advanced analytics does not have a natural home in most companies.  That’s leading to some organizational incoherence when it comes to growing the analytics function beyond a point solution or functional silo, in marketing or pricing for example.

Analytics executives must make the case that they manage a scientific function.  As such, it has its own organic management and structure.  It’s not really part of another function.  If so, the question still stands, where does the advanced analytics function belong in the corporate structure.

There is a reasonable case, being made now by several large companies, that it is its own strategic business unit with leadership alignment up to a chief executive.  I suggest that more companies will establish the role of Chief Analytics Officer.

In another post, I will discuss where this level of leadership will come from in the current level of maturity for this science.

Edward H. Vandenberg

Tom Davenport’s new book ‘Enterprise Analyitcs’ is just out. A quick browse shows a great selection of topics and authors with four chapters on organization and resources.
If you are in a large company, it’s worth thinking what “enterprise” means in your business. Chances are, analytics is currently a sprawling collection of small teams spread over different functional units, geographies, legacy companies, lines of business etc. Further, these units were likely initiated as a point solution activity, rather than a planned roadmap-but now you own them. The pathway to Enterprise Analytics will look daunting and disruptive for many and may not have support. Making this a compelling proposition with a plan that fits the legacy state is the job of the Analytic Executive and perhaps the future Chief Analytics Officer. Few people in your organization are likely to have the knowledge, credibility and judgment to support you. This subject is what the Analytic Executive blog is all about. Thanks to Tom et al for the book and to Tom for his recent article in HBR on Data Scientists.

Edward H. Vandenberg