The Center recently hosted a virtual roundtable on an important topic to CIOs, Partnering to Drive Change through Analytics, where we explored how organizations are applying analytics best practices today, the business value that the best performing organizations are experiencing.

In preparing for the session, I developed some observations on the topic that I think provide a useful perspective for CIOs as you all consider taking action on getting value from leveraging analytics and creating business value in your own enterprise.

Not a new topic

In researching the literature prior to the session to provide some historical perspective on how far back this topic goes, I discovered that people have been talking about what we describe as this massive explosion of data, initially called information overload, for longer than many of us have been in this industry. From what I could ascertain, the earliest known attribution of the term “information overload” was credited to an IBM advertising supplement in the New York Times on April 30, 1961. So obviously this is not a new topic.

So what’s different now: more data than ever before

That being said, we are clearly at a new frontier of information overload and explosion of data, which is astronomically more challenging, but at the same time very exciting from the point of view of being able to impact the way we do business.

To put this into a context for today, I like to look at the retail industry, which is at the forefront of collecting massive amounts of data, and more importantly putting that data to use in changing the way they go to market, manage the customer experience, streamline the supply chain, and create the next generation customer. Walmart is often cited as a great example as a retailer leveraging data and analytics across all of these elements. A fact that I found particularly noteworthy - as of about nine months ago, Walmart was processing over a million customer transactions per hour, feeding databases that were estimated in excess of two and a half petabytes (roughly the equivalent of 167 times all of the books in the Library of Congress.)

Walmart has unprecedented insight into what their customers are doing, what they want, and how to respond across their 8,500 stores worldwide. At the same time, they need to find a way to translate that insight into actions that drive customer benefit and stakeholder value.

How should CIOs respond to this incredible opportunity?

“Revolutions in science have often been preceded by revolutions in measurement,” said Sinan Aral, a business professor at New York University, in a 2010 article in the Economist. He went on to say that just as the microscope transformed biology by exposing germs, and the electron microscope changed physics, the proliferation of data is turning the social sciences upside down.

I see that as representative of the conversation we as CIOs should be having now – how to apply this insight, these data, to become the microscope for how businesses can learn and advance ourselves and our industries. There are a few takeaways for me from Katharyn White’s presentation that I would encourage CIOs to consider in looking to manage these conversations.

• It’s a journey – the research presented reflects the evolutionary process of adopting, implementing, and embedding the value of analytics in the enterprise. And as Katharyn emphasized, the process of gaining buy-in and creating change is actually a core part of the implementation. In leading change management efforts myself over the years, I see that implementing analytics is the type of program that requires deep change across the enterprise, and core shifts in the way people make decisions, operate and go to market. CIOs can leverage their expertise in change management, as well as their enterprise-wide view of data and information, to make the journey more successful.
  

• Learn from others – the research also showed that companies can be successful getting to value across many industries; success in analytics is not industry dependent, or even geography dependent. There are companies of all types applying best practices and getting exciting results – whether it is in growing sales, increasing efficiencies, or improving individual customer interactions. Katharyn shared the view that success with analytics benefits greatly from a cross-industry perspective, and from seeking out examples from many other environments as a way to leapfrog in your own industry. This echoes my own experience – and that of the Center’s commitment to peer-sharing. CIOs should seek to systematically leverage learning from others to innovate in an emerging area like analytics.
  

• Leverage your C-suite relationships – by definition, getting value from analytics, especially as companies migrate from aspirational to experienced or experienced to transformed (as described in the research), clearly requires data or information to be collected across functional silos and/or across multiple business units. Whether or not the data collection and management moves to the point of being centralized within the enterprise, there needs to be an integrated and shared view of who is doing what, and how they data can be cleaned, verified and leveraged across the silos. This is an important opportunity for CIOs to leverage your hard-won C-suite relationships, and reach out to connect on an integrated view of the possibilities to move to value in your enterprise. One partnership in particular that Katharyn mentioned – the one with the Chief Marketing Officer – struck me as interesting for CIOs to consider. Analytics is at the forefront of where marketing and technology are coming together, and the partnership represents an emerging opportunity for CIOs to truly push the needle on analytics and how the company goes to market.
  

What are you doing in your organization to move the needle on the path value through analytics? What lessons can you share with others?

In a previous blog on this general topic, I mentioned the importance for the CIO to understand Return on Investment (ROI) and its purpose in the area of financial management.  Historically the concept of ROI and its various measures including payback period, internal rate of return (IRR) and net present value (NPV) were designed to allow an organization to rank alternative capital investment opportunities with different cash flows as part of their capital budgeting process.  Those with the greatest ROI relative to risk were selected and funded.

As I learned in graduate school, one of the most common mistakes people make in applying ROI methodologies is to focus on the measurements themselves and not the underlying assumptions surrounding the cash flow forecasts that produce the ROI.  Here is an example from my own experience.

When I was the CIO for the Department of Human Services (DHS) in Oregon, we initiated a state wide data center consolidation project.  Seventeen agencies participated and DHS was the largest.  The capital investment was estimated to be approximately $24.0 million to build a new state of the art data center.  In order to justify the investment the Department of Administrative Services (DAS) was required to show a two year payback in cost savings, based solely on projected personnel savings (i.e., you don’t need 17 data center managers in a consolidated facility).  A two year payback is roughly a 50% IRR.

When the project was completed, DAS and the state legislature considered it a failure because the estimated personnel savings were not achieved.  To fill the hole, the state agencies who participated were required to cut their budgets by the amount of the shortfall, which displeased the agency heads and agency leadership in general.  It was seen as another IT fiasco.  What went wrong?

First and foremost, setting an ROI goal for this project at a two year payback or 50% IRR created the wrong incentive for management.  In order to achieve this goal, DAS artificially reduced the number of staff required in the new consolidated data center and accelerated the staff reductions to increase the projected savings and achieve a two year payback.  This was a mistake and lead to the perception of a failed project, reinforcing my earlier point that the validity of the cash flow projections is paramount.  So where did Oregon go wrong?

Essentially, the cash flow projections didn’t reflect reality and were backed into in order to achieve an arbitrary ROI goal.  Here are some examples of items that were overlooked:

1. In the out years, the new consolidated data center had the capacity to provide computing services to all state agencies.  This benefit was not estimated or included in the cash flow projections.
2. The project consolidated 17 separate agency data networks generating significant savings not included in the cash flow forecasts.
3. The consolidation of the data centers freed up valuable real estate in agency headquarters that was not valued or included in the cash flow forecast.
4. The construction of the data center was completed on schedule and at a cost $2.0 million below the budgeted $24.0 million.
5. Positions were eliminated (approximately 30+ at DHS) and the people who were in these position were able to fill other jobs that were vacant, leave state government for the private sector or retire.
6. The need to invest in 17 separate facilities going forward was avoided but again this benefit was not estimated or included in the cash flows.  The State wouldn’t accept cost avoidance as a benefit but maybe this has changed after Katrina and the Gulf of Mexico oil well blowout.

Bottom line, an IT project that produced substantial benefits to the State over the long run was deemed a failure due to highly suspect ROI analysis and guess who took the heat, the CIO’s.  In reality and taking into consideration all the benefits produced, if this project achieved a 4 year payback and only a 25% IRR, that’s a lot better than I’m doing in the market.