Analytics is Not a Dirty Word
What seems like a lifetime ago, I was intelligence officer in the Air Force. Beyond creating Snoopy calendars on the mainframe at NASA where my Dad worked, my career in the intelligence community is how I got involved in computer technology and eventually wound up in the IT industry. In the intelligence community, we used computers for the things you would assume that they are used for: that is, to find, fix and finish bad guys. We used computers to create databases containing all that we knew (or thought we knew) about installations, communications systems, governments personalities, and weapons systems of our potential adversaries. We used all kinds of methods to find that one vital scrap of information in mountains of data, including building our own technology solutions. But even though we made progress, I always felt like it wasn’t enough.We also used computers to assist with more mundane corporate like functions such as figuring out how to manage to funds appropriated by Congress.
As I reflect back on the systems we used, I can only wish that we had the commercial off-the-shelf analytical capabilities that exist today. For example, in Desert Storm, my unit ran out …
What seems like a lifetime ago, I was intelligence officer in the Air Force. Beyond creating Snoopy calendars on the mainframe at NASA where my Dad worked, my career in the intelligence community is how I got involved in computer technology and eventually wound up in the IT industry. In the intelligence community, we used computers for the things you would assume that they are used for: that is, to find, fix and finish bad guys. We used computers to create databases containing all that we knew (or thought we knew) about installations, communications systems, governments personalities, and weapons systems of our potential adversaries. We used all kinds of methods to find that one vital scrap of information in mountains of data, including building our own technology solutions. But even though we made progress, I always felt like it wasn’t enough.We also used computers to assist with more mundane corporate like functions such as figuring out how to manage to funds appropriated by Congress.
As I reflect back on the systems we used, I can only wish that we had the commercial off-the-shelf analytical capabilities that exist today. For example, in Desert Storm, my unit ran out of a particular kind of bomb fin because the ship bringing our re-supply of bomb fins was re-routed to meet some other need. For three days or so, we dropped bombs with another type of fin that made the bombs much less accurate. Dropping bombs with the wrong fins means more missions to accomplish the desired objective and that puts aircrews and airframes at greater risk. It also increases the likelihood that we will hit something that we do not want to hit. Today, logistics systems with state-of-the-art analytics can predict this type of problem and help identify options to avoid similar occurrences.
Later, when I was at the Pentagon, I helped manage the development and deployment of IT systems that supported the intelligence community and the warfighter. Several times a year we would be asked to support congressional testimony, defend new expenditures, live with program budget cuts, etc. Lacking a system to assist us in the analysis of budget changes to our programs, we simply did the best we could to answer questions from the Hill and to manage budget impacts on our assigned programs. What I would have given for the ability to ask complex queries of a database! Unfortunately, DOD still lacks a unified budgeting and forecasting system.
In fact, most federal departments and agencies are in the same situation. As a former Secretary of the Department of Homeland Security once told me about DHS, “the department has many financial management and ERP systems, but can’t tell you how many employees it has from day-to-day!”
I’ve watched many government agencies balk at the idea of data mining and complex analytics. They are concerned about switching to a new data architecture and the potential risks involved in implementing a new solution or making a change in methodology.
Having been there, I do understand their concerns, but fear of change is what’s holding government agencies back from being able to fully leverage the data that already exists to effect change at the local, regional, state and national levels. Analytics are the key to lowering costs, increasing revenue and streamlining government programs.
In my own government experience and now, looking at it from the other side, I have come to believe that government clients need to think about data the way the world’s top corporations do. Like all federal agencies, these companies already had huge repositories of data that were never analyzed – never used to support decisions, plan strategies or take immediate actions. Once they began to treat that data as a corporate asset, they started to see real results. The best part is that leveraging these mountains of data does not require a “rip and replace” approach. Inserting a data warehousing/data mining or complex analytics capability into a SOA or cloud computing environment can be very low risk and even elegant in its implementation. The potential rewards are immense!
That’s what’s needed in the government sector. We need to view analytics not as a dirty word but as a secret weapon against fraud and other challenges impacting all areas of the government sector.
With the current healthcare discussion around delivering lower-cost health care to consumers, data mining can bring results by finding those who are taking advantage of the system. We’ve worked with the Centers for Medicare and Medicaid Services to not only recover lost revenue but also to reduce the overall cost of identifying fraud, waste and abuse.
With the economic crisis bringing state governments to their knees, data mining can help quickly recover millions of dollars in uncollected taxes. The State of Missouri, for example, recovered $2.2 million in unpaid taxes in the first 14 days its data mining solution was in place.
Both of these organizations are acting like fiscally responsible corporations. They’re working to connect the dots, uncover trends, determine cause and effect, understand risk factors and ensure compliance.
We need to talk more about how government clients can enable strategic and operational decision making in areas of interest to government clients. How do we approach financial management (think EESA/TARP, ARRA and transparency), Medicare/Medicaid fraud detection, healthcare data management, tax compliance at the state and federal level, logistics and supply chain management? How do we leverage industry best practices in a way that makes sense in government?
I look forward to sharing my thoughts and hearing your feedback on these subjects. And if there’s something you’d like to see me address in a future post, please let me know.
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