This article originally appeared on the BeyeNETWORK
Well, Congress finally passed the stimulus passage, and there are many lessons to be learned from the process. A few weeks before the final statute was enacted, the Washington Post editorial page featured two back-to-back columns that caught my attention fully. They were opinion pieces written by noted Washington insiders, Martin Feldstein and George Will. The former is President Emeritus of the National Bureau of Economic Research and a former Chair of the Council of Economic Advisers; the latter a well known public policy pundit. Both cried out loudly, though in different ways, for rigorous and methodical analysis to be done on the stimulus package. What was needed to get it right, they both argued, was business intelligence. Not that they specifically called out for business intelligence by last name, first name and middle initial, but in effect that was the gist of their message.
It wasn’t just these editorials. Congressional testimonies and officials addressing the press in talk show after talk show seemed to agree on one point. Either there wasn’t enough data or we could not collect it in time or we were not able to analyze the data appropriately to be able to say with a reasonable amount of certainty what the impact of the package was going to be.
Feldstein called it “An $800 Billion Mistake,” while Wills’ piece headlined “Stimulus Math for the GOP.” In both cases, they documented the obvious: we just don’t know what the effects are going to be of the close to $1 trillion package. Wills even affirmed that “economic policymaking in turbulent times is a science of single instances, meaning no science at all.”
These are blunt and straightforward words. There are always going to be questions on the impact that any government program is going to have on the economy. There are multiple economic models that different enterprises in the public and private sector, as well as academia, use to try to simulate the U.S. economy with greater or lesser claims of success. The point Wills makes is that models have significant limits, mainly because they are based on history; and when we seem to get into real trouble is when something happens that we did not expect – the “black swan” postulated by Nassim Nicholas Taleb. Of course, there is always the real risk of what “we don’t know we don’t know.”
While this is certainly true, the fact is that we can predict certain economic behavior reasonably for certain periods of time; and as long as we effectively manage the risk inherent in “what we don’t know we don’t know,” this is better than no models at all. The mandate is to support our decision making as best we can; and that leads us right back to business intelligence.
And it is all about making decisions. We’ve all been exposed to the debates of the last few decades over whether tax cuts have a positive effect, or whether we can quantify what the impact is going to be and when it will actually take place.
We have also been told by some proponents that the relief money should go directly to the homeowners and not to the banks. We’ve heard both sides of the debate over whether to “bail out” the Detroit automobile manufacturers or not, or whether we need to fund a massive aeolic power – windmills – initiative.
Decisions, decisions, decisions. We’ve made the point again and again in my articles on the BeyeNETWORK over the years that our country’s public policy is established by making decisions on national priorities based on what the majority of people believe is more or less important in their lives. Generally that gets accomplished by voting for representatives that promise to support our same, or very similar, priorities on the issues that matter to us.
Then we need to make decisions on what initiatives we are going to enact and how much funding to allocate to each one. This gets done in Congress through the authorization and appropriation processes. While the Congressional Budget Office (CBO) tends to do a good job of analyzing a lot of data, it focuses on what members ask it to do; hence, there is a lot of analysis that just isn’t done.
Furthermore, in the most powerful and technologically advanced nation in the world, we should have the most precise, dynamic and best business intelligence resources available. We need them to periodically test and calibrate the economic models that we use to answer some of the questions that Feldstein, Wills and their many other colleagues will continue to formulate. Furthermore, we will need robust business intelligence to assist in the much more mundane process of helping the legislature make the right decisions on what, how and when it is going to spend the taxpayers’ money – our money – and assisting the Executive Branch to implement and operate these initiatives in the most effective and efficient way. That is the least we should expect, as citizens, from our civil servants. But it is also an opportunity for those of us in the business intelligence community to contribute in the process of helping to recover during these tough times.
Dr. Barquin is the President of Barquin International, a consulting firm, since 1994. He specializes in developing information systems strategies, particularly data warehousing, customer relationship management, business intelligence and knowledge management, for public and private sector enterprises. He has consulted for the U.S. Military, many government agencies and international governments and corporations.
Dr. Barquin is a member of the E-Gov (Electronic Government) Advisory Board, and chair of its knowledge management conference series; member of the Digital Government Institute Advisory Board; and has been the Program Chair for E-Government and Knowledge Management programs at the Brookings Institution. He was also the co-founder and first president of The Data Warehousing Institute, and president of the Computer Ethics Institute. His PhD is from MIT. Dr. Barquin can be reached at firstname.lastname@example.org.
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