September 30, 2013Doug Hadden
Doug Hadden, VP Products
Professor Matt Andrews of the Harvard Kennedy School has put out an interesting thought piece proposing a research methodology on how governments get great. He observes that "great governments are frustratingly rare and many governments that try to copy more successful examples do not see the same results."
Andrews suggests that a comparison of "Solution and leader driven change’ (sldc) and ‘Problem driven iterative adaptation’ (pdia)" be undertaken using "Theory Guided Process Tracing (TGPT)." He points out that the notion of leader driven change is explicit or implicit in government success stories. He points out that "these lessons seldom explain how such governance and leadership solutions emerged, however, which leaves many unanswered questions about the way great governments actually come about."
This is the "missing link" that mystifies observers who wonder why "best practices" often do not work in practice. I agree with Andrews that "Many problems and challenges" in government are "complex—involving multiple moving parts and interdependent players and defying technical solutions."
Organizational Change Success
I couldn't help but think of the shifts in business thinking from the days of In Search of Excellence to Good to Great. There seems to be a general trend in analyzing long-term business success to focus less on the mythical leader and more on the culture of change. Of course, popular imagination still seems to focus on the mythical and idiosyncratic leader. In some companies, the loss of the leader results in very little change in trajectory – in others – decline. The how is critical.
This is a Big Data Problem
Andrews uses a few examples of governments that have had great results. He points out the differing narratives of how the successes came to pass. My view is that analyzing "narratives" could be dangerous. Narratives are often simple and reflect myth – where the myth of success has a shred of truth. Narratives often bunch out alternative explanations. Quantity might solve any confirmation bias.
There is a general misunderstanding of what "big data" is and how it can be used. It's much more than more data. Big data analysis attempts to combine unstructured data such as documents, structured data such as statistics and activity streams. Semantic tools, neural net and statistics can be used to tie all of this together. This would enable tracking timelines of change (activity), context (documents) and results (statistics) to test the validity of SLDC and PDIA. It may also show the difference between a successful program (NASA and landing a man on the moon) or sustained economic growth (Singapore).
Big data is a potential solution for uncovering complex relationships. These tool sets could provide more insight on macro-level government practices that work and under what conditions. It is relatively easy to map a micro-level government intervention to a simple outcome. Macro-level initiatives are often driven by dogma. And, there are so many complex factors that it is rarely clear what affect it has on outcomes, positive or negative.
Let's Move Forward
Dogma is threatening to close the US Federal Government. Dogma has been used to cut foreign aid. Dogma has been used to squeeze out affluent minorities. Dogma squeezes out funding for climate change research or discussing climate change.
Many fear "big data" claiming that only subject-matter experts could possibly make any use of it. Yet, it is high time to break some of the myths – or, at least, show the applicability of any policy idea. In this case, it is even more critical to show how to effectively implement a policy idea. As Andrews points out: "The question is whether one can get past policy-specific answers and peculiar, situation-specific explanations for the outstanding achievements in these cases."
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