Bloomberg Government regularly publishes insights, opinion and best practices from our community of senior leaders and decision-makers. This column is written by consultant Robin Camarote, part of her series exploring changes to the management consulting business model.
Data is everywhere and, for many, is everything. It’s put on a pedestal where it is both loved and admired. It’s protected and cared for by smart, devoted people. And, it’s both the question and the answer when we cross paths with the OMB or Congress.
I started to write that federal programs “these days” have become synonymous with massive data collection, analysis and reporting exercises, but we all know that’s not 100 percent true. For as long as we have been a nation, we (the public) have demanded from our government (and our contractors) a thorough accounting for the money spent and the accomplishments achieved. In today’s government, that means data and that’s all good.
The issue? A little taste for good data leads to cravings for more. Without realizing it, we’ve become zombies with insatiable appetites for fleshy spreadsheets. That’s not so good.
Why? Data is expensive—in fact, far more expensive than we like to acknowledge. In government and in contracting, data is the fancy, bubbly, bottled water that we treat like tap.
We rarely talk about the cost of data because we believe data makes us smarter and better organizations. Federal executives want insights and answers—so they ask for more analysis. Program managers with their staffs and consultants want to be responsive and to show their program’s value—so they collect data to power such analysis and start crunching. They might wince a little in the process of getting there, but ultimately they move heaven and earth to produce polished, data-rich reports.
Yet for the dozens of well-purposed, well-intentioned federal programs, such as the Federal Real Property Profile (FRPP), the Employer Information Report, the Energy Review, and federal information technology (IT) investments, data requests like these are not making them smarter or better. They are merely creating a data collection burden on both federal employees and their contractors.
Each of these programs (and the many others like them) has a purpose. And all the data they produce has potential value. But how much did it cost to obtain it? Where is that data now? And who is using it?
The FRPP exemplifies a good idea gone wrong. If you’re not familiar with this program, the FRPP is a skim of a federal agency’s real property data and metrics. Agency staff collect the data and send it to the GSA. Using that submitted data, the GSA then produces an annual summary report of the federal government’s footprint.
And then not much else happens.
Now many important things are happening in real property at each of the agencies that reports data—but the FRPP data at the GSA is not reflective of that activity. In fact, the rules around FRPP data make it too hard to easily snag data from other systems and too superficial to do much interesting, useful analysis with it on its own. Yet agencies still bear the cost of gathering the required data, checking it, and submitting it for no return on their data collection investment—except for a check in the compliance box.
In their book, the “Agile Culture,” Pollyanna Pixton, Paul Gibson, and Niel Nickolaisen advise, “Always ensure that the cost of collecting the metric is significantly less than the value that it can deliver. And we do mean significantly less.” Recognize that the cost of collecting data is not zero and can sometimes be very high, especially if it involves continuous action by the delivery team members. This cost is often ignored and can have a huge negative impact on team productivity.
Even good programs can grow increasingly expensive because we need—and want—to understand broad, complex problems. There is no doubt that our pursuit of data and answers has yielded some insights, avoided some crises and enabled some right choices being made the first time. Yet is it right, or even sustainable, to pursue data without periodically asking ourselves how much it is going to cost to obtain the data?
How do we think the data we seek is going to answer an unanswered question? Is the tradeoff between the expense to collect the data and insight gained worth it? If the answer to this quick “gut check” is yes, then by all means, pursue that data with purpose and seriousness. However, if the answer is less than a resounding “yes” or we’re struggling year after year to ensure collection compliance, maybe it’s time to admit that the data isn’t needed or as valuable as once thought. Seeking data at all costs isn’t a wise (or sustainable from a political or budgetary perspective) approach.
The alternative is to treat data like other investments and to measure its return on investment. Keep collection and refinement efforts in check by continuously weighing the costs and benefits.
Previous entries in this series:
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