Dennis D. McDonald (ddmcd@outlook.com) is an independent consultant located in Alexandria Virginia. His services and capabilities are described here. Application areas include project, program, and data management; market assessment, digital strategy, and program planning; change and content management; social media; and, technology adoption. Follow him on Google+. He also publishes on CTOvision.com and aNewDomain.

Just The Facts, Ma’am: The Case For Data Literacy -- and Federal Data

Just The Facts, Ma’am: The Case For Data Literacy -- and Federal Data

By Dennis D. McDonald

Fear and uncertainty are powerful motivators. So it is for those who have over the past 8 years devoted untold hours to the promotion of government transparency and “open data” programs. 

Given the recent election outcome, what will happen to the programs that were set up to make previously “hidden” Government data sets available to the public? What will happen to the standard-setting and data stewardship programs that prepare data for public consumption? What will happen to innovative data research and analysis programs designed to improve medical care funded by the Federal government programs?

I don't know the answers to these questions. There’s always a lot of uncertainty when Administrations change. 

I do suspect that Federally sponsored statistical, data access, and data analysis programs that don't have direct ties to legislation and program mission statements will be among the first to experience scrutiny, starting with a review of how much these programs cost the taxpayer.

Here are my biases:

  •  The public has a right to know how tax money is being spent and to what effect. 
  • Many "open data" efforts have not made a convincing link with the impacts their sponsoring programs or missions are supposed to be supporting.

Here are some comments and suggestions based on my own consulting and research:

  1. Data access programs should be designed from the ground up with usage metrics and impact measures in mind. If that means that money needs to be shifted from data preparation to performance and measurement, so be it. Better to have a few clearly meaningful data sets available with reliable usage and impact data than many more files that are being made accessible because they're easy to publish.
  2. Government-sponsored data access programs need top-down support for provision of real-time and human engagement between government staff and the users and intermediaries who really interact with the data. Don't just toss the data out there, be prepared to explain what the data mean. Data access should not be treated as a PR function but as a service function that is directly integrated with how the government does its job, not something that is "added on" as a separately managed operation.
  3. Given the current anti-government atmosphere, the private sector must speak up about the value of government collected statistics on unemployment, productivity, prices, and educational performance. We saw attacks in the recent national campaign on the trustworthiness of government statistics. "Big business" – as well as nonprofits and the research community -- must demand government accountability and the gathering and publishing of authoritative performance data that reflect the actual state of our economy as well as the performance of the programs we’re funding. Otherwise we're flying blind. 
  4. Rising professionals with an aptitude for and understanding of data science and statistics must take some responsibility for explaining what data mean to the public, just as many younger scientists feel some responsibility for making sure the public understands what research is telling them. Having spent almost a decade as a “number cruncher” myself I personally understand the joys and fascination of data analysis, modeling, and interpretation. As more and more data are gathered and analyzed, though, we need to make sure the public understands and appreciate the basics of data and data analysis. Everyone needs to know what questions to ask when numbers start getting thrown around.

I'm not suggesting that everyone become a “data scientist.” I am suggesting that basic data literacy is becoming  just as important as learning to read and write.

After all, if we really think, for example, that we can bring those manufacturing jobs back to the Midwest and the Rust Belt, how will we know in four years if we’re being successful? And how do we convince people that the numbers are reliable?

Copyright © 2016 by Dennis D. McDonald

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