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A Primer on Public Sector Big Data

Today’s blog is from Don Pagel, VP Public Sector Services at Kronos.

Big Data, Data Warehousing, Data Marts, Analytics, Business Intelligence….all are part of the same evolution of the growing digital world, the data it generates and the structured information contained therein.

 

As computer systems began to house databases of transactional data, users and executives have asked for more and more “reporting” from those systems to investigate problems, maximize efficiencies within processes, and my favorite in the public sector, or determine solutions or answers based on factual information rather than supposition or anecdotal experiences. In recent years the data generated by all of the systems we use and the potential interrelated information from disparate systems has generated voluminous amounts of data…thus “Big Data”.

 

The public sector is beginning to use analytical tools to mine all of the data they have to inform their constituents, look for efficiencies and also to help off-set some political “agendas” by focusing on facts. Public safety organizations use collected data to look for geographical or date-specific trends to help staff for the best use of safety manpower as well as educate the public on what is going on in their areas. Human resource departments regularly extract and update public pay and benefits data to reduce demand on their staff for public information requests. Executives are finding value in workforce management data to determine labor productivity and improve resource and budget management. Finance departments are unifying all of their collections data into one analytical format from disparate systems in order to gage how well they are collecting outstanding fines and fees, develop metrics to monitor and look for ways to enhance processes to improve collections. Parking and Police departments are collaborating on vast amounts of data collected by license plate recognition equipment and software to share information that can be valuable to both organizations. Parking departments are also learning the value of parking transaction data for both numerous supply and demand calculations as well as justifying new garage or meter placements. Traffic managers have long used data to determine traffic patterns but now can use that same data to automate lane use or determine where the next road work or expansion is necessary.

 

Putting some thought into structuring data within a larger organization that may contain multiple systems can lead to an easier delivery of many of the above ideas, allow for “drill-down” structured online reporting as well as simpler ad-hoc querying to more easily find true factual information in a political environment. This is not generally an inexpensive project, but it can pay off handsomely for years to come. If your organization hasn’t started a Big Data project, I suggest you consider the following:

 

  • Take an assessment of all of the different important systems you have and determine their purpose.
  • What data is being stored on each system?
  • Are their proprietary analytic programs already on a system that can be used to export data to a larger environment?
  • Is there an opportunity in an external database to link these disparate data sets together? Over time, you may initiate some data normalization of different systems in order to link data easier.
  • Now that you have a data storage strategy for your important data, what are your current pain points that you most want to solve with data? Start small or start focused on just those pain points. There will be a tendency to over-develop analytics that may or may not be useful. Since you spent time up front on your data storage strategy, you can build them as needed so they have the greatest impact and potential use by your organizations.
  • Develop a plan to use the analytical tools developed in a structured way or using a process. In other words, don’t just make them available, develop a process or policy for their use.
  • Use dashboards that are visible to wide audiences so that there is a natural draw to the information as well as a natural desire to improve the results. Visibility can create powerful competition.
  • Develop education around the data being displayed. Don’t assume that others will know what the data is saying or how to use it to improve results.

 

In short, Big Data is collecting the vast amounts of data we have and putting it to good public use. You have the data, it’s a shame for it to go to waste

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