asking questions of data (Production Perig/


4 factors that make or break a data strategy

Using data to best support an agency’s mission means creating and deploying a data analytics strategy focused on the ultimate destination for that data -- the decision-makers who rely on it. In order to achieve this objective, there are four critically important factors to consider:

1. Design around the driver: It is vital to analyze data with the end user in mind. The decision-makers who depend on the insights derived from data analytics are the most important element of any successful program. All too often, however, the end user is considered only in terms of point of delivery rather than from the very start of the project planning process. Clearly defining how, when, why and in what form decision-makers need the information the program generates will give immediate and tangible metrics to the end results and, ultimately, enhance support of the agency's mission.

2. Know how to navigate the data: With the volume of data increasing every day, knowing how to streamline and navigate datasets will help increase the effectiveness and overall outcome of data analysis. As the quantity of data continues to rapidly grow, even greater insights are possible -- but only with the right navigation plan. Understanding where the data is from, what the inherent flaws are, what the holes or risks are in using it and how it is being processed by data scientists and engineers are all key in delivering effective data interpretations.

3. Gain powerful traction with the latest technologies and skills: Flexibility is another critical component to analyzing data efficiently because a data strategy rarely goes according to plan. Instead, it is common at the end of the data analysis process for data scientists and engineers to make discoveries and identify new relationships they didn’t anticipate needing to leverage during the planning stage. Without the right tools, it would be nearly impossible to go back and modify or create new models based on this newfound information, and critical insights would be lost.

Now, consider the most relevant technologies that can maximize value to both analysis and the delivery of insights to the end user. All these innovations, including artificial intelligence, machine learning and cloud technologies, come with challenges and technical nuances that require certain skill sets to guarantee proper execution and ensure they actually improve an agency's data analytic capabilities. Combining the right tools and skill sets enhances the ability to accurately interpret the data and allows it to be packaged so the end user gains timely, maximum value.

4. Remember the user experience: Lastly, incorporating the human element is essential to delivering successful intelligence to the end user. The shape and form of the data presented and the way the end user interacts and receives the data are key when looking to evolve with the ever-changing data landscape. Keep in mind, not everyone interprets data the same way, so it is imperative to have a common understanding of what audience will be deciphering this data, and the most important points that need to be communicated to the end user in a way that is easily understood and purposeful.

About the Author

Gordon Nelson is the chief data innovation advisor at Hitachi Vantara Federal.


  • Records management: Look beyond the NARA mandates

    Pandemic tests electronic records management

    Between the rush enable more virtual collaboration, stalled digitization of archived records and managing records that reside in datasets, records management executives are sorting through new challenges.

  • boy learning at home (Travelpixs/

    Tucson’s community wireless bridges the digital divide

    The city built cell sites at government-owned facilities such as fire departments and libraries that were already connected to Tucson’s existing fiber backbone.

Stay Connected