Close the Data Gap

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In simple terms, a data gap refers to missing data about a particular area of interest. Data gaps are a problem for organizations of all sizes. They are costly, frustrating and time consuming to fix. Three broad categories can be used to classify organizational data gaps as the following:

  • Absence of data
  • Lack of access to data
  • Lack of interest in using data

Where do you start?

To close a data gap, the nature of the gap must be determined. The first step is to identify what information is missing and why is it crucial? Once this data gap is identified, the next step is to determine the root cause of the gap in order to evaluate the most effective way to close it. There is a common misconception that all data gaps can be solved by simply collecting additional data. Cultural and governance aspects of data should not be overlooked to address the issue of data gap.  

The three types of data gaps and how to close them?

There are many reasons why data gaps may exist. Let’s deep dive into each type to determine a way to address it.

Absence of data

This can be further broken down as intentional and unintentional. Intentional data gaps are those that are created on purpose, such as the organization made a conscious decision not to collect certain types of data. For example, an organization might choose not to collect data about personal or financial information to protect the privacy of its users.

Unintentional data gaps are those that occur as a result of events outside the control of the organization or as a result of oversights or mistakes. For example, an organization might experience an unintentional data gap if systems in place for collecting data accidentally collect incomplete or inaccurate data. Unintentional data gaps can have significant negative impacts on an organization, as they can prevent us from making informed decisions.

There are a variety of tools and techniques that can be used to close data gaps.

Data Collection: One way to close a data gap is to start collecting new data. This may involve conducting surveys, social media monitoring, or online transactions to gather the necessary data.

Augment with external data: Look for external data sources that could potentially help fill the gap. These might include government open source, non-profit organizations, industry specific private companies, and online databases like world bank or crowdsourced data. Depending on the size and complexity of the gap, you may need to use imputation and interpolation to fill missing values.

It is also important to validate the quality of the external data, as the accuracy of your results will depend on the accuracy of the data you are using. If the external data is of poor quality or unreliable, it may not be suitable for filling your organization data gap.

Lack of access to data

Data governance is an effective way to close your data gap. If you are unable to access the data required, it will not be possible to get value from the data you collect.

Implementing effective data governance can help close data gaps by ensuring that the organizational data is of high quality, properly documented, and right access is granted to the people needing it. It also helps with confidence in the data, as it highlights how data is collected, and how it is to be stored and protected. A solid data governance in place ensures that everyone in the organization has access to the right information, at the right time.

Lack of interest in using data

One of the toughest impediments to closing a data gap is addressed by fostering organizational data culture. Investing in tech solutions to collect and analyze data will not produce value if people do not want to use data. Culture change is complex and requires persistent effort to incorporate data in day-to-day activities. Every organization has data skeptics who question the value of data and favor leveraging their legacy knowledge over data. Culture does not change overnight and requires small incremental steps.

Drive organizational data culture with the following fundamental elements:

Learn – Foster an environment where people can ask questions and are open to learning new skills.

Share – Build a mindset of data sharing and invest in data sharing and collaboration tools. Interest and Reward – Make data interesting, easy to understand using visualization tools, and provide incentive to use data.

Innovation and Agility – Encourage self-service analytics to enable organizations to be more responsive to changing circumstances and adapt quickly to changes. Organizations can increase innovation by users performing their own data analysis and independent research, leading to new perspectives.

Collaborate – In a successful data-driven organization, employees are encouraged to collaborate on projects, share insights with one another, and communicate openly about their findings. By encouraging interaction between teams and departments, organizations can break down silos and work together more efficiently to solve problems.

Organizations can effectively bridge data gaps and make better data-based decisions by using a variety of these tools and strategies.

About the Author

In this contributed article,

Sangeeta Krishnan is an engaging business intelligence and analytics leader who possesses a winning blend of subject-matter expertise and practical experience from a variety of industries. Most recently, she joined Bayer as North American Analytics Lead for mass sales. She has worked with Fortune 500 organizations, not-for-profits, and everything in between, helping various organizations build their operations and monetizing data products from the ground up. Krishnan is a public speaker, content creator having articles published in industry journals, and was recognized as a Finalist of the Women in IT Awards 2018 (USA) in the Data Leader of the Year category. She is the author of Thriving in a Data World (Business Expert Press, December 2022). 

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