Maintaining Data Integrity in an Enterprise

Maintaining Data Integrity in an Enterprise
By Keith Wertsching | June 21, 2019

Everyone suffers when an enterprise does not maintain the integrity of its data and the leaders employ that data to make important decisions for the enterprise. There are many roles involved in mitigating the risk of poor data integrity, which is defined by Digital Guardian as “the accuracy and consistency (validity) of data over its lifecycle.” But who should be responsible for making sure that the integrity of the data is preserved throughout collection, extraction, and use by the data consumers?
The agent who maintains data accuracy should ideally be someone who:

  • Understands where the data is collected from and how it is collected
  • Understands where and how the data is stored
  • Understands who is accessing the data and how they are accessing it
  • Has the ability to recognize when that data is not accurate and understands the steps required to correct it

Too often, the person responsible for maintaining data integrity is focused primarily on the second bullet point, with a casual understanding of the first and third bullet points. Take this job description for a data integrity analyst from Investopedia:
“The primary responsibility of a data integrity analyst is to manage a company’s computer data by way of monitoring its security…the data integrity analyst tracks records indicating who is accessing what information held by company computer systems at specific times.”

The job description demonstrates that someone working in data integrity should be an expert on where and how the data is stored, and be familiar with who should be accessing that information in order to make sure that company data is not stolen or used inappropriately. But who is ultimately responsible for making sure that the information is accurate in the first place, and for making sure that any changes needed are done in a timely fashion and tracked for future records?

In today’s world of enterprise database administrators, there is often a distinct separation between the person or team that understands how the data is stored and maintained and the person or team that has the ability to recognize when the data is not accurate. Let’s take the example of a configuration management database (CMDB) to highlight the potential issues from separation of data integrity responsibility. SearchDataCenter defines a CMDB as “a database that contains all relevant information about the hardware and software components used in an organization’s IT services and the relationships between those components.” The information stored in the CMDB is important because it allows the entire organization to refer to technical components in the same manner. In a larger organization, the team that is responsible for provisioning hardware and software components will often be responsible for also making sure that any information related to newly provisioned components makes its way into the CMDB. There is often an administrator or set of administrators that will maintain the information in the CMDB. The data will then be consumed by a large number of teams, including IT Support, Project Teams, and Finance.

When the accuracy of the data is not complete, the teams consuming the data do not have the ability to speak the same language regarding IT components. The Finance Team may allocate dollars based on the number of components or breakdown of types of components. If they do not have adequate information, they may fail to allocate the right budget for the project teams to complete their work on time. A different understanding of enterprise components may cause delays in assistance from the IT Support organization, which has the potential to push out timelines and delay projects.

One potential solution to this issue: make one team responsible for maintaining the accuracy of the data from collection to consumption. As mentioned before, this team needs to have an understanding of where the data comes from, how it is stored, how it is consumed, and the ability to recognize when the data is not accurate and the steps required to correct the information. The data integrity team must be accessible to the rest of the organization to correct data accuracy problems when they arise. As the team grows and matures, they should target developing proactive measures to test that data is accurate and complete so that they can solve data integrity issues before they impact the user. By assigning specific ownership over the entire data lifecycle to one team, the organization can enforce accountability and integrity and mitigate the risk that leaders make poor decisions based on false information.


[1] Digital Guardian:
[2] Investopedia:
[3] SearchDataCenter:

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