Managing Data Integrity

When was the last time you looked at a view of data, report, or graph in CRM and said to yourself, “This doesn’t look right”? You’re not alone. Keeping data up-to-date is a common issue for many organizations. We rely on its accuracy for decision making. An example of decision-making from data is determining which resource to assign to a project. If the project pipeline is inaccurate, a more senior resource might get tied up in a smaller project when their skillset would have been better used on a more important project. Another example might be deciding to make an investment based on erroneous forecasts of that investment’s future.

When data is out-of-date and you recognize this, the risk of an inaccurate decision is diminished as you have the opportunity to contact the data owner(s) to get an update. When it goes unnoticed, the risk of bad decisions increases. While there are many reasons why data can get out of date, there is often one common root cause: the person responsible for entering the data did so incorrectly or failed to do so. Rather than demonizing a person, we can look to find ways to make it easier for the data to be kept up to date.

There are many factors that go into data integrity:

Does the responsible party for the data entry also own the information gathering mechanism?

This can manifest when there is a team assigned to a record or there is a disconnect and/or lag in the data gathering process. For example, if there is a government agency that only provides updates periodically, but management needs information more frequently, this can present a problem. Possible solutions:

  • One record – one owner. No team ownership of a record.
  • Talk with management about the data they want and the source if outside the direct control of the responsible party. Have an open dialogue if the data gathering mechanism is flawed or doesn’t meet the needs of management to decide on a best course of action.

Does data have to be kept up-to-date real time or can it be done periodically?

Not all decisions have to be made ad-hoc. Some decisions can be deferred, occurring weekly or monthly. It is important that an organization examine the risk associated with each data element. Those that supply data feeding high-risk areas or decisions needing to be made more often need updates frequently from their data owners. Those with less risk or are used less-often can have less emphasis on being kept up to date. Remember, at the end of the day, a person, somewhere, had to provide that data. As individuals, no one is perfect and it is unreasonable to expect perfection on every record, every field, every time. Prioritize!

Can data be automated?

There are many tools available that can be added on to your software that automates data gathering. There are many companies that have created tools that, for example, go out to the web and pull in data updates related to a search topic. Consider installing or developing such tools where appropriate. This will reduce the need for a person in your organization to be assigned to this task. It will save time and money!

Consider using a tool’s workflow or a manually created workflow to help remind data owners to make updates.

Many data tools have built in workflows. These can be used to set tasks or send an email periodically for data owners reminding them to update a record. An example might be to create a field called “Last update” which should be changed each time a person reviews the record to make updates to important fields. If this data is more than a week old, an email can be sent to the data owner. Where such tools are not available in the tool, one could use their email application to have a reoccurring task or calendar item to remind them. At last resort, a sticky note on a physical calendar can do the trick!

Data is the life-blood of an organization. Keeping it up-to-date is important for decision making affecting both small and big outcomes. Most data comes from people. Help your people by setting up reasonable, sound business practices and processes around data integrity. It won’t prevent erroneous data, but you’ll find less of it and will make you and your data owner’s work-lives much easier. For a case study about how Edgewater has followed these practices, click here for more information.

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