• Malcolm Tutt

Common Data Management Mistakes

Common Data Management Mistakes

Data is expanding at rates never before thought of, and the management of that data has to grow just as quickly.

The first realization is that data is an asset, not physical but digital; it is just as, or maybe even more, as important as your network or your telephone system and needs the same rigor applied to maintain and safeguard it.

Businesses can avoid a few common mistakes when managing their data.

Ensuring a fit-for-purpose data governance framework is in place.

Not appointing a data officer or, as a minimum setting a governing body in the company is a mistake.

The framework should identify data classification, data life, data-in-transit, access controls and policies, backup and recovery, and more.

Data lives; it is born and ultimately expires – managing the data life cycle is critical.

Not understanding data design and architecture.

Not having clear policies regarding how and where data is stored is a recipe for disaster. Many companies are blissfully unaware of data copies that have been made – how many times is data copied into a spreadsheet used a few times and then forgotten? Did that spreadsheet with all that personal information get disposed of?

Did you know that an email attachment I a mirror copy of the document? So, when you send that spreadsheet to a colleague, you are creating another copy.

Not getting a handle on data quality.

Users are often disappointed with data supplied and will, in many cases, build their own data and store those copies of data on their local machines or other storage.


This data is out-of-sight of the governing framework and policies. It is a mistake to ignore users when they complain about the quality of the information they are getting; it is a mistake to allow users to continue with their own datasets without understanding the risk and, worse, the breach in compliance.


Ignoring data silos.

Think of departments in a company as silos, and each has its objectives, measurements and more in place. Each department may have its’ own view of, for example, the customer. Each department may need different information about the customer – but what to do if that data is different – even subtlety?

Not expending the effort to ensure data alignment, the accuracy of data, the time relevance of the data across departments is a mistake.


Focusing on compliance only.

Companies seeking to achieve legislative data governance to appease stakeholders and regulators’ requirements are almost certain to use shortcuts. In most cases, this approach will result in the company looking to IT to satisfy the governance requirements. But, in truth, compliance is a business-wide requirement – from the CEO to the Technician in the server room.


The mistake is not involving the entire organization and driving data management from the top down.

Boiling the ocean.


Data management can be daunting and, at times, downright confusing. So breaking data management down into deliverables is essential, but more important is to avoid attempting to resolve all the companies data management problems in one go. As such, the biggest mistake is not agreeing that data management is an ongoing process.