Your decisions are only as strong as your data: maintaining data quality for better outcomes

09 Jun, 2023

While it’s often said “data is an asset”, many organisations are unaware that their data management processes aren’t as robust as they could or should be – and the quality of their data is compromised. Poorly managed data not only leaves your organisation open to severe financial and reputational risk in the case of a security breach, it also means you are trying to make business decisions with an incomplete data reference.

As 9Yards Information Security Consultant Paul Yeardley says, “If you don’t have good data, you’ve got to spend a lot more time trying to work out what things mean, which affects your efficiency and overall impact. Your greater business isn’t going to achieve the outcomes you potentially need. If you get something right, it’s more by accident than it is by design of having good data.”

“Because data is everything,” he says, “if you don’t have the right data, you can’t make the right decisions.”

So what do organisations need to prioritise to ensure high quality data – and informed business decisions? First, a greater understanding of the significance of data quality, and second, the right policies and processes to maintain it.

The significance of data quality

“The biggest gap we see in data management is that it’s often an afterthought,” says Paul. “Organisations don’t think about it upfront. They start out saying ‘data is an asset’, but sometimes that ends up being lip service because across the organisation there’s not a great understanding of its significance.”

So what is data quality? It refers to the accuracy, completeness, reliability and consistency of data. It underpins the trustworthiness of your data analysis, and resulting business decisions.

The quality of your available data directly affects:

  • Decision-making: When referring to incomplete or incorrect data, you are no longer making an informed decision.
  • Risk mitigation: Inaccurate data can lead to incorrect forecasting, compliance issues and financial discrepancies.
  • Customer experience: Accurate data drives improved customer service, personalised experiences, and targeted marketing campaigns, leading to greater loyalty and brand reputation.

Planning and designing for the maintenance of quality data from the outset is the best case scenario. Educating team members, company-wide, on the role data collection plays is frequently an underestimated factor of data management.

“It’s not just a technology thing,” Paul says. “People are a very important part of data management. Truthfully, you could implement a reasonable level of data management with an Excel sheet and the right people. It wouldn’t be the ideal scenario, but you could do it.”

Conversely, where people don’t understand the importance or big picture role of the data they are collecting or entering, they are more likely to capture incompletely. “If they don’t understand the importance, they might just put “N/A” in a field to avoid an error message. And then down the line, that could have a negative impact when auditors come in, or you have regulatory requirements to follow, or it has a follow-on effect on financial decisions.”

How to ensure your organisation can maintain data quality

As it sounds, maintaining data quality is not a one-off action, but an ongoing activity. It requires a systematic and company-wide approach to ensure you are always working with the most accurate and secure data.

Maintaining data quality can involve:

  • Data governance: Define the policies, procedures and responsibilities of data management – and standardise and enforce them across the organisation.
  • Data validation: Avoid inaccuracies, inconsistencies and duplicates through regular validation. Use automated tools as well as manual checks to ensure integrity. (“One of the easiest things you can do to maintain data quality is to only use free-text fields as a last resort,” Paul says.)
  • Data integration: Human handling introduces opportunities for human error. Integration architecture reduces those risks by enabling inter-application connections that share data automatically.
  • Data cleansing: Remove irrelevant, duplicate or outdated information by running regular audits and applying standardised protocols for data cleansing.
  • Data security: Ensure data integrity and prevent unauthorised access by implementing robust data security measures and policies.

Want to review data management and quality in your organisation?

If you’re inspired to inspect the health of data and its management in your organisation, and begin planning for a stronger target state, arrange an initial consultation with our Information Security Consultants today.