Poor Quality Data Leads to Poor Quality Information

Poor Quality Data Leads to Poor Quality Information

For us, data is the most vital asset to a business. For some, the topic is hardly riveting. When you’re trying to bring about change and improve the management of data, you might hit a barrier when your colleagues simply switch off.

If data quality initiatives are to be successful, you’re going to need to bring about real culture change. How can you engage other people who don’t feel quite as passionate about the subject as you?

Apathy or Denial?

Apathy is certainly part of the problem. Businesses are used to ignoring data. Why break the habit of a lifetime? The phrase ‘data warehouse’ is a handy tool to make people think data is shut away. It’s on a hard drive, in a data centre. It’s none of our business. It’s not our job to care.

The second factor is denial. Data is valuable, and it takes time and money to maintain its value over time. In an era of constrained budgets and a lack of available credit, businesses like to pretend they have more important things to spend money on.

Active Data Management

Clean data is essential if you’re to achieve your business goals. Getting great ROI in marketing, meeting sales targets and delivery excellent customer support; these critical objectives are all linked by the quality of data. Equally, all will fail if data is not prioritised as it should be.

In order to improve data, the business must attack its data quality problem at the root. Using the SPIN method, we can break the problem down quite simply:

  • Situation: What are the root causes of your flawed data?
  • Problem: How are the data flaws you’ve identified causing issues in the business?
  • Implications: How are those issues affecting employees?
  • Needs: What benefits would employees see if the data was cleansed?

A Chief Data Offer can act as a vital conduit for culture change, as they are in a unique position of bringing concerns about data to a wider audience in the business. They can work with web developers to design better data capture processes, and they can engage with front line staff to find out why data entry is creating duplicate records.

They can also measure data problems against organisation-wide KPIs and feed back to senior management to obtain that crucial boardroom buy-in.


Poor quality data leads to poor quality information. There is no way around it. If your data is failing the business, the domino effect is clear: waste, cost and dissatisfaction that cause havoc with profitability and productivity.

If your data is going to act as a foundation, it must be clean and reliable, and you need cooperation from your colleagues to bring about positive change.

To purify and cleanse your data, you can hire a CDO, use data quality software or automate crucial processes. Most businesses combine all three approaches. By tackling the problem at its root, and rolling out a structured data management plan, the whole business will begin to understand why the business can’t ignore its data quality problems any longer.

Article written by Martin Doyle
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