So, first things first. What is data quality? In it’s simplest form data quality is the measure of the condition of your data within a CRM, based on consistency, completeness, validity, relevancy, timeliness and whether it’s accurate and up to date. By doing this it makes sure quality levels are being measured frequently and can therefore help organizations identify any errors that need to be resolved quickly and run checks to see whether the data is fit to serve its intended purpose.
Data is a hot topic right now. Click here to find out about our market leading data quality tool.
All marketing agencies want to talk about data insights and the value that they can derive from this data. There’s a good reason for that. In modern times data is one of the most valuable resources available for marketers, agencies, publishers, media companies and more.
Data is only ever data, unless it’s high quality, accurate and brought to life in the right way. IBM estimates that bad data costs the U.S. economy $3.1 trillion per year. Those costs come from the salaries businesses must spend correcting bad data and errors that cause mistakes with customers as well as loss of revenue from loyal customers.
Improving the quality of your data is a big opportunity.
Factors which contribute to the quality of data broken down:
Accuracy – Accuracy refers to how well the data describes the real-world conditions it aims to describe. Inaccurate data creates clear problems when it comes to decision making, marketing campaigns and relationship building with customers.
Completeness – If data is complete, there are no gaps in it. Everything that was supposed to be collected was successfully collected. Gaps in data can lead to customers not being contacted or the wrong customer being contacted. If your data is incomplete, you might have trouble gathering accurate insights from it and therefore make wrong decisions.
Relevancy – The data you collect should also be useful for the campaigns and initiatives you plan to use it for. There is no point in having all of this data if it’s not relevant to your goals.
Validity – This refers to how the data is collected, as data is only ever valid if it is in the right format. If your business data does not meet this criteria, you will have trouble using it for any business function.
Timeliness – Data typically becomes less quality and less accurate as it gets older. The best way to prevent this is to use data that reflects the current reality.
Consistency – Data should be consistent in its content and format. If it isn’t consistent, groups may be operating under assumptions and therefore your different business functions may not be well coordinated.
Why is data quality management important?
Many businesses nowadays are using data more and more to make decisions across the company. As more people reap the benefits good data quality control and increase their businesses ROI using it, in industries it is now becoming a matter of keeping up with the competition. Companies that don’t have data quality assurance are at risk losing the race and falling behind their competition.
The better your data’s quality, the more you can get out of it.
Many of today’s businesses integrate data quality tools into all of their integral strategies. This increased integration means that data quality can impact many aspects of a business flow.
Data quality issues are also a big no no for compliance-related issues. It’s harder to demonstrate industry compliance if your data is not well maintained, this is vital for sensitive personal data.
Good data management is crucial for keeping up with your competition and taking advantage of big opportunities. High-quality data can provide various benefits including improving decision making so budget is spent wisely, better audience targeting and more effective campaigns, relationships with customers are improved, and your business will have an advantage over competitors.
To find out how Lloyd James could help you improve the quality of your data click here to get in touch.