5 Simple Tips for Improving Data Quality Smartly
Data is king in today’s digital world, powering almost all aspects of customer interactions and operational procedures. Technology has increased performance tremendously, but it has also led to a huge reliance on data, which is a double-edged sword. A data-driven company does more than just gather data; it also extracts insights that are useful and implementable. However, according to the Harvard Business Review, inadequate data quality can cost businesses an astounding $9.7 million a year, with integrity problems affecting almost half of all data sets.
According to Gartner, this equates to a $15 million average financial hit per organization. The effects of poor data quality are extensive, ranging from distorted insights to resource waste and damaged reputations. It makes sense that improving data quality has elevated to the top of many CIOs’ and CMOs’ priorities.
Addressing problems with data quality, however, can be a difficult task, particularly when client data is dispersed over multiple organizational systems. We explore Acrotrend’s methodology for inspecting data quality and putting practical solutions into practice to address the underlying causes of low-quality data in this blog.
Unlocking the Power of Your Quality Data
Collecting data is just the tip of the iceberg when it comes to building a fully data-driven organization. Finding useful, practical insights into such data is the true problem. Companies could lose a staggering $9.7 million a year due to inadequate data, therefore it’s critical to maintain strict quality standards in addition to gathering a lot of data.
Here are five innovative methods to improve the quality of your data and realize its full value:
- Encourage everyone in the organization to have a common understanding of data quality.
Make sure that everyone understands what high-quality data is and how to define it, from directors to developers. - Put in place strict data governance guidelines to make sure they meet organizational goals.
It is imperative to cultivate a culture of data awareness in addition to technology. To manage data efficiently, make a substantial investment in data governance techniques, which include standards, procedures, and rules. These tactics guarantee that staff members follow defined criteria for data quality. - Although everyone recognizes the importance of data analysts, who makes sure they get high-quality data?
This duty is borne by data stewards. Data stewards supervise the quality of data while working closely with analysts. To maintain quality standards, they use a variety of tools and software, including data cleansing solutions, to quickly identify and fix any problems that may arise. - Find data inaccuracies at the source.
Don’t just erase mistakes that occur. Examine their causes to find out why things happened. Employ profiling-tool-equipped software solutions to automate this procedure, effectively analyzing data pieces and pinpointing the sources of problems. - Avoid mistakes in the future. It’s critical to put policies in place to stop similar errors from happening again after determining and fixing the error’s cause. Although data profiling aids in identifying the problem, proactive measures are required to protect company data from corruption that recurs.
Conclusion
Using a data verification tool is crucial for managing consumer data. This utility eliminates duplicate entries by guaranteeing your database's names, addresses, phone numbers, and email addresses are accurate, current, and formatted correctly. Utilizing poor-quality data damages your reputation, impairs customer service, and harms productivity and profit margins. According to research, it costs $1 to keep faulty data out of your database, $10 to fix it, and $100 to make judgments based on it. This emphasizes how important it is to deal with data quality issues immediately.
