5 Tips for Improving Data Quality and Consistency

Have you been thinking about improving data quality?

There’s no question about it: data quality matters. Your data doesn’t just exist for the sake of existing: it’s crucial for the success of your organization. The data can help put you above the competition by informing your business decisions regarding marketing, operations, and more. It doesn’t just exist for the sake of analytics: it can really help your company.

However, high-quality data is necessary. If it’s not reliable, you won’t be able to use it effectively. Not sure what your data quality is? Then it’s time to take action. You need to make decisions based on data. It’s not a trend; it’s a necessity. But if you don’t know if your data quality is up to par, you can’t know if your decisions are, either.

Avoid making risky business decisions by taking control over your data quality now. In this guide, we’ll give you a plan so you can improve your data quality. Then, you’ll be able to use that information to make the best possible business decisions. Keep reading to learn what you need!

 

1. Decide What You Want From Your Data

The first step is to figure out what you want your data to do for your business. How are you going to use this information? This will help you figure out how you’re going to evaluate your data for quality.

Data quality might mean something different to you than it means to other organizations. For your organization, maybe the most important aspect of data quality is making sure you have accurate contact information for your customers. This will let you ensure that they get information or shipments accurately and on time.

However, for other companies, data quality might mean having detailed prospect profiles that will inform your marketing strategies. What does data quality mean to your company? It’s all about having the right information for the right purpose.

This means that the first step you’ll need to take is to figure out how your business will be using its data. Then, you can create your definition of data quality based on how you plan to use it.

Finally, you’ll need to figure out how you’ll measure your data quality, based on the definition you’ve outlined. You can use key performance indicators (KPIs) that are unique to your business to measure data quality.

It’s important to have these metrics defined, so you can figure out how effective your efforts towards improving data quality are. You’ll also be able to measure the return on investment of your data improvement strategies and allocate the resources you need effectively.

 

2. Assess Current Data Quality

Once you have an idea of your ideal data quality metrics, it’s time to take a look at where they stand now. You can’t implement new data quality management plans if you don’t know where you’re at.

Figure out where you stand in relation to where you need to be, and work from there. Right now, you might not even know where your data quality is. Getting a handle on the current situation will give you a significant advantage.

 

3. Hire the Best People

You need the right people to oversee improving data quality. Very few organizations centralize their data quality efforts. If you put the right people on the team, and designate a great leader, those efforts will go much farther.

If your data quality strategies are spread out across different departments, you’ll end up with different strategies instead of a cohesive plan. Your efforts to improve data quality are much more effective when you centralize them and give some people the designated responsibility for overseeing data.

Assigning one person to be the data “owner” (with a title like “chief data officer”) will put the responsibility in a single person’s hands. They’ll be able to take charge of the data standards and quality, so the efforts won’t be fractured across different departments.

If hiring for this position seems too expensive, keep in mind that the results could pay off nicely. Hiring one person to manage data quality can actually raise your profits, by streamlining your efforts.

Of course, this doesn’t mean that one person has to do it all by themselves. Make sure the person in charge is supported by a great team. Hire (or promote from within) the best data service officers, data stewards, data scientists, or analytics professionals. They’ll help make sure the new data policies are enforced.

 

4. Implement New Processes

Once you have your data team put together, you can put your new data improvement policies into place. Make improving data quality part of the everyday work at your organization.

You need a proactive approach that integrates with daily activities. Otherwise, you’ll end up finding the issues only after they arise, rather than preventing them beforehand. You’ll have to fight harder to fix problems after they occur, and you might lose customers or reputation in the process.

You need to implement processes that work from a business perspective. Make sure your data team knows exactly how the data will be implemented in the organization, so they can enforce processes accordingly. The best data isn’t valuable if you can’t put it to work for you.

Over time, you can tweak your best data practices to fit the business better. However, taking action rather than reacting is essential to have an advantage.

 

5. Use Technology to Your Advantage

You’ll likely find your organization gathering masses of data that can’t be handled without technology. Implementing data organization technology is one of the most important parts of improving data quality. Make sure to streamline the technology you use, too – using different technology across the company can hinder your efforts instead of helping them.

 

Ready to Start Improving Data Quality?

Improving data quality will quickly pay off. It improves your business operations, which improves your bottom line. With great data, you’ll have the information you need to make your organization more successful.

Want to learn more about how to make your data management strategies effective? Then don’t miss this post.