There’s no denying that data quality is of paramount importance. A study by McKinsey established that organizations utilizing high-quality data are likely to make 19 times more profit than the organizations that aren’t data-driven. Now, that’s a statistic you cannot ignore.
If your goal is to stay on top of data quality, top-notch data quality management is what you need. To this end, there are many practices you can follow, such as making data quality your company’s No.1 priority, regular monitoring, learning from mistakes, investing in training, appointing skilled personnel, and making the most out of technology.
To know more about these data quality management best practices in detail, make it a point to stay with us until the end!
Top 6 Practices for Flawless Data Quality Management
We have rounded up some of the data quality management best practices that have witnessed wild success in the past. See for yourself:
1. Make Data Quality Your Priority No.1
Irrespective of the industry your business is associated with, you’re bound to deal with lots of data daily. Here’s an example. Let’s say that you’re at the helm of a clothing enterprise.
Your company has many departments and units, like marketing, design, merchandising, sampling, planning, and so on. And all of them deal with disparate sets of data. That’s why data quality cannot be a one-person show.
Every department and unit needs to ensure optimum data quality is their top-most priority from top to bottom. When every team prioritizes data quality, they will reap numerous benefits, like improved and faster decision making, precise audience targeting, sharp marketing campaigns, etc.
Here’s a statistic for you: IBM estimated that poor data quality is responsible for costing US businesses a mindboggling $3.1 trillion per year. Yes, you read that right.
2. Regular Monitoring is Essential
When it comes to data quality management best practices, this is an indispensable one. Data quality cannot be achieved overnight, and it requires regular monitoring. You can constantly measure your data quality by evaluating certain parameters:
- The ratio of Data Errors
- Frequency of Empty Values Incidence
- The Email Bounce Rates
- The Costs of Storing Data
- How Long it Takes for You to Extract Relevant Information
By evaluating your data based on these parameters, you can gauge how accurate, relevant, valid, and consistent your data is. Through regular monitoring, you will be able to identify and rectify the errors easily and on the go.
Data auditing can prove to be very useful in this regard.
In the absence of regular data quality monitoring, these errors will start piling on top of each other. And it will take a lot of time, effort, and resources to fix them.
3. Learn from the Mistakes
When you monitor data regularly, you will be able to spot the issues that occur frequently. They might be responsible for compromising data quality.
After you’ve spotted the issues, there are two things you need to do:
- Rectify them and
- Make sure that the issues don’t crop up again.
Considering that data is vast and complex by nature, errors are an inevitable consequence.
The errors can be anything- incomplete data, duplicate data, incompatible data formats, and so on. It would help if you rectified them at the earliest to uphold optimum data quality.
Data errors can prove to be very detrimental in the long run, as is exemplified by the following case study on Hawaiian Airlines. When passengers booked tickets, a glitch caused the reservation software to charge them based on dollars instead of miles. It resulted in some passengers being billed as much as $674,000 for a ticket!
Learning from mistakes is a great policy that one can follow anytime, anywhere. That’s why it’s one powerful data quality management best practices out there.
4. Invest in Training
At the end of the day, data quality rests in the hands of the employees handling them. And data quality is not something that you can learn in one sitting. It would be a good idea to get your employees trained in the various aspects of data quality.
Their training should be intensive, and it should cover the various concepts and principles that go into ensuring data quality. Here’s what you can do. If you’re a business owner, you can encourage your employees to take data quality-related courses by incentivizing them.
There are many great certification options out there, and you can pick one that suits your requirements. You will find that investment in data quality training creates a win-win situation, as the productivity of your employees gets a major boost.
Research has found that dealing with below-par data quality can reduce employees’ productivity by nearly 27%.
5. Appoint Skilled Personnel
Now, here’s a no-brainer data quality management best practice. Let’s revert to the example of the clothing enterprise we started with. You have many units and departments, and it’s not feasible for 2-3 people to monitor the data quality of every team.
That’s why you should consider appointing a data quality monitor in each unit. Typically, data monitors would be the people who are well-versed in the various aspects of data quality, making them ideal for the job.
You can have the data monitors report to you at regular intervals. In this way, you’ll be able to establish a streamlined process that comes with the twin benefits of being hassle-free and effective.
6. Make the Most Out of Technology
Some of the wildly successful data quality management best practices are the ones that make strategic use of the latest technology. That will help in optimizing, integrating, and automating data.
You can use specific platforms, like the cloud storage ones, to hold all your data quality systems and tools in one place. It will help in improving accessibility.
However, there’s one thing you need to do: ensure that your data is thoroughly cleansed before it is sent to storage. You can outsource this task by availing InfoCleanse data cleansing service.
If a study published by New York Times is to be believed, incorporating technology in data quality practices can save the employees’ time by 50-80%.
According to Ronald G. Ross, data quality plays an essential role in a company’s decision-making and daily operations. In this context, data quality is very much like the axle of a wheel.
By following data quality management best practices, you can stay ahead of the game by tapping into well-maintained data whenever the need arises. What’s more, you will also be able to reap immediate rewards.
We hope that this post has been useful in helping you understand the various data management practices and how you can implement them.