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Data Quality Management Best Practices

Posted by Sas Chatterjee on Jun, 30, 2021 01:06

Aside from employees, data is a lifeblood of any organization. It can help businesses make better decisions, solve problems, understand performance, streamline operations, and even know target customers. But it depends on how data is managed. In this simple guide, we will explore different topics on data quality management. Without further ado, let’s begin! 

What is Data Quality Management? 

As the name indicates, data quality management is the practice of maintaining correct and accurate information within an organization, from the data acquisition, implementation to distribution. Managerial oversight of data is also necessary. 

Why Good Data Quality Matters

The competition in multiple industries is more demanding than before, and standing out from the rest is every business owner’s objective. But it is easier said than done. This is where good data quality can come into play. 

When running a business, you have numerous responsibilities to deal with and shoulder. That’s why you or your IT team is more likely to take shortcuts when handling big data. 

Here are some of the benefits of reliable data quality management: 

A High Return On Investment 

One of the advantages of DQM is that it can increase your web traffic, client conversion, daily sales, and ROI. Particularly, with error-free data, your team can craft marketing campaigns that drive interest, attract attention, and resonate with your target market’s requirements. Here is a list of all the benefits you can get from BI tool like Power BI.

What’s more, this big data in marketing can show which specific content brings in the most web traffic and sales. Over time, it can give you an idea of where to focus on developing any advertising campaign, which minimizes unnecessary expenses. 

More Informed and Quicker Decision-Making

Every day, businesses make decisions, and the absence of data can cause delays—time matters for every organization in both the private and public sectors. The availability of data, on the other hand, can be a lifesaver. Every person in all phases of operations can decide with confidence and take advantage of new opportunities using predictive analytics consulting.

Effortless and Effective Data Implementation 

Incomplete and inconsistent data is a common problem in effective data quality management. Besides the extra time to fix that data, there will be a delay in implementing insights.  That’s frustrating. But with precise and consistent data, it would not take time to implement something. You can always get correct picture from dashboard of BI tools the glimpse of your organization performance.

That means your business will always have a competitive advantage over the others using real time analytics. Moreover, you can take once a good opportunity knocks. 

More Efficient Market Targeting 

You have in-depth expertise in product development or employee supervision. But admit it or not, targeting your preferred audience is not your specialty. That’s all right! Data quality can come to your rescue. Without the correct data at hand, marketers do not have a choice but to target a broad audience, which is a no-no. 

When your marketing team has good access to comprehensive information, they can target the right customers for your services. Additionally, it will not be challenging to craft campaigns that appeal to your market. 

Long-Lasting Client Relationship

Most businesses aim only to acquire sales or revenues. Do not do the same thing. It is ideal to strive to develop a long-lasting client relationship aside from customer conversion and ROI. 

While there are various ways to develop rapport with clients, spending time to study your market is suitable. Make sure to include their interests, preferences, and needs. 

With accurate client data, you can tailor your marketing campaign, services, and customer support according to their problems and expectations. 

EPCGroup have been providing Microsoft Power BI Consulting for data management in organizations. EPCGroup is also Microsoft Gold certified Partner. We have over 100+ years of total combined experience with organizations having custom requirement.

Best Data Quality Management Practices

As a business owner, you want the best for your company. But managing data for the first time is complex. Failure is always part of the process, mainly for beginners. But that does not mean you cannot avoid mistakes in your big data. 

Below are some of the experts-recommended quality management practices for beginners and pros: 

DQM Should be One of Your Top Priorities

Lead generation and client conversion are among the business aspects that many individuals focus on. Managing data is one of the things that some ignore. What everyone does not know is that with a consistent data, it will be easy to generate leads and convert them into high-paying clients. 

If you do not prioritize the management of your big data, it is never too late. It is also worthwhile to let your team undergo extensive and relevant training for successful and better implementation. 

Data Automation Can Give Your Company an Edge Over the Others 

Manual data entries have been the trend for businesses. While they are cost-effective, they are time-consuming and daunting. What’s worse is that the risk of committing errors is higher than expected. The data could be incomplete, ambiguous, duplicated, and outdated. This only increasing opportunities of error in data quality management.

Say bye to that problem with data automation. You could now invest in data entry automation software. But be wary when choosing the right technology. Do not be tempted to purchase something cheap. Always direct your attention to tools with great value. 

Internal Training is a Smart Idea 

Not every part of your company knows how to manage big data. That’s why it is never advisable to implement a new task like data management right away.  Achieving good data quality requires in-depth experience in principles, technologies, and procedures. While some information is available online, nothing can beat a formal training. If you are using Microsoft BI then you can go for Power BI training for employees for better managing big data.

If you are well-versed in DQM, you can handle the training yourself to save some cash. You can also consider finding and working with an experienced trainer. 

Check our Power BI Cost for effective data management tool.

A Data Auditing Process Should Be Done Regularly 

Suppose every department in your organization is competent in managing data. And you have cutting-edge technology. But do not relax. Your database may have duplicates, inconsistencies, and other errors. 

A frequent data auditing process can give you peace of mind. You can look for inaccuracies and outdated entries. From then on, update the details. 

Using the Cloud is Perfect 

Maybe, your data quality management tools are in separate corporate centers. For that reason, getting data from different sources comes with complexity and confusion sometimes. 

The trick here is to move your tools to the cloud to make them more accessible. With today’s variety of cloud providers, the selection process will be easy and effortless. 

It is also advisable to make a list and compare their services. Do not forget the pricing plans, features, and customer support. The one that excels in these departments will be your best bet. 

You can also implement your own server for Business Intelligence reports like Power BI report server which gives higher rate of data refresh for real time analytics.

What Makes Data a high quality data? 

It is quite tricky for beginners to measure and determine the quality of big data. But have you ever wondered what makes data good? 

  • Accuracy. Your data should be precise and free of errors. It should not be misleading. 
  • Timeliness. It should be collected at the right time to make accurate, confident, and quick business decisions. It is also vital to update any obsolete information often. 
  • Availability. Let’s say you have consistent, timely, and accurate data. But that won’t help when it is not accessible for every department in your company. Make sure to transfer them to a platform in which everyone can access whenever they are. 
  • Relevance. Collecting data is a part of business operations. But do not just gather data that are not relevant to your company’s needs, as all of your efforts will only go to waste. 

Consequences of Poor Data Quality Control 

A lot of organizations do not mind DQM until it is too late. These are some signs that your business has a poor data quality control: 

Unexpected and Unnecessary Costs 

According to a study conducted by Gartner, companies lose around $14.2 million per year due to poor data quality management. That’s massive. And there are many things you can do with that vast amount of money. 

Although internal training and data automation may be costly, nothing is more expensive than losing your hard-earned money to inefficient DQM. 

Ineffective Decision-Making

Another disadvantage of poor DQM is ineffective decision-making. Poor decision-making can impact everything about an organization, from marketing, product development, employee productivity, quality services to reputation. 

A confident and quality decision, on the other hand, can result in constant business success. 

Missed Opportunities 

Besides financial losses, poor data control can affect your credibility. Your customers might have a negative impression of your company because of ineffective DQM. Moreover, you will not only lose customers to your competition but also miss other opportunities along the way. 

How to Maintain Data Quality? 

Once you have achieved data with the highest level of accuracy and consistency, your job does not stop there. Maintaining data quality management will be your next goal. Let’s be honest. It is not as straightforward as you think. 

Below are a few tips and tricks that will guide you from start to finish: 

  • Always understand where your data comes from, no matter how hectic your schedule is. 
  • Find time to comprehend and examine the margin of error. Any inaccuracy that stays in your system can be a huge problem. 
  • Set metadata measurements to avoid any misinterpretation of information. 
  • Lastly, be objective when picking out and eliminating data. Do not make decisions based on your opinions.  So, what’s your thought? We hope this guide helps you turn data quality into a reality

If you are planning to invest in a data management tool then you can check our Power BI Features.

Let's Get to Work Together!

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