The Importance of Data Quality in Big Data Analytics 

A company’s ability to make better business decisions that boost sales, enhance internal procedures, and offer it a competitive edge over rivals can be facilitated by data insights, which increases the accuracy of analytics applications. 

Big data analytics can be used to provide relevant data insights and enable business leaders to make data-driven decisions based on such insights, uncovering hidden trends and patterns that are not readily apparent by utilizing typical analytics techniques. 

The success of big data analytics projects depends on data quality, which is a hurdle that big data faces, much like any other digital or IT solution. 

In fact, many companies are aware of these issues and taking action to address them in order to get the most value possible from their data assets. Keep reading to explore all the importance of data quality in big data analytics.

What Is Big Data Quality?

In general, the criteria for data quality will vary amongst analytics initiatives. A retail or eCommerce company would, for instance, want to have a broad perspective of the big picture, specifically their business performance, if they are collecting business data to study consumer behavior on their website. 

In this case, the retail organization is not required to have 100% accurate visitor activity records in order to conduct specific analyses and determine the present status of the business. 

An accuracy of 98% or more might be necessary, though, if a hospital or healthcare facility uses advanced analytics to track and comprehend the health or well-being of individual patients. 

Different data quality requirements may apply depending on the business initiative’s goals or on industry norms. Instead of pushing for the highest possible degree of data quality, business organizations need to have a realistic grasp. 

Analyzing the big data quality standards required by your organization and establishing them for your big data is the first and foremost step.

What Exactly Is Good Data Quality?

Organizations must comprehend the standards and traits of data quality in order to decide whether a particular data set is appropriate for their initiatives. Accuracy, completeness, reliability, relevance, and timeliness are some of the many data quality standards that take into account these fundamental qualities. 

It should be highlighted that, in terms of big data quality only, not all of these five requirements apply to big data projects and that no requirement can be guaranteed to be satisfied 100% of the time.

What Are The Benefits of Good Data Quality?

The simplest way to comprehend the significance of data quality is to consider the benefits that are served by implementing a data-driven strategy. Some of them are listed below. Let’s take a look at them!

1. Reduced Mailing Expenses

Less undeliverable mail is a result of accurate client information. As fewer packages need to be resent after failing to reach their destinations, you spend less on postage.

2. More Favorable Customer Relations

Knowing your customers is made possible by reliable data insights. This prevents you from providing them with things they don’t want to hear and enables you to anticipate their requirements. You build goodwill and a solid relationship with your brand by exceeding and meeting customer expectations.

3. More Consistent Data

Inconsistent data across the organization is a common issue for businesses with multiple points of contact with their clients. Duplicate records missed communications opportunities, and departmental data silos are all caused by inconsistent data.

Making data accessible throughout your business to foster team communication and maintain record consistency is a crucial component of maintaining data quality.

4. Increased Successful Marketing

Targeting an excessively broad audience is a waste of money when you don’t have demographic information on your clients. A large audience might not even be interested in your goods or services.

For more successful campaigns, you can easily identify the characteristics and habits of your prospective customers by creating databases packed with high-quality data.

Conclusion

So, this was all you needed to know about the importance of data quality in big data analytics. You can easily head to Education Nest and enroll in various informative courses similar to Data-driven Decisions with Power BI to learn more about big data analytics.