Today we will talk about data quality and how it affects data analytics. Before you get into analytics and, more to the point, data, you have to know the source of your data.

I’ve been doing research for a long time, and what I observe is the source of the data. For any other process with the data you collect, you need to verify the start of the data so that you can do further processes with the data collected. 

So, where the information comes from is the most important thing to know, and how do you collect quality data? 

There are so many opportunities for layers of data collection to solve your problems. Without further ado, let’s look at how to do it.

When it comes to analytics, you can hear that 59% of businesses use analytics as their capability. So it’s not just limited to large organizations; everyone can collect high-quality data and use the information for better analytics according to business technology and further needs.

Data quality standards

Data is available in large quantities for any business, regardless of the field. 

You have to be aware of the quality of less information or any other helpful document, which would be useless if not superior in quality. And no business wants that to affect productivity in the long run. Right? 

And to achieve standard data quality, a business must follow a documented agreement or pre-planned format. 

This includes:

  • Documentation
  • Data format
  • Data characteristics
  • Pre-planned business standards

Your customer may not be satisfied, or your product may not be able to compete in the marketplace if your information is invalid. And it will ultimately affect the entire business cycle. 

Dimensions of data quality

The image above shows the dimensions of data that an organization must follow. You’re probably thinking about how you can measure data quality. Since data quality is a top priority for any business, we’ve explored several ways to achieve quality through Web Data Integration (WDI). Stored and structured data from Web sites using a process that integrates and organizes whole data into a workflow from various Web site sources is WDI. In a nutshell, it is a process that involves conversion, data access, data mapping, quality assurance, and more.  

Data quality issues

This is where the data management process comes in, as businesses are faced with the challenges of managing massive amounts of data. But at the same time, it’s crucial to address quality issues. 

 Data management is a continuous process. Every day, data must be checked and processed with quality. 

In 2016, IBM faced a data quality problem with a high price to pay for solving it, and it was $3.1 trillion for the entire U.S. economy. Imagine the value of data if it wasn’t processed with quality. 

Doing the research, we can tell you that about 30 percent of data analysts spend 40 percent of their time validating data before using it for business operations and decision-making. This clearly shows the magnitude of the problems with data. 

How do you ensure data quality?

Monitoring information is a key aspect of ensuring the best quality and cleaning up all business data for better use. Information validation is the further work of uncovering new opportunities and using qualified information. 

How qualitative information helps businesses

Quality data helps businesses achieve desired results and builds customer confidence in an organization that provides quality products. It also helps bring data, technology, and organizational culture together to achieve meaningful results. 

First, check the uniqueness of the data and analyze it. 

Metadata management: Many people have verified data quality in various ways. 

Next in line is to help documentation for data processors and providers to ensure proper accessibility of the data measurement.

Policies now require management of the data collected because people in different parts of the company may misinterpret specific data terms.

Centralized metadata management helps solve these problems by reducing inconsistency and guiding toward quality standards. 

At the end of the day, you must draw up some specifications according to business standards that offer data vocabulary so that all incoming data will go through the same qualification cycle. 

The quality of your information will make your service/product more competent and help you reduce the costs associated with the quality of lesser statistics. i.e., decisions made using incorrect analytics. 

Choosing the right tools

The procedure for determining the value of your data and addressing flaws in your data that support adequate information for operational business processes and decision-making is all about data tools. 

Demoing any data quality tools is a wise decision to get hands-on tools before executing data quality tools for better results. Here we list the essential cloud tools that help ensure data quality : 

-> Data Profiling

-> Data management

-> Data Preparation.

It is essential to choose the right tools and technology to keep all the data available to make it accurate. There are four main aspects to consider before using data quality tools and technologies to get reliable analytics information:

– Data management 

– Integration with third parties 

– Fully mobile support for end users

– Shareable dashboards for simplified communication

Why data quality matters

It’s important to know what your data represents, i.e., the data type. Thus, data resources are equally important in defining and changing your data to meet the needs of organizations. 

In this regard, we have learned that high-quality information ensures greater effectiveness in ensuring a company’s success. This is based on reliance on data and fact-based decisions rather than following legacy systems.

Let’s look at five significant components that show the importance of data quality: 

Completeness: Incomplete data leads to a waste of time and resources, while the absence of gaps in the data indicates that the data is reliable and more efficient. 

Accuracy: Clean data and data collected from the database shows its relevance and accurately reflect its value.

Consistency: Consistency is key. The data collected should be of the expected type so that it can be easily used.

Validity: The initial process is essential for better understanding, which ensures that the data is valid for the end result.

Timeliness: The information shows its value, which is used to improve business performance. To achieve this goal, the data must be obtained in the expected time so that it can be used promptly. 

Each of the above components must be done correctly to produce quality information.

Yes, the inadequacy of any one component or aspect can lead to a failure in the data qualification process. With real-time data and analytics, businesses are better equipped to make better and more informed decisions.


One project can be done with ease, but when it comes to managing a large spreadsheet, a continuous process is needed to make the data more focused and results-oriented. It takes effort and planning to make it reliable and accurate. And that’s what entrepreneurs are looking for. 

Being confident in your data allows you to make better decisions, and you can rely on it. The above aspects will help you ensure a high level of data quality, or contact us about data quality in business intelligence.