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Data Quality Services – Understanding Its Importance and Benefits!

Data Quality Services – Understanding its Importance and Benefits!

hasansumon623
by Hasan Sumon — 3 months ago in Business Ideas 4 min. read
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Nowadays, data is a burning topic in the business industry. Data Quality Services is concerned about the insights and worth it can add to your organization.

The reason being that data is one of the most priceless resources available today to agencies, marketers, publishers, media groups, and many more.

But data is only helpful if it is of good quality. Bad data is neither important nor useful for company benefit. When used incorrectly, bad data can make your company make the most harmful decisions that can be costly to your company.

IBM estimated that bad data costs $3.1 trillion per year to the U.S. economy. These costs equate to the time spent by employees in correcting bad data and errors, which results in mistakes with customers.

Data quality is a determination of the state of data as per various factors like accuracy, entirety, consistency, reliability, and whether it is updated or not.

Checking data quality levels can assist companies in identifying data errors that need to be determined and find whether their data is good to serve its projected purpose.

Noticeably, it is a big opportunity if you get a chance to improve the quality of your data. Let’s take an overview of data quality and why it is important for businesses.

Why Data Quality is Important?

Bad data can have noteworthy business risks for companies. Poor-quality data is often considered as the source of operational insufficiency, incorrect analytics, and bad business strategies.

Examples of the economic harm that bad data quality can cause include; extra expenses when orders are shipped to the incorrect customer addresses, missing sales opportunities due to flawed or incomplete customer data, and fines for inappropriate financial or regulatory conformity reporting.

Several factors that add to data quality are:

Accuracy

Amongst marketers who pay for demographic data, 84% believe that accuracy is imperative to help them make their purchasing decisions.

Accuracy refers to how correctly data portrays the real-world situations it aims to explain. Incorrect data creates apparent problems, as it can cause you to make incorrect decisions.

The actions you take according to those decisions might not have the impact you expect, because they are based on incorrect data.

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Completeness

If data is absolute, there will be no differences in it. Everything that was projected to be collected was successfully gathered.

If a customer overlooked some questions in a survey, the data they presented would not be complete. If your data is not complete, you might have difficulty in collecting correct insights from it.

If someone ignores some of the questions on an assessment, it may make the provided information they present less useful.

For example, if a respondent doesn’t comprise their age, it will be tougher to mark content to employees based on their age.
Also read: Is eCommerce the Future of Retail?

Relevancy

The data you gather should also be helpful for the campaigns and start-ups you planned for its usage.

Even if the information you gather has all the other features of high-quality data, if it does not apply to your goals, it’s not functional for you.

It’s imperative to set goals for your data collection so that you understand what type of data to collect.

Validity

Data is valid only if it stays in the correct format, of the exact type and fits within the right range. If data does not match these standards, you might find difficulty in organizing and analyzing it.

There is numerous software available that can help you transform data into the right format.

Timeliness

Timeliness means how recently the event the data signifies occurred. Usually, data should be traced as quickly after the real-world event as possible.

Normally, data becomes less functional and less correct as time passes on. Data that imitate events that occurred most recently will make it possible to replicate the present reality.

Using out-of-date data can result in inaccurate results and taking measures that don’t display the current reality.

Consistency

When evaluating the difference between a data component and its equivalent across numerous data sets or databases, it should be similar. This lack of variation between numerous versions of a single data item is referred to as consistency.

A data item needs to be consistent in both its content and format. If data is not consistent, various groups may be working under different theories about what is correct.

It means that the different departments within your company are not well coordinated and may even unwillingly be working against each other.

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Benefits of Good Data Quality

Good data management is vital to stay competitive in the market and to enable your company to take benefits from opportunities. High-quality data can also help your business to get various concrete benefits.

Some of the possible benefits you get with good data quality comprise:

  • Well-versed Decision-Making
  • Enhanced Audience Targeting
  • More Efficient Content and Marketing Campaigns
  • Better Relationships With Customers
  • Easy Implementation of Data
  • Competitive Advantage
  • Improved Profitability
Also read: Shoppable Images: What are They and How They can Increase Brand Sales and Trust

Data Quality Management

In data quality management, the main objective is to develop a strategy for data preparation, to avoid future data quality concerns and to clean data that does not match the data quality Key Performance Indicators (KPIs) required to attain the business goals of today and tomorrow.

The data quality KPIs will normally be measured on the key business data assets within the data quality measures as data uniqueness, data consistency, data completeness, data conformity, data precision, data timeliness, data accuracy, data validity, data integrity, and data relevance.

The data quality KPIs should relate to the KPIs used to calculate the general business performance. The processes used to avoid data quality concerns and ultimate data cleansing involve the following disciplines:

  • Data Governance
  • Data Profiling
  • Data Matching
  • Data Quality Reporting
  • Master Data Management (MDM)
  • Customer Data Integration (CDI)
  • Product Information Management (PIM)
  • Digital Asset Management (DAM)

Do you feel worried about managing your data and extracting its highest quality to make more informed decisions? If so, contact professional Data Quality Services and make yourself prepared to compete in this data-driven world.

Hasan Sumon

Hi, I am a writer and blogger, writes tech blogs for a variety of online publications. I am also a blogger outreach expert and content marketer.

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