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What is the fuss around Data Quality? Why is it important?

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Our digitally-connected world has opened a wealth of opportunities to acquire data and craft campaigns that are effective, personalized, and tailored. With the right information accurate across all channels at our fingertips, organizations have become empowered to create approaches with increased chances for success.

On the other hand, the fact that there are many points of data entry on both the customer and company end increases the chances of errors being introduced into organizational databases. If steps are not taken to prevent this inaccurate data entry and to cleanse databases of incorrect client data, marketing efforts, awareness campaigns, and other outreaches to your users can actually be less effective.

To successfully meet the challenges of our data-driven age and take full advantage of the opportunities it offers, companies and organizations must strive for the highest data quality.

What Is Data Quality?

A definition is the following: it is the ability of a given data set to serve an intended purpose. It refers to the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses, usually by a database, data warehouse, or data analytics system.

A company, nonprofit organization, or other entity cannot have the highest high-quality data without an accurate understanding of what it looks like.

According to experts, data is of high quality when it satisfies the requirements of its intended use. In other words, companies know that they have good quality data when they are able to use it to communicate effectively with their constituents, determine clients’ needs, and find effective ways to serve their client base.
This definition is broad enough to help companies with varying products, markets, and missions to understand if their data is up to standards.

A Short List of Data Dimensions are:

Accuracy- Accurate data convey true information about a company’s clientele. If there are errors in client data, contact with customers is impossible, and it is extremely difficult to reach a larger audience.

Completeness–  data is also defined by its completeness. To get a full picture of customer needs, as well as maintain open channels of communication, a business must have data that includes all of the pertinent information and is up-to-date on customers’ contact information.

Consistency– The key to quality is the concept of whether the data is understood. Massive databases full of data are useless if reporting and modeling cannot understand what the data says about your users and how best to reach them.

Integrity– High-quality data can mean the difference between high profitability and a company that just barely gets by. It is here to help your company acquire, retain, and understand client data.

Relevancy– Data should not only be accurate, but it must also be relevant to the needs and purposes of a business. Companies waste valuable storage space if they collect information that is extraneous to their purpose, and irrelevant data may also prevent key customer targets from being identified in reporting and analysis.

What Do I Need To Do Know About Data Quality?

Quality data is useful data. To be of high quality, data must be consistent and unambiguous. Its issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies.

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What Activities Are Involved?

Data quality activities involve data rationalization and validation.

It aslo efforts are often needed while integrating disparate applications that occur during merger and acquisition activities, but also when siloed data systems within a single organization are brought together for the first time in a data warehouse or big data lake. Data quality is also critical to the efficiency of horizontal business applications such as enterprise resource planning (ERP) or customer relationship management (CRM).

A Few Uses of Data Quality are:

  • Increasing the value of organizational data and the opportunities to use it.
  • Reducing risk and cost associated with poor-quality data.
  • Improving organizational efficiency and productivity.
  • Protecting and enhancing the organization’s reputation.
  • Data profiling.
  • Data standardization.
  • Data monitoring.
  • Data cleansing

Let’s sum up

When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better operational efficiency.

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