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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 many opportunities to acquire data and craft compelling, personalised, and tailored campaigns. Accurate information empowers organisations for successful approaches.

On the other hand, multiple data entry points increase database errors. If steps are not taken to prevent this inaccurate data entry and cleanse databases of incorrect client data, marketing efforts, awareness campaigns, and other outreaches to your users can be less effective.

Besides, companies and organisations must strive for the highest quality to meet the challenges of our data-driven age and take full advantage of its opportunities.

What is it?

A definition is the following: it is the ability of a given data set to serve an intended purpose. Also, it depends on its processing and analysis capabilities, typically through a database or analytics system.

A company, nonprofit organisation, or other entity cannot have the high-quality without an accurate understanding of its appearance.

According to experts, data is of high quality when it satisfies the requirements of its intended use. In other words, companies know they have good quality data when they can 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 factual information about a company’s clientele. If there are errors in client data, contact with customers is impossible, and it is tough to reach a larger audience.

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

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

Integrity– It can mean the difference between high profitability and a company barely getting 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. Also, companies waste valuable storage space if they collect information extraneous to their purpose. In addition, 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 valuable 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.

Efforts are also often needed while integrating disparate applications during merger and acquisition activities. Also, when siloed data systems within a single organization are brought together for the first time in a data warehouse or big data lake. Moreover, data quality is 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:

  • We are increasing the value of organizational data and the opportunities to use it.
  • We are reducing the risk and cost associated with poor-quality data.
  • We are improving organizational efficiency and productivity.
  • They are 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 quickly processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts, other types of data analytics, and better operational efficiency.

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