What To Do With The Poor-Quality Databases?

Using poor-quality database

Duplicate, incomplete, or out-of-date data sets, unstructured data, or data that can’t be shared are all examples of poor-quality databases. It’s trapped in an employee’s Excel worksheet, for example. The relevant data is required for every successful digitalization effort. Yes, every business has data, but it’s only valuable if it’s well-structured, up-to-date, and accurate.

Using a database is similar to cooking: the meal won’t taste nice if you don’t have the correct components or low quality. This article illustrates why businesses should avoid using low-quality data like chefs should avoid using low-quality food.

Let’s look at each of these outcomes and how to avoid them. Rather than reaping the rewards of making better-informed company decisions, this will aid in the enhancement of excellent quality data.

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  • Poor Decision-Making:

Poor-quality data leads to poor decisions. A decision may not be better than the information it is based on, and critical decisions based on insufficient quality data can have dire consequences. This is another reason you should make sure that your data represents reality.

  • Business Inefficiencies:

Poor-quality data causes inefficiencies in business processes that rely on data from reports to products to order, and just about everything in between that requires facts. These inefficiencies can result in costly redecorating efforts to validate and correct data errors rather than focusing on original duties.

  • Mistrust:

Poor-quality data breeds mistrust, especially in industries where regulations govern relationships or business with specific customers, such as finance. Maintaining good quality data can mean the difference between compliance and fines of millions of dollars. If the data is inaccurate, time, money, and reputation can be lost, adversely affecting your business and reducing customer trust.

  • Lost Revenue:

Poor quality data can lead to loss of revenue in many ways. For example, the communication fails to convert to sales because the underlying customer data is incorrect. Insufficient data can result in wrong targeting and communication, especially multichannel sales.

Obtaining high-quality data:

When dealing with data, keep the following five criteria in mind to avoid poor data quality:

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  • Accuracy: It is concerned with the accuracy of the values stored in various database record fields. Is the name accurately spelled? Is the monetary amount correctly entered?
  • Completeness: Users should be aware of the breadth of the data and know what is contained in each data piece, such as “total revenue.”
  • Consistency: The summary information matches the underlying description.
  • Uniqueness: An item or entity must match the only object in your data in the actual world. In the real world, ABC Ltd. and ABC Ltd. are the same entity; thus, one is eliminated.
  • Timeliness: The data must be current and relevant to the business’s needs, and there must be a procedure for certified users to update or amend the data manually.

Data of high quality is a valuable commodity that is both wanted and required for project management, fraud prevention, performance evaluation, financial management, and effective service delivery. While data quality is critical, it is sometimes overlooked in haste to complete your other jobs. Give your company’s data the engagement it deserves so you can make better business choices, sales projections, and negotiations.