Data quality

Data quality

Data quality is essential as one gets the desired results. Data entails statistics and facts gathered together for imminent reference and scrutiny. Coming up with a reliable database requires one to make an informed and structured decision. The reliability of data depends much on how relevant and valid the information collected is substantial (Batini & Scannapieco, 2016). Being smart is the key to ensure the result of the data is complete. One should ensure, data collected lacked gaps, and what was supposed to be assembled was effectively received.

Accuracy of data denotes the precision and how well it represents real-time conditions that the objective describes. It ought to convey an accurate message and lack elements of error. The state of correctness and competency needs to be brought up as well as the intended purpose. For example, i was collecting data to determine the age of my target market. Accuracy will be of importance to come up with the exact age of my consumers. It is discouraging to notice I mainly deal with the elderly when the young folks primarily consume most of my products. Reliable data should also be timely and provide relevancy to justify the effort in place. Janssen, van der Voort, & Wahyudi (2017) implied that time is of the essence as data usually turn out to be less useful as time lapses. Real-time results are essential to data as delayed evidence could lead to a state of affairs where one makes inaccurate verdicts. For instance, after discovering the age of my consumers, I should be on time to provide for them the products they require at that particular period. Incomplete data is ineffective as it lacks comprehensiveness and thus the importance of complete data.

Understanding the ample set of necessities constitute to informed decision making and determines whether all the requirements were met. An example is when offering a questionnaire; the questions should be systematic and ensure that all gaps get filled and completed dully by the client.