The data quality assessment: does your data measure up

The realities of our time are such that companies with different budgets are forced to use only good-quality data. They will help you avoid many problems and find the best business strategy. The so-called data quality assessment is used to evaluate the information received and verify it according to many criteria. It allows you to identify any inconsistencies and reject certain information.

Procedure for assessing the quality of information

Proper organization of managed data QA is an essential task for any business owner. This process consists of many stages, each of which specific actions are performed. The work is carried out in manual and machine mode. In the first case, all checks are carried out due to the efforts of the company’s specialists, and in the second – special software. Both options have pros and cons, so choosing one is an individual decision of the company owner or authorized persons. Manual verification is suitable for its low cost. At the same time, it is carried out over a long period, which is not always acceptable for company owners. Software quality control increases costs but provides a definite time advantage.

The control procedure is rather complicated. At its initial stage, all the necessary information is collected without additional actions (for example, sorting and checking). This task takes much time, so it is often provided not to company specialists but particular programs. The information is then checked against one or more criteria. Their number and list are chosen by business owners depending on current needs. Verification also requires a certain amount of time. The sorted information is sent to a standard array, which is additionally studied by people or programs (depending on the chosen method). The results are transferred to the customer, who uses quality materials to achieve specific goals (for example, important decisions are made based on the information received).

Definition of quality

Different information makes it impossible to say what quality it has immediately. To determine this, performing a data check on eight fundamental indicators is necessary. Each criterion will remove certain information from the public database, leaving only qualitative information. This check applies to various data, regardless of their presentation (numbers, text, graphs, charts, etc.).

Key evaluation criteria:

1. Uniqueness. The importance of meeting this indicator cannot be overestimated. Identity significantly impacts quality, so engage in evaluating most of the information. To meet this parameter, certain information must be present in the database in the singular. Also, they must not occur in other arrays.
2. Relevance. It is a crucial parameter. Therefore, it is taken into account almost always. Up-to-date information is information provided at the right time in the right place. So it will be helpful for the customer, and its use will not lead to any problems. Any inconsistencies with this parameter will reduce the quality as much as possible, rendering the array useless.
3. Completeness. An indication of all data about a person, event, or action is conformity to the completeness indicator. At the same time, the scarcity of certain information will make them incomplete and improper for use in work. Completeness is one of the critical criteria, so it is not recommended to neglect it.
4. Accuracy. It points to the information according to reality. Professionals consider this parameter to be crucial and take it into account in any quality check. Ideally, the accuracy should be as high as possible. So simple information will become demanded and help evaluate the actual state of affairs.
5. Accordance. Information about one person, action, or event collected from various sources must be identical. Only then will it pass the compliance test. If any differences become noticeable, the quality level will drop sharply, and you will have to recheck the entire array.
6. Relevance. This indicator is regularly used to determine the quality. Accordance with him will make valuable information to the company. With this data, you can take various actions to optimize the company’s work and build plans for the future.
7. Interconnectedness. In special situations, a criterion such as interconnectedness is used to determine quality. It demonstrates that materials from several arrays have standard features that connect them. This data property is widely used when working with client databases. It lets you quickly find information about a person and identify all his actions.
8. Reliability. Very often, reliability is separated from other essential criteria. This is because it is a complex indicator that considers several information properties simultaneously. Most often, reliable data meet the requirements for completeness and accuracy.
   Data that meets all of these requirements can significantly help your business, but it isn’t always easy to find. For this reason, you should partner with a data provider, like ZoomInfo, to get more relevant leads. However, there are also other ZoomInfo alternatives to explore, so be sure to do your research. 

Possible problems

Quality control will become incomplete if there is no check for the presence of deficiencies in the public database. All of them significantly reduce quality, so you should eliminate them promptly.

Unacceptable disadvantages:

1. Passes. This shortcoming has a strong negative impact on data quality. Completeness suffers from it, which affects the result of evaluating the entire array. This problem becomes especially acute when working with a customer database. Omissions in their personal information (for example, date of birth, full name, etc.) make it difficult to identify individuals and any actions with customers.
2. Repetitions. The problem arises when information is collected from several sources. This leads to the appearance of repetitions, which reduce the quality and distort the assessment of the current situation.
3. Anomalies. The selling price of a product cannot be lower than its cost. If such information is found in the array, then such a deficiency is called an anomaly. It reduces the quality and complicates the statistical analysis (it distorts the calculations of income, average cost, and many related indicators). Most often, anomalies appear due to the human factor, and their presence can be eliminated only by machine data control.
4. Contradictions. Information about an identical product, service, or action may differ in several sources. This shortcoming is called a contradiction, which can be pretty challenging to identify. The presence of such a problem will cause distrust of the authorities used and will lead to the need for additional verification of all information.
5. Incorrect formats. If data is collected from foreign sources, incorrect format mappings may occur. This is due to the use of non-standard units of distance, time, and cost in many countries. Getting rid of such a problem will be as difficult as possible. Even if this can be done, the various costs will be huge.

Checking the available data by quality parameters is a mandatory procedure. It provides a multi-stage verification, which will clean information from unnecessary “garbage.” This work requires significant financial investments; however, the money spent will pay off many times over with the proper organization of the process.