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The data quality assessment: does your data measure up

Modern business practices require the use of quality data. They are an important component of the success of any company, as they reduce the number of erroneous decisions and increase the likelihood of a correct business development. To become the owner of such information and select it from a large number of materials, you need to master the process of data quality assessment.

The data quality assessment

All self-respecting companies necessarily devote a lot of time to assessing the quality of data. This process is a set of activities aimed at obtaining only important, accurate and reliable information. It will become the basis for making most decisions that affect various aspects of the work of any company.

Assessment and managed data QA are required processes. They help to sort all the incoming information and make sure that it meets all quality criteria. This approach will provide an opportunity to obtain valuable data that will eliminate all shortcomings in the work and enhance the influence of positive components. The data quality assessment process is complex and multi-step. During its implementation, all materials are tested for a variety of indicators. The result is the removal of everything superfluous from the general array of information, which can mislead business owners and make them make the wrong decisions.

How to measure data quality?

To date, there is only one effective way to assess data quality. It consists in comparing the available information with certain standards according to many different criteria. This process requires careful attention to detail. Only in this case it will be possible to perform the work qualitatively and provide the business with high-quality data.

Measure data quality criteria:

  1. Accuracy. It is this characteristic of information that is considered by many experts to be the most important. Accuracy refers to the correspondence of data to true values. This is not always the case, so accurate information is a very valuable resource. Its presence enables businesses to see a plausible picture and draw appropriate conclusions from this.
  2. Uniqueness. This characteristic is very important for the correct assessment of the quality of information. It makes it possible to highlight certain indicators that are not found in other data arrays. Thanks to this, the process of identifying this or that information is greatly simplified and the possibility of its substitution by other materials is excluded.
  3. Relevance. Information will only be useful if it is up to date. This characteristic is understood as the correspondence of data to the current moment in time and their timely use for business needs. Relevance is one of the key parameters by which the quality of information is measured. Any inconsistencies will render the collected data obsolete and, in most cases, useless for further use.
  4. Compliance. For data to be truly high-quality, it must be validated. This concept means the identity of information taken from different sources about the same object or phenomenon. The presence of any inconsistencies will render the collected data unsuitable for use during business decision making.
  5. Completeness. Another important characteristic of the quality of information is its completeness. It characterizes the absence of any missing data and gives a holistic view of the person, object, event, phenomenon of interest. If there are even small gaps in the material, then it automatically becomes incomplete and, in most cases, it is inappropriate to use it.
  6. Relationship. Much of the information available to people is interconnected. This relationship is a characteristic of the qualitative data used by businesses to make decisions. This term is characterized by the ability to find “traces” of information in different arrays and link them together to obtain a holistic view of the situation. The lack of correlation will also negatively affect the ability to quickly identify data, which will slow down the work of specialists as much as possible.
  7. Relevance. This characteristic is important for assessing data quality. It shows the level of compliance of the collected information with the needs of its owner. In simple words, the data that has become available to businesses gives them the opportunity to make important decisions regarding certain aspects of their activities. Irrelevant information can also be useful, but not for making decisions on issues of interest to the business.
  8. Reliability. In some cases, specialists use this characteristic of information quality. It is a combination of accuracy and completeness of the data being the key parameters. In this regard, reliability makes it possible to comprehensively evaluate the available materials and further improve the quality of the data.

The process of measuring the quality of information also involves checking it for certain problems. Each of them negatively affects the final result and makes the data less useful for its owner.

The main problems, the presence of which is unacceptable in qualitative data:

  1. Missing values. This problem is one of the main ones when it comes to working with clients. Missing data does not allow you to get a holistic view of the person, which automatically affects the quality of this information.
  2. Duplicates. This problem turns the whole array into non-unique and useless information for business. The presence of duplicates also forces specialists to carry out additional work to remove them from a large amount of unique data.
  3. Contradictions. For information to be of high quality, it should not contain any contradictions. Their presence will not make it possible to pass a check on such a parameter as compliance. In addition, contradictions will call into question the reliability of the sources from which the information was extracted.
  4. Abnormal values. Even in a large array, data values ​​should be within certain limits, consistent with common sense and a general idea of ​​​​the situation. The presence of any deviations will complicate the process of checking the quality of the data and make it less useful for business.
  5. Incorrect formats. This problem is often encountered in data sets collected from a large number of sources (domestic and foreign). Incorrect data in them appears as a result of using different units of measurement, time format, and other things.

Evaluating data against generally accepted quality criteria is difficult. This process involves painstaking work involving qualified specialists. However, all the funds invested in this business will not be in vain. They will provide high-quality data that will become indispensable when making important decisions.

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