In a business environment, data is one of the main assets. Based on them, they develop advertising companies, monitor the development of the company and plan future investments. In this regard, it is important that specialists can operate with reliable data. This will make it possible to avoid mistakes that will negatively affect the financial component. To achieve this goal, constant data QA is needed. It will help to avoid various mistakes that entail many negative consequences for the business.

Data engineering and analysis

Quality data is not just information collected from different sources. They represent the end result of the work of specialists or special software. This whole process is called data engineering. It provides for the collection of information, with its further analysis and search for materials in accordance with the specified selection criteria. The extracted data from dozens or even hundreds of sources are collected into a single array. Further, the information that the business needs is extracted from it. Selected materials are shared with financial analysts and other company representatives who influence important business decisions. Based on this information, experts generate various reports that help evaluate the effectiveness of the business methods used and discuss a further action plan.

Measuring data quality

Data quality is not expressed in any units. Therefore, to measure them, they are compared with existing standards. This approach makes it possible to evaluate the collected information and determine its quality. Measurements are made on sets of parameters. Each of them is important for the final result, so they should not be neglected. Despite the huge number of indicators, 6 main ones are used to measure data quality. They are spelled out in a special document formed by the Data Management Association. The parameters specified in this paper are used when checking any data, from insignificant to the most important. To the six main parameters, many experts add 2 auxiliary ones. They also greatly affect the quality of the information collected.

1. Accuracy. This indicator is one of the main ones in determining the quality of data. At the same time, many experts consider it to be quite controversial and does not have a single definition. Most often, accuracy is understood as the ability of data to reflect the true state of affairs. An example would be a customer’s birthday. If it does not contain any errors, then the data can be considered accurate. If any, the information should be discarded or rechecked.
2. Uniqueness. This parameter must be taken into account when assessing data quality. Often it is the most important, as it allows you to identify a particular array of information. A striking example of the uniqueness of data is the client’s current account number. It is not repeated anywhere, therefore, it allows the information owner to identify a person in case of his further cooperation with a firm or organization (for example, for monthly transactions).
3. Timeliness. The data will remain of high quality if they are submitted at exactly the specified time. Any delay will render the collected information useless 9 out of 10 times. For example, it is important for a business that the database is updated every 24 hours. If there is a delay of at least a day, then the information will become of poor quality, as it will be submitted out of time. In this case, its use will lead to the fact that the expectations of business owners do not coincide with reality.
4. Consistency. This parameter has a huge impact on data quality. According to him, information collected from different sources on the same indicator should be identical. Only in this case can it be called consistent. An example of such data would be a customer’s zip code. It will always be the same, no matter what database you take it from. If there are any inconsistencies, then the data cannot be considered consistent.
5. Completeness. Some financial analysts consider this indicator to be the main one when measuring the quality of information. It shows the absence of data gaps about a person, event, transaction. For example, if a customer’s full name is not specified in the customer information, then the information cannot be called complete. The same is true of past events. The data on the completed transaction will be incomplete if they do not indicate the amount, time, or any other mandatory information.
6. Interconnectedness. The company’s specialists who work with databases should be able to link one information to another. This will allow you to collect data about one person, event, transaction from different sources. For example, when ordering goods, the customer specifies the delivery address, bank account number, first name, last name, phone number, and more. If these data cannot be linked together, then delays in the work of the company will begin and the likelihood of errors will increase (the seller will send the goods to another client, deliver the order to the neighboring district of the city, etc.).
7. Relevance. An important component of quality information is its relevance. It characterizes the degree of compliance of the data with the current needs of the business. An example of irrelevant information is data from the history of the formation of the company, which does not have any impact on the analysis of future income required by the business.
8. Reliability. This measure of information quality is complex. It simultaneously depends on the completeness and accuracy of the data. Therefore, to ensure sufficient reliability, it is necessary to collect all the data that are of interest to the business and check them for compliance with the real state of affairs. Only in the case of a positive assessment of these parameters, the information can be considered reliable. An example is customer data that does not have any gaps and is repeatedly checked for validity.

In the modern world, any information is the most valuable capital. With it, small and large companies can get a lot of benefits for themselves and avoid unwanted mistakes. Moreover, any data must be of high quality and reliable. Only in this case they will become the basis for the future success of the company and help it conquer new heights.