What Are Data Quality Metrics?

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In any field of activity it is impossible to do without a large amount of information. It can be expressed as letters or numbers, or as text. In all cases, you need to be able to measure its quality.

Thanks to this, it will be possible to determine the degree of usefulness of the data and the possibility of using it to perform certain tasks. To cope with this work, you need to use data quality metrics. They help specialists to separate high-quality information from low-quality information.

Importance Of Using Metrics For Data QA

Not all business owners agree to evaluate the quality of data according to generally accepted metrics. This leads to various consequences that negatively affect the work of the entire company or its individual divisions.

Reasons for using metrics in the process of measuring data quality:

  1. Risk reduction. Information verified using data quality metrics will enable businesses to avoid making spontaneous decisions. This will minimize the risks and positively affect the future income of the company.

  2. Expansion of the client base. Properly selected data will become a qualitative basis for determining the target audience, as well as studying its wishes and individual preferences. This approach will allow you to get enough information to resolve any disagreements with existing customers and get the attention of other people.

  3. Reduced competition. Very often, competitors are the biggest problem for businesses. To reduce their impact, it is necessary to use only data that has been verified against key metrics. This will help to quickly adapt to any changes in the global market and leave behind most of the competitors.

  4. Improving the image. The use of advanced methods of working with data automatically raises the status of any company. This increases the trust of customers and creates the image of a reliable partner with whom it is pleasant to deal with the company.

  5. Increasing income. The use of data quality metrics provides access to information that can be used for economic benefit. In this case, the company will be able to work more efficiently with customers, solve problems that reduce profits faster, and make plans in advance for several months/years ahead.
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Key Indicators Of Data Quality

It is important for businesses that all data used is of high quality. In this case, they can be used at work without fear of any negative consequences. To determine the quality level, basic and auxiliary metrics are used. Compliance with each of them endows the information with special properties inherent only in qualitative data.

Key indicators:

  1. Uniqueness. In most cases, it is the main indicator of data quality. Thanks to it, it is possible not only to operate with data that is not found anywhere else, but also to identify a specific array. A good example of data uniqueness is a bank account number. It consists of a set of numbers that do not repeat anywhere in the same sequence. The same is true for a person’s phone number.

  2. Completeness. Many experts consider completeness the most important indicator. It is far from always such, but in most cases it is included in the list of mandatory criteria for verifying information. Completeness means the presence of absolutely all data about an event or a client.

    A space in one of the paragraphs will automatically make the data incomplete. As an example of such a situation, personal data of a person is often cited, in which there is no information (for example, name, year of birth, bank account number, etc.).

  3. Accuracy. Accuracy is one of the key indicators. It characterizes the ability of information to correspond to the real state of affairs. This parameter is taken into account in all cases when data arrays of any size are processed. An example of accurate information is a person’s birthday. It remains unchanged regardless of any factors.

  4. Relevance. Relevance is an important characteristic of data quality. It will be endowed only with information that is suitable for use at one time or another. A striking example of relevance is information about the number of sales of a particular product per unit of time (for example, per month). After 30 days, new information will appear, and the previous one will become irrelevant.

  5. Relationship. An important ability of qualitative data is the ability to associate it with information located in other arrays. In this case, it will simplify the process of identifying customers or collecting a history of certain events (for example, transactions made).

    An example of a correct relationship is the data provided by the customer during the purchase of goods in the online store. In the future, they can be linked to other orders made by the same person.

  6. Compliance. The absence of contradictions characterizes qualitative data. In this regard, information about a particular person or event must be relevant in all sources used. Any inaccuracies will reduce the quality of the entire data set. An example of a match is the customer’s phone number. It will match the same set of numbers taken from any other source.

  7. Relevance. Information will be useful if it meets the requirements of its owner. In this case, it will be possible to fully use it to perform the assigned tasks. A prime example of irrelevant data is a list of product characteristics that have no bearing on the business’s required analysis of the financial benefit from its sale.

  8. Reliability. Often, experts separately consider this indicator. It characterizes the ability of data to be both accurate and complete. This combination makes the information reliable and suitable for any purpose. A good example of reliable data is a correctly entered date of birth of a person. It does not contain any inaccuracies or gaps.
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Possible Problems In Measuring Information Quality

Compliance with data QA metrics information does not guarantee 100% quality. To achieve this, it is necessary to rid the overall array of various minor problems. If this is not done, then the data obtained as a result of the work done will become less useful for the business.

Possible problems:

  1. Duplicates. This problem is common. It arises due to the identity of information in various sources and the impossibility of its removal even at the stage of collection. Duplicates negatively affect the quality of the entire array and require additional data cleaning measures.

  2. Passes. This shortcoming complicates the process of using small and large data arrays. It is characterized by the presence of gaps in certain information (for example, about a client or some action), which makes it difficult to get a complete picture of a person or event.

  3. Anomalies. If the data goes beyond the established limits, then it becomes anomalous. Such information negatively affects the overall quality and complicates the process of further use of the data.

  4. Contradictions. The presence of such a problem is often caused by the low reliability of the sources of information used. The contradictions encountered in the arrays reduce the quality of the data and force specialists to carry out additional checks. This increases financial and time costs, which negatively affects the overall state of the business.

  5. Non-standard formats. The use of multiple sources can lead to inconsistencies in the presentation of certain information (for example, date of birth or time of transactions). In this case, the quality of the data drops sharply, with all the resulting negative consequences for the business.

Information surrounds us from all sides. Not always it is of high quality and useful for use. To determine the level of its quality, special data quality metrics are used.

With their help, arrays of information are removed from everything superfluous, leaving only the most relevant data. With the right approach to business, the latter will be an excellent basis for making important decisions that affect the operation of the business.

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