Modern life is inextricably linked with endless streams of information. It is not always of high quality and useful. To eliminate this shortcoming, you should use data analytics. It will make it possible to sort information and reject all unnecessary. The result will be obtaining high-quality data that will be useful not only in business but also in everyday life. 52g4q
What is data analytics? 5f606d
Optimizing data quality is an important part of the success of any company. This allows you to operate with accurate, unique, and reliable information that helps make the right business decisions. This is what data analytics does. This direction provides for the extraction of certain information needed by the business from the general array. It helps to analyze the current situation, take measures to improve it, and make plans for the future.
Data analytics is a complex area. It is based on many different areas of knowledge that allow you to analyze the available information. Specialists working in this direction apply a variety of techniques and involve artificial intelligence in their work. This approach makes it possible to carry out dozens of data manipulations and maximize their quality.
How to measure data quality? 683bx
Nowadays, the traditional way of working is used to measure data quality. It involves the evaluation of the information received and its comparison with standards. The latter can be general or self-installed. In the second case, they will be more rigid, which will make the data of the highest quality. The measurement process is complex and multi-stage. It is characterized by checking information for compliance with the following quality criteria:
1. Accuracy. An important part of any data analysis is to check for accuracy. This characteristic shows the correspondence of this or that information to reality. Verification is carried out by analyzing the collected data and studying it in several reliable sources. The final result makes it possible to see the true state of affairs and make decisions based on the current situation.
2. Relevance. In most cases, the information has an expiration date. Because of this, data that was useful yesterday will become useless tomorrow. To prevent this from happening, the information is checked for relevance. It characterizes the correspondence of data to a certain point in time. Checking by this criterion eliminates the possibility of getting into the array of outdated and irrelevant, at the moment, information.
3. Uniqueness. Information will be of little use if it is non-unique. If it is used, the risk of making wrong decisions that reduce income and damage the company’s reputation will increase. The uniqueness of the data ensures that there are no identical values in any other sources. Thanks to this, it is possible to simplify identification and reduce the risk of data spoofing.
4. Completeness. When working with large datasets, it is important that they are complete. This characteristic is understood as the presence of absolutely all information, some of which may not be used at all. In addition, completeness makes it possible to find any data of interest without additional manipulations.
5. Compliance. Information will be of high quality if it meets the needs of the business. Therefore, it is important that the data used covers only those aspects that are of interest to the company (for example, the number of sales of a particular product). If the information provided belongs to another category, then it will be useless.
6. Relationship. When measuring data quality, do not forget about such an indicator as the relationship. It is especially important in cases where the information being prepared relates to working with clients. Thanks to him, it is possible to establish a relationship between a person’s name and his personal data, thereby simplifying the identification process and allowing you to quickly receive all the necessary information to perform certain actions (for example, placing an order, conducting a transaction, etc.).
Very often, additional criteria are used to measure data quality. All of them allow you to give information about certain properties that meet the needs of the business. The more such verification criteria there are, the better the information will be. However, in this case, the time of data preparation will increase and the process of their analysis will become more complicated.
All collected materials are checked for problems. They have a negative impact on the quality of information, making it a less valuable resource.
Problems with poor quality data:
1. es. Many experts consider this lack of information to be the main one. It is characterized by the absence of certain data that may be important for business. Such omissions noticeably worsen the quality of the selected material and make its use inexpedient.
2. Copies. Information is repeatedly copied and transformed in various ways. Because of this, duplicates often occur, which greatly reduce the quality of the data array. The presence of such a problem is an unpleasant surprise. It negatively affects many aspects and creates many problems for businesses.
3. Contradictions. The same information in dozens of sources can be different. Such contradictions force additional checks and analyses of a large amount of data. This entails certain financial and reputational losses for the business.
4. Anomalies. It is impossible to move faster than the speed of light. A similar rule is typical for an array of qualitative data. It cannot contain values that would exceed the maximum limits and contradict common sense. However, such data is sometimes still found. Because of them, you have to carry out additional analysis of information and spend precious time deleting them.
5. Incorrect formats. Different countries around the world use their own data formats. Therefore, in the case of collecting information from a large number of sources, inconsistencies arise. Getting rid of them is a real problem, which takes a lot of time and effort.
A huge amount of information can confuse business owners, causing them to be guided by poor quality information and make the wrong decisions. To select and sort it, data analytics is used. This process is complex and costly from all points of view. At the same time, it allows you to get valuable data that will become the basis for the rapid development and prosperity of your business.