For a present-day business, data is a vital asset. Even small companies collect, store, analyze them to use in making strategic decisions. This process requires a lot of effort and money. Therefore, it is very important that the result is good. And this is only possible if your data quality is high.
Let’s look at what factors affect data quality. Specialists in business intelligence (BI) highlight the following parameters as priority:
- Credibility. When data is entered into tables, it is important to ensure that the same dimension is used, the correct values. For example, length can be measured in metric and imperial units. And one characteristic in a text format can correspond to many formally different meanings denoting the same thing. All this reduces the data quality, and in some cases makes them unsuitable for further use.
- Completeness. Several fields are used to describe each object. If we are talking about high quality data, then all of them must be filled.
- Inheritance. Data can flow from one set to another. And in each of them the same data must be identical. Therefore, it is important for data quality to follow the origin of the tables.
- Uniqueness. Duplicates are extra storage costs and an additional source of errors.
- Relevance. Data has an expiration date. Irrelevant ones should be disposed of in a timely manner.
If we are talking about big data, automated solutions are needed to monitor it.
Masthead offers the observability platform for data monitoring based on ML algorithm
This solution is suitable for business, science and other application areas that use big data. Its advantages are:
- The data monitoring in real time on the whole scale.
- Security – the algorithm uses logs, but does not have access to the data itself.
- Messages come immediately to the selected communication channel.
- You set trigger thresholds, tables priority.
- Set up takes less than 20 minutes – this is a zero code tool.
Follow the link https://mastheadata.com/ to the Masthead website to learn how you can improve your data quality today.
