Best Data Quality Tools

The rapid increase in the volume of incoming information complicates the process of its processing to the maximum. Very often this becomes a real problem, because without this work it will be impossible to obtain high quality information. To remedy the situation, you need to use special data QA tools. They will speed up the process of analyzing and processing data, as well as help in managing them.

Features of using data QA tools

Automation of analytical processes forces specialists to use data quality tools. They are programs that help manage data and monitor its quality. When used correctly, it will be possible to get rid of any inconsistencies and quickly fit the available information to the established criteria. The tools that are used to ensure high quality information have their own characteristics. Knowing them, you can operate such software as efficiently as possible and achieve your business goals.

Data QA tools are capable of performing several important tasks. These include cleaning information from everything superfluous, its integration and management. The capabilities of such programs are enough to identify any anomalies and get the result of the desired quality. Nowadays modern tools are used. All of them are simple, but they allow you to perform the most complex tasks. Thanks to this, specialists manage to create perfect data arrays of the highest quality.

Best data quality tools

There are many tools available to help ensure high quality data. All of them differ from each other in characteristics, functions and purpose. All this complicates the choice of the appropriate option a little, but allows you to find the optimal program for solving the problem.

Best data QA tools:

1. Informatica. The leader among the most popular tools is Informatica. It opens up access to a large number of useful functions that help analyze the data and remove everything unnecessary from the general array. The program can check addresses, find duplicates, remove errors and integrate data. Informatica can also monitor data quality in real time. The only drawback of this tool is its resource consumption.
2. IBM InfoSphere. This American software tool is popular in many countries around the world. It has an extensive client base, including many well-known global companies. This product effectively copes with a variety of tasks. Most often, it is used to monitor data quality. The main disadvantage of the tool is the difficulty of integrating it with other products. This creates certain difficulties that some users do not like.
3. SAP. Software from a well-known German corporation has a wide customer base not only in Europe but also on other continents. This was made possible due to the efficiency of the tool and its simplicity. Most often, SAP is used to plan and develop development strategies. It provides specialists with high-quality data that does not require additional analysis. Among the minuses of the program, there is a not very high speed of processing large amounts of information and a rather large resource intensity.
4. S.A.S. More than 2.5 thousand customers have evaluated the SAS software tool. Among them there are large companies that are known all over the world. The main distinguishing feature of the program is the ease of interaction with other tools. This makes it possible to quickly perform a large number of tasks and effectively manage data quality. SAS is ideal for statistical analysis. It also does a good job of managing metadata. The disadvantage of this tool is its large volume and slow execution of some operations.
5. 5 Talend. This tool is gradually gaining popularity and becoming one of the most popular in the world. It is characterized by open source code, thanks to which it allows you to achieve deep integration with various sources of information. Talend is suitable for a variety of tasks. It also performs well when working with data that changes in real time. The disadvantage of this product is a non-standard user code, which is why working with the tool requires certain knowledge and skills.

5. Ataccama One. This tool is great for getting high quality data. It offers users advanced functionality, thanks to which it is possible to perform many different jobs. Among other features of Ataccama One, users note the ease of managing metadata and the ability to quickly work with large amounts of information. The only negative point is the difficulty of identifying errors. This makes it difficult for users to pinpoint the exact cause of a given data problem.
6. Innovative Systems. This tool is often used by small companies. At the same time, it is also suitable for large companies that occupy leading positions in the world rankings. The product offers users a wide range of services. It can be used to standardize, profile, analyze and clean data. Innovative Systems is also suitable for monitoring the quality of information. The non-standard interface is the only negative point. Because of this, specialists need some time to adapt to it.
7. 8 Oracle. This modern tool perfectly copes with tasks of increased complexity. It allows you to set strict criteria for selecting information, which makes it possible to obtain data of the highest quality. An important advantage of the program is a simple interface. Experts also praise Oracle for its versatility, which no other tool can boast of. The downside of the program is only a slightly limited export of information.

Selection tips

The process of choosing software tools for data analysis and quality management is the most important stage of work. To find the right one from the many available options, you should take into account several nuances, as well as take into account the recommendations of specialists.

Helpful tips for choosing:

1. Before making a choice in favor of a particular tool, decide on the problems that you need to solve. In most cases, available programs are capable of performing only a certain set of tasks (for example, searching for duplicates, errors, missing information, etc.), which is why they are not recommended to be used as universal tools. In this regard, carefully study the purpose of the tool in question, and only then make your choice.
2. Each data QA tool has its own set of features and functions. They must be studied before making a choice of a particular program. This approach will make the specialist more knowledgeable and will allow you to find the most suitable option for your particular case.
3. Always use only modern tools. They are well adapted to today’s realities, as well as more perfect and multifunctional. Outdated programs should also not be forgotten. They can be useful for performing the simplest tasks (for example, preliminary cleaning of information from gross errors or pronounced inconsistencies).
4. Each tool available to a specialist has its own strengths. Knowing them in advance, it will be easy and in the shortest possible time to choose the best tool for performing a particular job. If you need to solve several problems at the same time, then you should give preference to programs that can effectively cope with most of these tasks.
5. Do not forget about the shortcomings of the considered tools. Each of them has them, which is why this or that program will be ineffective when doing a certain job. In order not to face such a situation, you need to carefully study the negative points and determine the degree of their impact on the work performed.
6. Only licensed tools must be used. They are able to work effectively and help specialists solve problems of varying degrees of complexity. In addition, their operation will be as safe as possible in terms of the safety of the processed data (information will not be available to anyone else).

The process of data quality management is impossible without the use of special software tools. They will simplify the work of specialists and help to achieve the desired results in the shortest possible period of time. The correct use of such tools will positively affect the quality of the data used, which will become the basis for making important business decisions.