Problems with data management? How AI and machine learning can solve one of the biggest problems.

Statistics published by Cisco show that global Internet traffic will reach 3.3 zettabytes per year by 2021. (How big is the zettabyte? Believe me, this unit of measure for the amount of information is simply huge.) This is undoubtedly a bewildering figure, but it is completely justified considering the amount of data companies currently store. Therefore, effective data management is imperative. However, most companies are not able to overcome the main problems in data management, such as data storage, dark data, access and data integration. To remedy this situation, companies need specialized assistance, and this assistance is available in the form of machine learning and artificial intelligence.

But, for starters, we need to look at the data management challenges faced by IT departments. First, companies are poorly equipped to process a huge amount of unstructured data that is received daily. And in the end, they simply personalize the data somewhere, which is not only foolish, but also unethical. Moreover, business decision makers prefer not to discard data. Another problematic aspect is the lack of attention to data storage policies.


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Each organization wants to get quick access to data, but given the cost of high-speed cloud storage, companies prefer to archive some of their data using cheaper and slower storage media. As a result, when serious problems arise, enterprises have to hire employees to eliminate these problems and implement projects, which naturally diverts attention from the main business goals.

The role of machine learning and AI in data management

Unstructured data is the main reason for creating data management problems in companies. However, artificial intelligence, analytics, and machine learning can help overcome these problems.

Quick data sorting

The company accumulates a huge amount of dark data, which people do not even realize. However, AI and analytics can use machine learning to more easily get data. Together, these systems can use the capabilities of algorithms to sort various types of documents, emails, images, videos, etc. — all of them are stored on servers . All that remains to be done is to give the expert the opportunity to analyze recommendations for classifying data in an automated process, and, if necessary, adjust it and implement it in a business strategy. Much of this process is also related to the problem of data storage. Analytics helps prepare a series of recommendations that allow you to delete data from files.

Identification of one-time data

Analytics, AI, and machine learning can identify data that will rarely or never be used objectively. However, these technologies are not as demanding as company employees. For example, these processes identify which records or data have not been available in the past five years. Thus, they allow you to delete data that technically may be outdated. How does this help the company? Firstly, it saves staff time and saves them from unnecessary employment to search for such potentially obsolete data; secondly, they can rely on an automated process to carry out their primary tasks. But the final decision must come from the staff, whether to store the identified data or not.

Efficient data grouping

Computer system analysts are often responsible for determining what data they should collect for queries. However, most often during this process, they tend to create a repository for this type of application. Then they put into the repository various types of data from different sources, thereby creating a so-called pool of analytical systems. But before they can complete this step, they need to develop integration strategies in order to gain access to the various sources from which they derive data. Although it is worth noting that this procedure is still largely carried out manually now, machine learning can improve the efficiency of the process by automatically developing “mappings” between the application data repository and data sources. This leads to a significant reduction in integration and aggregation time.

Assistance in organizing data storage to improve access to it.

Over the past five years, many storage service providers have made significant progress in automating storage management. All this was made possible thanks to the improvement and widespread use of solid-state drives at discounted prices. Because of this, IT departments no longer need to think twice about using some kind of “smart” mechanism for storing data. This technology is very effective because it uses machine learning to understand frequently used data. It also helps companies figure out which data is rarely or not at all. The automation process is convenient here, since it can be used to automatically store data in slow or fast mode, depending on the business requirements set by the machine algorithms. This level of automation is very useful for employees, as it helps them to speed up the work process and move away from manual storage optimization.

But it is also impossible to get around the fact that the data management process - no matter how easy it seems - can create problems for IT departments if the data is not processed correctly. Worst of all, the situation will only get worse as more and more data continues to flow daily. Thus, every day any solutions to this problem become more complex.

Report problems and how to solve them.

It is very important that data architects, IT directors and those responsible for managing the storage understand the seriousness of the situation and report information to high-ranking company officials, as a rule, they are the chief executive officer, the chief operating officer and the chief financial officer. But due to the complexities associated with data management projects, this strategy is not so easy to implement. Nevertheless, pointing out the importance of marketing analytics, as well as projected cuts in data storage costs, IT managers have the opportunity to discuss these issues with company directors on ways to improve strategic decisions.