Problems managing data? How AI and machine learning can solve one of the biggest challenges

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 measurement of the amount of information is simply huge.) This is undoubtedly a staggering figure, but it is completely justified given how much data is currently stored by companies. Therefore, effective data management is a must. However, most companies are not able to overcome the basic problems in data management, such as data storage, dark data, access and data integration. To remedy this situation, companies need specialized help, and this help is available in the form of machine learning and artificial intelligence.

But, for starters, we need to consider the data management issues that IT departments face. First, companies are poorly equipped to process the huge amount of unstructured data that arrives daily. And ultimately, they just personalize the data somewhere, which is not only reckless, but also unethical. Moreover, business decision makers choose not to discard data. Another problematic aspect is the lack of attention to data storage policies.



Every organization wants quick access to data, but given the cost of high-speed cloud storage, companies prefer to back up some of their data with cheaper and slower storage. As a result, when serious problems arise, enterprises have to hire employees to eliminate these problems and implement projects, which, of course, distracts attention from the main business goals.

The role of machine learning and AI in data management

Unstructured data is the main cause of 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 that people do not even know about. However, AI and analytics can use machine learning to more easily retrieve data. Together, these systems can use the capabilities of algorithms to sort various types of documents, emails, images, videos, etc. - they are all 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, configure it and implement it in business strategies. A significant part of this process is also associated with the problem of data storage. Analytics helps prepare a series of recommendations to remove data from files.

One-time data identification

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

Effective data grouping

Computer system analysts are often responsible for determining what data they should collect for queries. However, most often during this process they usually create a repositoryfor this type of application. Then they put various types of data from different sources into the repository, thereby creating the so-called pool of analytical systems. But before they can complete this step, they need to develop integration strategies to gain access to the various sources from which they draw data. Although it is worth noting that this procedure is still largely carried out manually, machine learning can increase 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.

Help organize data storage to improve access to it

Over the past five years, many storage service providers have made significant advances in automating storage management. All this has been made possible thanks to the improvement and widespread use of SSDs.at discount prices. Thanks to 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 commonly used data. It also helps companies figure out which data is rarely or not used at all. The automation process is convenient here, as it can be used to automatically store data in slow or fast mode, depending on the business requirements established by the machine algorithms. This level of automation is very useful for employees, as it helps them speed up the process and move away from manual storage optimization.

But it’s impossible to circumvent 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.

It is necessary to report problems and solutions.

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