5 Areas Where AI Can Help Resolve Data Management Issues in IT
In this article, We describe about some areas where AI can help resolve data management issues in IT
Handling a huge amount of data is a big challenge for IT departments. In this article we will learn how Machine Learning and AI is of great help when it comes to sorting, organizing and aggregating a great amount of information. To resolve data management issues in IT companies, AI or Artificial Intelligence rules the roost now. As per reports from Samsung, The global internet traffic has crossed one zettabyte, i.e. 1 million terabytes, in the year 2016. This is indeed a big figure but is still not enough to represent the huge amount of data that the organizations are having to deal with.
The main areas where management of data becomes a challenge are:
- Retaining data
- Integrating the data for best analytics results.
- Understanding dark data
- Access to data
The main reasons for the struggle are as follows:
The collected data is of such huge variety and is unstructured to such an extent, that the companies are having to put the data anywhere.
The companies are still confused as to how much historical data, the audit and the legal processes will demand. This is a debatable matter and the companies often do not want to discard data for this purpose.
Every business demands fast access to data. Storage with high speed on the cloud or the premises is quite an expensive process, most of the times. Thus, some data should be archived off for cheap and slow storage. To look after all these problems, often the company diverts from the main issues.
Integration of data is a very tough task for IT companies. As aggregation of data is playing a big role now, often dissimilar sets of data are put together into searchable repositories for newer kinds of queries.
Ways in which AI can help
It helps in quicker sorting of data:
As discussed, an IT company accumulates a huge amount of dark data. Most of us are even unaware of it. AI and analytics use machine learning to get hold of this data more easily. They mine these data and use the power of algorithms to sort through the different types of documents, images, emails and videos. All these are stored in the servers and just need an expert’s review to classify these data. There is also a need to tweak the data if the situation arises and apply to the business process, as per the recommendations of the automated process. Retaining data is the next big issue. Analytics helps in doing all these from the big amount of data that is stored in the servers.
Efficient grouping of data:
The analytics developers have the main duty of identifying the data which will be needed for queries. They often, create a repository for this kind of application. These repositories are put to best use by collecting data from a huge number of source. This is also known as analytics data pool. For drawing out data from various sources, the analysts have to come up with various strategies of integration. Though this process is highly manual, machine learning or ML is capable of increasing efficiency. The automatic development of mappings between the sources of data and data repository of the application helps in both integrating as well as aggregating.
Assistance with storage of data for better access:
For the last five years, the data storage vendors have been successfully automating storage management. This has been made possible with the help of solid-state storage technology at very less prices. This technology is quite effective as it employs machine learning to understand the data which is commonly used. It also helps the businesses to figure out the data which has been rarely or never used. The process of automation is helpful in this regard as it can be used for automatic storage of data, depending on whether it is fast or slow storage depending on the rules of the business established by the algorithms of the business.
Identification of the disposable data:
A very good use to which AI or ML can be put in the identification of data that is rarely or never used in a very objective way. It is possible for the processes to identify the data or records that have not been used at least for the last five years. This helps in rooting out of the data that are technically obsolete. It helps the employees in manually searching for these data and discarding them.
Communicating the problem and the solutions:
It is very important that the CIOs, the data architects and everyone responsible for storage management understand the importance of data storage, management, retention and other data management services. But, often due to the complexities associated, it is really not a very easy idea. Organizing the data for best access for them, Artificial Intelligence is the best tool.
AI and ML are the latest boons to the IT sector when the question is to resolve data management issues. Data is precious and every company should know how to deal with it to access to its numerous advantages.