Statistics released by Cisco show that global Internet traffic is going to hit 3.3 zettabytes per year by 2021. (How big is a zettabyte? It's this big.) This is no doubt a staggering number, but it makes sense considering how much data companies are currently storing. For that reason, effective data management is a must. However, most companies are unable to overcome key data management challenges, such as data retention, dark data, access, and data integration. To rectify this situation, companies require help, and this help is available in the form of machine learning and artificial intelligence.
But first, we need to look at the data management problems encountered by IT departments. For starters, companies are ill-equipped to handle the vast amounts of unstructured data that comes their way daily. In the end, they simply staff the data somewhere, which is not only reckless but unethical as well. Moreover, the individuals in charge of business decisions prefer not to discard data. The lack of attention on data retention policies is another problematic aspect.
Every business wants rapid data access, but considering the cost of high speed storage in the cloud or on-premises, companies choose to archive a chunk of their data using cheaper, slower means of storage. As a result, when serious issues occur, the company has to assign staff members to handle the projects, which detracts from core business goals.
Role of machine learning and AI in data management
Unstructured data is a major reason why data management presents such difficulties for businesses. However, artificial intelligence, analytics, and machine learning can help overcome this problem.
Sort through data quickly
A company accumulates huge quantities of dark data, a lot of which people are entirely unaware of. However, AI and analytics can use machine learning to mine data more easily. Together, these systems can harness the power of algorithms to sort through various types of documents, emails, images, videos, etc. — all of which are stored on the servers. All that’s left to do then is for an expert to review the data classification recommendations of the automated process, tweak it if necessary, and implement it into the business. A significant portion of this process also deals with the problem of data retention. The analytics help produce a series of recommendations that allow data to be purged from files.
Identifying disposable data
Analytics, AI, and machine learning are able to identify data that is rarely or never used in an objective manner. However, the technology is not as discerning as the employees of a company. For example, it is possible for these processes to identify which records or data have not been accessed in the past five years. This way, they enable you to root out data that might technically be obsolete. How does this help the company? Well, it saves employees the hassle of hunting down such potentially obsolete data; they can rely on the process to get the job done instead. But they still have to decide whether there is any reason to retain this data.
Efficient grouping of data
Analytics developers are often tasked with the responsibility of determining what sort of data they need to collect for queries. However, more often than not during this process, they tend to create a repository for this kind of application. They then put the repository to good use by drawing in different sorts of data from diverse sources, thereby producing what is known as an analytics data pool. But before they can complete this step, they are required to come up with integration strategies so they can access the various sources from which they draw data. While it’s true that this is still a highly manual procedure, machine learning can increase its efficiency through the automatic development of “mappings” between the data repository of the application and the data sources. What this does is decrease the integration as well as aggregation times significantly.
Assistance with data storage organization for improved access
In the course of the last five years, a lot of data storage service vendors have made considerable leeway into the process of automating storage management. All this has been made possible by the advancement and widespread usage of solid state storage technology at reduced prices. Owing to this, IT teams no longer have to think twice about employing some sort of “smart” storage engine. This sort of technology is quite effective because it makes use of machine learning to understand commonly used data. It also helps businesses figure out which data is rarely or never used. The process of automation comes in handy here, because it can be used for automatically storing data in slow or fast storage, depending upon the business rules established by the machine’s algorithms. This level of automation is helpful for storage managers because it helps them overcome the trouble of manual optimization of storage.
There is no getting around the fact that the process of data management — no matter how easy it seems — can pose a problem for IT departments if not handled correctly. The worst part is that the situation is only going downhill from here as more data continues to stream in on a daily basis. So, any options for resolution become bleaker with each passing day.
Communicate the problem — and the solutions
It is essential that data architects, CIOs, and those responsible for storage management understand and present the gravity of the situation to the C-level “chiefs,” typically the chief executive officer, chief operations officer, and chief financial officer. But due to the complications associated with data management projects, they are not really an easy idea to sell to the higher-ups. Still, by indicating the importance of quicker times for marketing analytics as well as the projected storage cost reductions, IT managers have a shot at getting their points across in C-level discussions regarding ways to improve strategic ability.