Data is the core activity in every sector, but businesses fail to recognize its true value and keep on neglecting this process. You know big companies like Google Flu failed in the initial year due to poor data management that aimed to provide predicting flu outbreaks in about 25 countries. At 2013, it completely failed at a whopping ratio of 140%. Why? It’s obviously improper data management that didn’t allow the algorithm to work properly.
There is no doubt that humans are prone to errors and the staggering number is very common if a company generates chunks of data in their daily routine. Therefore, effective data management is a must for every business sector.
However, numerous businesses are incompetent to conquer important data management challenges that include dark data, data retention, data integration, and poor data access. After so many attempts, if you are seeking help to streamline your data management process, embrace smart technologies.
Today everyone is talking about AI and machine learning. Firstly, let us look at the data management problems stumbled upon by IT departments of businesses.
Usually, businesses are not well-equipped to handle an enormous amount of unstructured data that comes their way on a daily basis. In the end, they divide the data work among staff members, which is a rash and immoral behavior of the organization towards their employees.
As a result, it deviates the staff to focus on core business aspects. Moreover, the lack of focus on data retention procedures is another challenging aspect.
Every business wants faster access to data, but since the cost of high-speed storage in the cloud or on-premises is way too high, organizations opt to store their mass data using inexpensive, inefficient means of storage.
Let’s take a look at the Role of AI and Machine learning:
Role of AI and machine learning in data management
Unstructured data is a key reason why data management replicates the problems mentioned above for businesses. Nevertheless, analytics, machine learning, and artificial intelligence can help to overcome these problems. Learn how?
Scrutinize data quickly – Every company gathers huge amounts of dark data, a lot of which businesses are completely unaware of. However, using machine learning can help you mine your data more efficiently. You can harness the power of algorithms utilizing this system to scrutinize various types of documents, images, emails, videos, etc., which are stored on servers.
So what’s left to do? Creating an expert review on the sorting out data and recommendations of the automated process, embracing if necessary, and implementing the same into the business. An important part of this process also deals with the problem of data retention. The analytics help to crop up a sequence of recommendations that allow data to be expelled from files.
Detecting disposable data – AI, machine learning, and analytics are capable of identifying data that is hardly or never used in an effective manner by an organization. Although, the technology is not as sharp as the employees of a company.
For instance, it is surely possible for these technologies to identify, which records or data have not been accessed in the past five years. Thus, it accumulates data that might be officially outdated. How have companies benefited from it?
Well, it saves employees from the difficulty of searching these potentially outdated data; instead, they can depend on the process to accomplish their tasks. Still, employees need to understand, if is there any goal to retain this data.
Competent alignment of data – The analytics developers are often responsible for defining what type of data they need to collect for queries. But, during this process, they end up creating a warehouse for this sort of application.
Developers can make the best of warehouse data by accumulating different types of data from varied sources, which is referred to as an analytical data pool. However, developers can accomplish this step only when they have the right integration strategy to ease access to data sources.
Unfortunately, this process is highly manual, but machine learning can improve its proficiency through the automatic improvement of “mappings” between the data warehouse of the application and the data sources. It significantly saves time spent on integration and aggregation.
How effective data storage can improve the overall accessibility across the organization?
Over a decade, automating data storage management has enabled businesses to have easy access that too at a considerably low price. Thus, teams across the businesses no longer have to think thrice about embracing intelligent storage engines.
These technologies are quite effective because it understand commonly used data well. It assists businesses with data, that is never or merely used by the firm. The automation process with these technologies comes conveniently here as it is also used for storing data in slow or fast storage, depending upon the business’s established standards. This high level of automation allows the storage manager to take a breath of relief from manual optimization of storage.
There’s no doubt that an effective data entry system can pose numerous problems for businesses if not processed correctly. Thus, make use of efficient technologies that can help you streamline your data management process entirely, give easy accessibility, and allow you to stay focused on core business operations by maintaining a smooth efficiency within the process.
To understand how effective data storage can revamp data management, learn more about SAN Storage Solutions by visiting this website and discover the key to transforming your data management strategies.