Srikanth
Managing information is a troublesome work we all agree and most of us struggle to do it efficiently. Organizations vary in attitude when it comes to their employees understanding about managing information. Some still think that their employees need more training to understand what information should be kept and which one is not for the record, others are more flexible. In all practicality, organizations vary in their state of maturity when it comes to information management and governance issue.
ML and Search Technology may help in better information management
Implementing advanced technology without taking into account the people and the process cannot solve business problems. The businesses need to bring together the people, processes and the technology on a common thread. However, technology like Machine Learning may help us in searching, managing and discovering information.
For example, if everyone in a business is on a homogenous environment like Microsoft Office 365 environment then it is easier to build a connection using Microsoft AI technology, Office Graph or SharePoint and having a perfectly knit information governance in place.
However, the problems begin when different units of a business are working in a different environment. Here improved AI with improved Machine learning ability comes into play.
It can sort through dark data
Every business has chest of accumulated data that people do not know anything about. Using machine learning in combination with its algorithmic power can sort and handle all different kinds of emails, documents, and images etc. which are stored in servers. Machine learning, AI and analytics together can work on this unused data and do a pre-sorting. Someone capable then goes through these sorted data and review the recommendations given by the machine and perform necessary tasks.
Help to decide what to keep and throw away
Machine learning can objectively identify data which have not been used for a long time and give the recommendation to throw it away. This could save loads of time for the employees because once identified, they only need to decide on keeping or throwing the data.
Help in aggregating data
During the process of determining the kind of data needed for aggregating queries, data analytics developer most of the time build a storehouse for the application and then pull out various kinds of data from different sources to finally make up the pool for analytics data. This is still a manual process and developer needs to build up integration methods to access diverse sources from which they need to pull up data. Machine learning can help it done more efficiently by automatically “mapping” between data resource and data storehouse. This minimizes the time to complete the whole process to a considerable degree.
Efficient organization of data storage for best access
In the last few years, data storage sellers have made considerable improvement in automated storage management, which has mainly been possible for low-cost solid-state storages. Technological advances on this front have enabled IT people of the organization to use “smart” storage engines that utilize machine learning to analyse the frequency of usage of a particular data. Machine algorithms feed into the system also enables data to store either in fast or slow storage space depending on their use.
