Today, there are an incredible number of challenges around the world across the industry domain that can solve by providing the right training data – sample — to the right machine learning algorithms. Great thanks to the latest developments in Machine Learning algorithms.
If we look at the basics of machine learning, the perspective of handling data is a way different to computers when compared to humans. The process is fast, accurate and flexible with computers. Data management is not a separate industry sector; it’s an integral part of each and every organization. And need to be handled with high priority.
Machine learning continuously evolving over a period of time which enables to handle the data to get the best use of it across the industries. Sometimes data management becomes more important than algorithms to drive the solutions. It is said in Forbes publication, enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
We can picture the use of ML in various fields of data management, a resource that enhance the business benefits in several industries.
Sorting through dark data:
To sort and handle different types of emails, documents and images stored on different servers, machine learning, and its combined algorithm power will be helpful.
Deciding which data to cutoff:
AI, machine learning, and analytics can systematically identify the seldom data and indicates that data is obsolete. Which can take the maximum time of employers.
Aggregation of data:
Sometimes there is a need for aggregation of data for queries, and it needs integration to access the data from different sources. But using machine learning, it makes the process so efficient by automatic mapping between the sources and data repository application.
Organized data storage system for best access:
There are different kinds of data such as most used, seldom and never used data. IT departments to use “smart” storage engines which uses machine learning algorithms to classify different types of data. This eliminates the concept of manual address storage optimization.
Managing healthcare data:
The decline of archaic analytics to extract insights from images, EHR system reports and voice recordings, made a way for ML to extract meaning from all these diverse data sources with its powerful algorithms. Clinical data analysis and imaging technologies are new bloom in health care.
Machine learning in finance data management:
The two main purposes for the adoption of ML in finance and banking sector are to extract customer intelligence and lifetime value of a customer from data and for fraud detection. Machine learning algorithms can grab the customer’s financial history and analyze the market aspects. Today, the entire financial industry is working on multiple machine learning projects to understand Customer buying behavior and spend pattern to drive their business growth.
ML for managing marketing customer data:
The ever-growing unstructured data on social media, prospective companies can mix “listening technologies”. The human customer service can soon replace by machine learning algorithms along with the help of NLPs.
ML for manufacturing sector:
Machine learning algorithms and platforms are helping manufacturing companies to find new and innovative business opportunities, refine product quality, design, and optimize manufacturing operations. Asset Management, Logistics and Supply Chain Management, and Inventory Management are some of the hottest areas of machine learning today.
These are just a few samples of machine learning applications in data management, still there are many such as data security, personal security, online search, smart cars, and many more. Keeping the value of faster time, storage cost, and potential person power for data management ML plays a major role. Due to its unique capability, ML is the only answer for smart digital development. And it continues to play a crucial role in the future of enterprise data management. The only catch is machine learning is it works only when your training data is representative of the population.