When deriving information from data, businessmen and scientists use about 80% of their time to find, clean and reorganize relevant data sets, leaving only 20% of the time for the actual value-generating. Therefore, organizations and sectors are now shifting towards making the best use of existing resources and data assets— to reduce risk and increase productivity.
This is where Machine Learning (ML) comes in. The use of ML in data management leads to less operational cost through improved efficiency, a superior user interface, context-driven services and data quality through increasingly viable operations. Machine learning software uses statistical methods to make predictions and automatically teach itself to improve prediction accuracy over time. However, often Machine Learning is not comprehended by operational teams and requires good technical expertise which generally rests with the analytics company.
Easing operation through predictive analytics:
Although employees still play an essential part in data management and analytics, it is the tools like AI and ML that assist an organization in analyzing its data more quickly and efficiently. Furthermore, due to advances in machine learning, cloud computing and storage, enterprises are now also considering machine learning with a more positive outlook.
For instance, by offering customized analytical tools, ACSG Corp, a Critical Infrastructure Analytics Company, has made this work exponentially easier and less time consuming for the organizations and sectors around the world, resulting in better productivity and efficiency in operations. With secured and responsible data handling and analysis, ACSG Corp has adopted Machine Learning to make analytics seamless and lucid for Critical Infrastructure Sectors. This leads to increased productivity in data management & quality, without affecting the business-as-usual exercises.
Similarly, AI-based solutions by Prolitus Technologies, help organizations automate processes and leverage machine learning tools. The company designs, implements and integrates machine learning applications specific to customers’ business. A team of business analysts and software engineers at Prolitus Technologies work together to create solution design for the organizations— to enhance the efficiency of various businesses.
Efficiency in operations due to ML:
As the widespread use of ML in Data Management increases, a resource that shows how modern innovations and tools can enhance the organization operations across the data value chain, it is expected the future business operations could completely rely on the technology. Nevertheless, higher prediction accuracy is also being regarded as a priority to conduct seamless business operations. Thus, the companies providing such services are upping the ante. To obtain higher prediction accuracy, Talentica recently used an ensemble consisting of a trained deep neural network which not only engineers practical data-driven algorithms but also eases business operations.
Nevertheless, while ML helps organizations and sectors gain efficiency in operations, there is always a possibility of data risk which needs to be identified, monitored and controlled for absolute effectiveness.