How Data and Analytics will Transform Businesses in 2020?
Data and Analytics are used everywhere especially in the digital enterprise field. Driven by the huge promise of big data, data analytics have gained huge traction in organizations and the potential of machine learning and other varieties of technologies even gained the usage more. Data and analytics have become key parts in serving customers, hire people, optimize supply chains, optimize finance and perform well so other functions in the organizations do well.
Augmented Analytics is one of the most dominant techniques used in the organizations, while assessing vendor selections over the next few years. Several vendors are incorporating augmented analytics into their products and services to improve the experience for users. Similarly, the augmented data management will also have the ability to analyze data, which is more dynamically. However, many tasks come with the data management side of the operation such as schema recognition, capacity, utilization, regulatory/compliance, and cost models, and more. It is expected that data management tasks will reduced by 45 percent via the addition of machine learning and automated service-level management.
For Augmented Analytics, NLP and Conversational Analytics are complimenting in a great deal. Both of them provide non-data experts with a new kind of interface into new queries and insights. However, 50 percent of the queries related to analytics through search, NLP or voice will be automatically generated as per Gartner. Nowadays, most analytics and BI platforms implemented basic keyword search. It also takes ranking functions and synonyms and other functions which most of the organizations cannot do today.
In addition, graph processing and graph databases offer data exploration and the logical concepts and entities like organizations, people and transaction are becoming the trend. Gartner also predicts that the application of graph processing and graph databases will be growing at a rate of 100 percent annually via 2022 to continuously accelerate data preparation and offer more complex and adaptive data science.
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