Big Data Trends to Watch Out in 2020
Big data and analytics are vital resources for companies to survive in a highly competitive environment.
FREMONT, CA: During the past few years, Big Data has become an insightful concept in all the technical terms. Besides, the accessibility of wireless connections and advances have facilitated the analysis of large data sets. Enterprises are picking up strength continuously by enhancing their data analytics and platforms. In the wake of 2020, there are huge upticks in Big Data use worldwide, with organizations running to adopt the significance of data operations to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2019, and is set to proceed with its rapid growth and reach $247 Billion by 2022. It's the ideal opportunity for enterprises to look at Big Data trends for the coming years.
In big data environments, cloud concepts eliminate the limiting local IT infrastructures of companies. A major theme of the future is Wide Data. This means that IT is increasingly searching for the fragmented, widely distributed data structures generated by inconsistent formatted data and data silos. In the past few years, the number of databases for several data types has doubled from around 160 to 340. The firms that will benefit are those that tackle to bring data together in a meaningful synthesis in the future.
• Data Competence as-a-Service
A combination of data synthesis and analysis will develop the effective use of data further. It will be vital that users get assistance in reading, working, analyzing, and communicating the data. To achieve this, firms must promote their employees' data knowledge by utilizing partners who provide software, training, and help the SaaS model. This not only enhances data know-how by optimally combining DataOps and self-service analytics but also enables data-supported decision-making.
• Intelligent Metadata Catalog
Metadata is structured data that have information about the features of other data. This lets huge amounts of data be localized, captured, synthesized, and automatically processed in the distributed and diverse data stocks. Intelligent functions based on machine learning are leveraged for data preparation, collaboration, and an optimized workflow. Since the context is preserved, the data is more accessible and can also be used for future projects.
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