dataMDo you know just how much new data information we’re creating? According to IDC forecasts exponentially more and more, growing to 163 zettabytes by 2025. And not only does IDC predict the frantic growth of data generation, it estimates that one-fifth of all this data will be critical if not hypercritical to our daily lives.

So, how will we deal with this enormous amount, handle, store but especially evaluate to make conclusions? In the field of data analytics, companies can use a variety of technologies by now that automate the job. Some of the current technologies are only beginning to be used in different application scenarios in the areas of analytics and data management.

Certainly, the ability to monetize data through embedded analytics and to offer new data-driven services significantly increases value and allows to tap into the tremendous potential of data analytics. The most important trends you should keep in mind here are:

Comprehensive enterprise-wide analytics: demand for more efficient data management and a data value chain that provides decision-relevant information continues to grow rapidly. At the same time, high data quality, master data management and other data-centric functions are becoming increasingly important. Successful companies combine all these activities and components in a cross-departmental and enterprise-scalable analytics strategy.

Technology convergence: AI, Predictive Analytics, IoT, and Blockchain are technologies that require reliable data collection and targeted analysis, especially since the convergence of these technologies creates all new opportunities. By accessing, analyzing and processing the ever-increasing amounts of data, companies create a powerful basis for granting secure access to other user groups within and outside of their own organization and for providing new, action-relevant insights.

Building embedded analytics: organizations will leverage the benefits of embedded analytics across all departments, both as an extension of business process visibility, and to enhance interactions with customers, suppliers, and business partners. In addition, the use of embedded analytics will align with the convergence of other key data analytics technologies as more companies use AI and machine learning to streamline and more efficiently manage their processes based on a well-founded data value chain.

Improved data protection and higher data security: The General Data Protection Regulation (GDPR) was the first of many measures to implement higher requirements for data security, data protection, storage and use of personal and confidential data. Governments and businesses will be even more challenged than ever in the future to protect personal and confidential data from unauthorized access and to define what may be publicly available.

Although the increasing amount and complexity of the data may at first glance appear to be an obstacle when companies want to increase their value by adding big data processes, but they can’t neglect innovative data management and analytics solutions to support evaluation and processing. Complemented by artificial intelligence, machine learning and embedded analytics, this will enable them to identify new business opportunities and increase their competitiveness.

By Daniela La Marca