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Analyzing streaming data starts with the right tools

As we continue to add streaming capabilities to data infrastructures, the amount of data created by IoT and connected devices is expected to reach 847 zettabytes per year by 2021, leaving organizations with a new challenge to overcome: storing and analyzing this massive amount of data for the best business outcomes.

IoT and connected devices contain real-time data flows or streaming data sources that must be managed and assessed in real-time, because some of the data can be time-sensitive to an organization. Using the proper type of data management system with AI and machine learning-powered analytics allows you to not only react in real-time, but also perform multiple rounds of cross-sensor and historical analysis to get the most from your data.

Use streaming analytics to understand complex IoT data

Being able to understand the data collected at the edge begins with using the right data management tools to adequately perform an analysis, which provides the most beneficial data-driven decisions. However, the use of traditional analytics tools simply cannot meet the requirements today to perform real-time analysis on this scale of complex, fast-moving data. Next-generation technologies are required to meet the data challenges of today and tomorrow.

The two critical factors that limit the value of data in most businesses is the depth of analysis and the speed of analysis.

For example, consider the retail industry. Using a traditional analytics tool that only stores historical data would allow the retailer to analyze an increase or decrease in demand for a specific product as a whole over a certain time frame. Historical data can tell the retailer that a certain product performed better in terms of sales compared to another product, therefore the first product is better. However, what if this is not the case?

By leveraging the power of streaming analytics and location intelligence combined with the historical data collected over time, retailers have the ability to drive accurate replenishments of a well performing product to locations with a higher demand for that product.

Traditional analytics tools are simply not cut out to perform analysis on the incredible volume of data streamed and stored at the edge. Businesses looking to perform an accurate analysis of their data must look towards an active approach to data analytics by combining the power of historical analytics, streaming analytics, graph analytics, location intelligence and machine learning-powered analytics to make data-driven decisions in real-time.

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