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Data management Strategies for an Effective Edge Computing Solution

By Capital Markets CIO Outlook | Monday, February 11, 2019

Technology has helped immensely in the ubiquity of data. Organizations are gathering huge pipelines of data with devices, applications, and systems of intelligence. An effective analysis of this data provides a massive amount of insights to the enterprises, helping them to make informed decisions.

Vast volumes of complex and raw data make data analysis a costly and complicated process as enterprises need to be wary of the fact that choosing particular data for analysis can bring in bias risks and undermine the value of insights. Companies also need to comply with the laws and regulations which bars cross border data transfers. The compliance regulations require companies to make optimum use to data by remaining in the same geographies.

Edge analytics can be a boon for companies as it offers effective ways to identify and analyze real-time data at the point of data collection. Edge analytics platforms deliver contextual, relevant, and real-time insights into the processes of a company without using any central location. These platforms enable companies to retain customers, improve service quality, and strengthen market share. As all the collected data does not need to be analyzed at the point of collection, it is an enterprise’s prerogative to choose the data that needs to be analyzed at the edge for an efficient business process.

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Here are a few data management strategies that can help companies to make an effective edge analytics solution:

Comprehensive Data Plan: enterprises should develop a data acquisition and retention plan by prioritizing valuable data. A tiered view of the data can help companies to extract the maximum out of the relevant data.

Hybrid model: A hybrid model of data processing takes advantages of edge computing as well as cloud and central processing. While edge computing helps to make operations efficient, Cloud computing can be used for improved artificial intelligence and machine learning processes.

Prevent Data Hoarding: Storing redundant data can result in unnecessary expenses and cause security and compliance risk. Enterprises should devise a solution to choose relevant data which can be used in data analysis.

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