Active Information Management Improves Big Data Analytics

Big Data appeals to data analytics and companies worldwide because of the potential it holds. We have access to more data than any other time in history, and that amount of data is only going to increase in the coming years. If harnessed, Big Data has the potential to help companies make better predictions, know what customers want and need and grow at a faster rate than ever before. However, there are a few downsides to the volume of data available to analysts like sorting through which data sets are actually useful and also what type of data is being analyzed.

A large portion of Big Data is unstructured. This data comes from sources like social media, email and blog posts. Traditional data management solutions struggle to handle this unstructured data that results in extra time and energy needed to processes the data or some of the important data is never analyzed at all. This discrepancy between businesses’ ability to manage and store new data and manage their old data yields an overall declining data value despite the increased data availability. Over the last several years, IBM has developed a solution they call Active Information Management, which looks to take on this problem by effecting a sound information lifecycle governance approach.

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Active Information Management focused on a holistic approach to maintaining and recording important historic data while also adapting to handle today’s unstructured data types. It requires the disposal of old, irrelevant data that no longer provides value to a business but also provides ample storage for the countless datasets being generated every day. The three basic steps outlined by IBM are: discover (old datasets), classify (decide what to do with both old and new datasets) and define (rules for keeping and storing data to maximize analytics capabilities and profits).

Active Information Management Improves Big Data Analytics - YourDailyTech
Active Information Management

IBM’s Active Information Management utilizes StoredIQ to connect content management with lifecycle governance. StoredIQ is the specialized solution that puts Active Information Management to work. It allows users to discover, analyze and transform unstructured data without the use of a repository or additional storage application. Patterns and trends are easier to spot. StoedIQ also has a sophisticated search feature that makes it easier to track and find historic or previously used datasets that are relevant to a particular project. In short, Big Data is more effectively utilized with StoredIQ’s Active Information Management because of the time it saves and the new insights (e.g. data trends) it provides to its users.

Rebecca Seasholtz

Rebecca is a senior Materials Science and Engineering major at Georgia Tech. She specializes in soft materials (i.e. plastics and textiles) and has also worked extensively with functional materials for electrical applications. Rebecca is originally from Grayson, GA and likes to spend her free time running, cycling, drinking coffee, or hanging around the campus house of a ministry she attends at Georgia Tech. Contact Rebecca at [email protected]