How To Tame Unruly Data

Why tame the Big Data Beast?  Big Data is changing the way companies do business, pushing IT firms to look beyond traditional technologies. There is a need to process ever greater volumes of unstructured, weakly-linked and often unruly data, in order to extract the FULL value of the insights and information contained within it, which can then be used by enterprises to make business critical decisions. Newer tools are being built that can process ever larger volumes of data in less time than before. It’s only by taming Big Data that you can achieve faster, more robust, results in real-time. How do you make sense of the huge volumes of data in complex and unstructured formats? The advent of mobile networks, cloud computing, IoT and other new technologies have paved way for ever greater volumes of information. This deluge of data, generated every second, is critical to a company operating successfully in the current business environment. That’s why Big Data has gained so much popularity in the digital business world. As data comes from various sources in huge volumes and velocities, in complex and unstructured formats, how can organizations tame this unruly data?

According to Dr. Demirhan Yenigan, Big Data Expert and Professor of Analytics at GWU, the growth of unstructured data have been at an unprecedented pace from various sources such as email, images, social media posts, sensor data, transaction files, mobile data, and weblogs. This unstructured data has many elements embedded in it, for instance, consider a blog post, it comprises of various elements such as the content, data and time of posting, embedded links, author, and the like. All these elements make searching and analysis of these unstructured data a difficult task. It is hard to organize large volumes of data using traditional database frameworks, which is the main reason why Big Data is referred to as unruly. There is a need to bridge the gap between the Big Data deluge and the ability to pull actionable insights from this vast pool of data. A lot of different techniques are being employed to capture and store this vast information. New tools are used these days to capture, data mine, and perform statistical analysis for generating useful outputs. Tools like Hadoop, MapReduce, Apache (Hive, Pig, Spark), MongoDB and Big Table have the capability to process and store massive amounts of data efficiently and cost effectively.

The other aspect is what you will do with all this data once you organize and capture them in some kind of data store. That’s where the data mining and analytics components come into play. This requires analyzing data by running data mining exercises in order to find patterns, interesting trends and relationships between the different components of this data. For analyzing unstructured data, organizations need to leverage cutting-edge analytics tools. Analysts and data scientists employ various analysis techniques such as predictive analytics, stream analytics, text analytics and data virtualization to make better decisions using data that were previously inaccessible or unusable. 

There is no dearth of incoming data, it is growing exponentially. By leveraging cutting-edge tools, it’s possible to make great leaps in the type of data mining and analysis one can do in the face of this exponential growth. Big Data has vast potential to drive businesses with unique information intelligence. Big Data platforms and analytics software focus on providing efficient analytics, turning data into quality information, and providing better insight into the business situation. The Future will definitely be driven by businesses using their data for smart decision making. 

 For more insights on big data analytics process,view the entire interview series

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By Demirhan Yenigan | September 15th, 2016 | Business Analytics & AI

About the Author

Demirhan Yenigun Managing Director, Analytic Services for Macrosoft

Demirhan Yenigan

Demirhan Yenigun has 30 years of leadership, strategic thinking and accomplishments in implementing analytical solutions for Fortune 500 companies helping them to enhance their marketing and sales efforts. Demirhan is an Industry Advisor and Adjunct Professor of Data Analytics and Data Mining at Decision Sciences Department of GWU Business School.Demirhan is currently the CEO of Metrica Group and DomainGo.

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