Big Data Explained

Big data has been a buzzword for several years now, but concrete definitions and holistic examples can be difficult to pin down. Since the term’s inception, ‘big data‘ has been defined as datasets with high volume, velocity, and/or variety. These three core characteristics are often called the “3 Vs” of big data. Social media is a classic example of big data; there are thousands of posts every minute, and, although small patterns like trending topics exist, large patterns are unpredictable. There are endless sources of massive amounts of data beyond social media. The International Data Corporation predicts that, by 2025, the global data sphere will hit 175 Zettabytes.

Big data is inherently difficult to handle. If you were to predict tomorrow’s events based on today’s posts, you would have to aggregate millions of posts from multiple social media sources. It would be even more difficult to make a model that is able to handle all of this unlabeled, unstructured data. Even after surmounting these challenges, finding meaning and creating visualizations from a sea of data can be overwhelming — even for experts.

The field of collecting, storing, analyzing, and utilizing big data is called big data analytics. Even with all the challenges big data brings to the table, tech leaders have invested heavily in managing and analyzing big data. For example, consider Netflix’s recommendation algorithm. Netflix engineers examine the viewing habits of millions of users to make customized recommendations that predict what you may want to watch next.

Most industry leaders currently use some flavor of big data analytics; applications aim to streamline operations, anticipate trends with market analysis, or to collect feedback for a better user experience, to name a few. Big data analytics opportunities are constantly evolving and expanding. Web giants like Amazon, Facebook, and Google have massive amounts of data on user behavior to customize online marketing strategies. Manufacturing companies track suppliers, distributors, and producers involved with each product to ensure their supply chain is sustainable and fully optimized. Hedge funds have countless data streams from global news, Federal Reserve actions, and stock market movements that need to be taken into account to allocate investments effectively.

Small businesses can also take advantage of big data analytics by using many of these same methods. A retailer can analyze which items in its online catalog attract the most users, how long users stay on each page, and what keywords lead users to that product. This provides key insights on how to optimize marketing strategy and exceed customer’s expectations.

Big data has revolutionized how companies create, store, and manage their digital content. Across every industry, big data analytics have repeatedly proven their value to secure competitive advantages. As the global data sphere continues to grow, innovative big data analytics strategies can help bring about success in any market.

Caitlyn Caggia is an experienced systems integrator and solutions architect; she holds a MS in Electrical and Computer Engineering from Georgia Tech.

Tags: big data, big data analytics, small business, technology

This entry was posted on August 1st, 2020 and is filed under Technology. Both comments and pings are currently closed.