Big Data's Theory of Relativity » GoGrid Blog

Big Data's Theory of Relativity » GoGrid Blog

During the past several years, Big Data has made headlines, entered boardrooms, and been prioritized by companies of all sizes in the hopes that their processes will present new opportunities for long-term success. In many cases, the programs will enable organizations to experience financial, operational, and managerial benefits much as squeezing juice from a handful of data-filled oranges will provide the much-needed drink for any thirsty business.

Big Data’s theory of relativity

Still, executives can’t approach Big Data with the mindset that virtually any project will suffice. In fact, if firms take this route, they’ll often find themselves overburdened with complex information that doesn’t necessarily yield any advantages. IT and business decision-makers need to establish Big Data initiatives with their companies in mind because implementing a generic house-blend of products and strategies won’t respond to the unique challenges and demands faced by most organizations.

Although there are many important factors to prioritize when building a Big Data program, two in particular should be emphasized: Understanding the business and prioritizing quality over quantity.

Understand the business
If Big Data executives in large enterprises or small organizations don’t fully understand the needs of their specific companies, they’ll find it increasingly difficult to implement the tools, technologies, and processes necessary for employees to complete operations in a timely and efficient manner. Breaking down Big Data projects into smaller segments parsed out to multiple departments can help firms embrace more relevant information that augments key procedures.

Marketing, sales, and other decision-makers who intend to embrace Big Data must understand their core principles and needs if they want to ensure their endeavors are as effective as possible. If executives recognize the direction they want to travel, building an analytics program that caters to those specifications will be much easier, regardless of the volume of information those departments are responsible for managing.

Quality over quantity
Although the business world has experienced numerous challenges with Big Data initiatives, one of the most prevalent and disruptive is the idea that “more is better.” Although this concept may be true in a number of situations, it is irrelevant when it comes to Big Data, despite the name. In fact, many organizations that adopt this mentality are overrun by so much information that decision-makers and employees don’t know which way is up or where they need to go to reach success.

Accumulating qualitative information is much more effective for companies that want to thrive in the Big Data landscape. If teams simply harness as many resources as possible, they’ll tend to lose sight of their objectives and won’t take advantage of the opportunities they originally set out to achieve. Businesses need to come up with key questions before seeking answers because generating irrelevant solutions to unknown queries won’t deliver the advantages companies are seeking.

As the Big Data movement accelerates and picks up more momentum in the future, executives need to keep their specific organization in mind when embracing initiatives. Rather than deploying an ineffective program and accumulating irrelevant information, decision-makers should map out a strategy that will allow them to capitalize on the opportunities that will improve their unique processes. By taking this forward-thinking approach, firms will find it much easier to experience the financial, operational, and managerial opportunities that are often associated with Big Data, gaining competitive advantages over rival companies that failed to plan their endeavors carefully.

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The GoGrid Team is committed to bringing you the information, advice, and tools necessary to easily evaluate and deploy a broad range of Big Data technologies and maximize your infrastructure to meet your specific needs.

Big Data’s Theory of Relativity – December 2, 2013

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