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5 Big Data Analytics Myths Debunked

Big data analytics is a concept that almost all businesses have heard of in some shape or form. Decision makers know about the various benefits such as timely insights, real-time monitoring and forecasting and more personalized experiences, which can in turn lead to additional revenue streams.

But as with any trend, misconceptions are still at an all-time high. These false truths make big data analytics a solution many businesses are afraid to implement into their infrastructure, leaving companies at risk of losing customers to a competitor, making inaccurate business decisions and missing critical opportunities for growth. Here are 5 myths that still need to be addressed for decision-makers who are still on the fence about implementing a big data analytics strategy:

  1. Only big companies need big data analytics – Whether you are a small business or a Fortune 500 company, leveraging big data analytics to better manage your business is essential, as it can help to identify problems, find opportunities and gain competitive advantage. Many believe that SMBs are inadequately equipped to leverage big data; however, small businesses have a unique, competitive advantage to leverage big data. Small businesses have the speed and agility that mid to enterprise level companies’ lack. For SMBs that invest in a big data strategy, they are able to monitor, tweak processes and see results much quicker than their counterparts with an unprecedented level of detail.
  2. Big data analytics is too complicated to comprehend – The problem most people face with Big Data Analytics is how overwhelming the ecosystem can be. It’s daunting and there are many technologies that serve many different purposes. Not all big data solutions are equal. When looking at a big data analytics environment, most need to store, process, analyze and visualize data. Pretty simple right? But, there are specialized tools that are designed to meet a company’s environment – whether on premise or in the cloud. Lean on experts to help you decipher through the noise. A good service provider will help you to first develop the use case, understand what data you need and the value you are trying to get from the data. The next step is identifying the technical framework and applying the appropriate technologies to help you get the most out of your use case and actionable insights.
  3. You don’t have big data – Most companies know they have big data. What most companies don’t know is what the good data is. Without the proper structure in place, lots of time and money could be wasted collecting the wrong data—or even worse, time could be wasted collecting good data with a story that goes untold. Companies need to first start with a purpose by understanding what type of data they need to analyze. What is the use case? What behavior are they trying to change or seek? This will help you to determine whether you need to capture all data or just a subset, at a large level or in some other format. Make sure you understand the source and types of data—where it comes from and how accurate it is.
  4. Big Data Analytics is only an IT matter – When done right, leveraging big data analytics for the entire enterprise can directly impact the entire company—sales, marketing, service, human resources, etc. Labeling big data as an “IT matter” limits the company from reaching its fullest potential. Rather than looking at big data as a number-focused output, big data analytics should be approached as an asset that can solve a business issue or behavioral use case. Once companies begin to look at big data from a company-wide perspective, they will naturally develop a use case that is most impactful and efficient. After this data has been used to create actionable results, be sure to share this business success, highlighting the behavioral story behind the numbers.
  5. Big data = big investment – Not really. Of course this is relative and depends upon what kind of solution a company decides to go for, but budget spend on big data can lead to huge ROI. Using a public managed-cloud platform for big data analytics minimizes the costs and complexities usually associated with data scattered across multiple systems and platforms, along with independence from local IT on-premise infrastructure constraints. Prior to purchasing, companies can now test drive big data analytics in the cloud. This not only gives companies the freedom to test new technologies quickly, but it’s also a cost-effective way to ensure that the platform’s the option that makes the most sense for their business needs. And don’t forget, there are service providers who can assist teams with a 4 to 6 week test drive project!

Blair LinvilleContributed by: Blair Linville, Chairman & Chief Executive Officer at Tectonic. Blair has successfully scaled cloud technology operations for multibillion dollar and growth oriented, middle market companies. Before founding Tectonic, he served as the CIO for Caesar’s Entertainment, a $12B company differentiated by powerful marketing & industry leading analytics. In his role, he utilized big data and analytics to draw insights about customers and then used Marketing Automation and CRM to shape customer behavior. After four years at Caesar’s Entertainment, Blair finally received his personal “aha!” moment where he learned the tremendous impact of leveraging technology to draw insights about customers and how using those insights to drive meaningful action across a large scale aids in a company’s growth. From there he founded Tectonic, a premier big data analytics provider renowned for its marketing automation, CRM transformation and cloud consulting services, with a global client roster of legacy brands including MetLife, Ralph Lauren, Unified Brands, and the Chicago Cubs.

 

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