infoTECH Feature

July 31, 2017

How to Overcome the Hurdles of Big Data

Big Data is simply defined as a large collection of structured and unstructured data. This data is gathered in real time and stored for eventual analysis. Real-time analytics and big data give companies insights that can lead to better strategic business moves and decisions. Basically, if it’s harnessed correctly, big data can give an organization a competitive edge. However, the amount of data being created and stored on a global scale is phenomenal.

Although this massive creation and storage of data increases the potential for gaining key insights from business information, only a small percentage of that data is analyzed. So, many companies are unable to use the data that’s collected by and presented to them. This happens because there are essentially three challenges preventing businesses from harnessing the power of this raw information: budget constraints, time constraints and too much data to handle. However, there are ways for every company to overcome these challenges.

Budget Constraints

Unfortunately, big data can lead to conflicts between information management and information technology. However, this problem can be solved by integrating the two and providing instant access to data through a unified system. Integration is ideal, but it comes at a price – an expensive price. Fortunately, this problem can be solved by using business unit leaders to define the value that each project brings to a business.

Understanding the business value proposition changes the funding model of the big data project. So, instead of looking for room in existing budgets, business unit leaders simply sponsor big data efforts. A business would also have to make smart decisions, such as offering cost-effective solutions with full vendor support. For example, offering managed services with in-memory computing.

Time Constraints

Data is delivered quickly, which means it should be turned into usable intelligence quickly. For example, for retail businesses, it would be useless to identify that customers are price checking or comparing products using a mobile app if that information isn’t used to offer competitive prices and products. To avoid these types of mistakes, organizations should implement a big data strategy that keeps pace with data velocity by changing business economics, infrastructure and processes.

Additionally, using an analytics consulting firm or other type of solution provider could counteract the time constraints that plague an internal IT team. However, using an expert from the start could ensure an organization has enough time to analyze and act upon all the data they collect and store. Plus, every company should implement the use of scalable and virtually "fool-proof" hadoop clusters to boost the speed of their data analysis applications

Too Much Data

Collecting too much data is a major hurdle for big data. In fact, so much data is being collected that it can’t even be analyzed using ‘traditional’ methods. Not to mention that the amount of collected data is growing exponentially, as companies strive to learn as much about their customers as possible. However, the problem is not just with the amount of data, it’s also with the variety of data types and the number of sources they come from. Besides that, a company can only control certain types of data.

Important data, such as content and social media stats, can’t be controlled, so a good infrastructure transformation is needed. This will ensure the data is aggregated and managed easily then converted into useful information with real-life applications. For example, a company could couple point-of-sale (POS) information with inventory and/or production information to help manage how products are marketed during a promotional period.


No matter the company, the hurdles of big data are the same. However, these hurdles can be overcome with a bit of ingenuity, smart decisions, focus on infrastructure, operations and processes, data transformation and external professional help. With all this help and more, a company should be better equipped and capable of turning real-time analytics and data into useful information.

Edited by Alicia Young

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