4 Obstacles Big Data Analytics Implementation Needs to Overcome

Few solutions are as pivotal to your future agility quite like big data analytics. Lowered operational expenses, boosted revenue, increased productivity, and even improved market predictions are all attainable through the evolving algorithms powering these tools.

In fact, more companies recognize the real potential of their data. At least 83% of business leaders say big data is adding profitability to their products and services, indicating that remaining competitive requires big data analytics implementation to happen with clockwork precision. Unfortunately, plenty of obstacles exist to hinder the path to comprehensive insight. Without talented big data professionals helping in the transition, analytic strength is often deficient.

Which obstacles present the greatest struggle? Here are four challenges we see companies struggle with constantly:

1.) Data Silos Limit Your Findings

Departmental mentalities are not just damaging to enterprise-wide collaboration. When each department hoards their own data in silos that are separate from the larger organization, the efficacy of analytics tools is significantly lower. Big data is all about getting the total picture. Any type of partition limits the possible predictive results.

For example, let’s say you want to explore your employment engagement. You want to know if using gamification to engage employees works over a period of year. If any performance metrics kept by your delivery, sales, or marketing teams are isolated from engagement metrics kept by human resources, the overall findings will be shortsighted.

Effective big data analytics implementation breaks down barriers in the process, dissolving departmental thinking from the top down. Cleaning up data infrastructure early sets the stage for longer periods of intensive analysis. Executives need to see the value of comprehensive integration. While making that case, the perspective of an experienced data analyst helps to surgically overcome objectives and raze the digital barriers separating data.

2.) User Acceptance Is Low

Most employees are habit driven. They are accustomed to the elementary or existing analytics tools their departments have used. Introducing a more comprehensive big data platform out of the blue will garner mixed feelings. To prevent underutilization of core platforms, a large part of big data analytics implementation relies on the way the tool is presented to potential users.

Strong implementation strategies make employees part of the solution. Getting them involved early can help to outline the most valuable features to end users and gauge familiarity with possible tools. Furthermore, it helps to guide their acclimation to the new tool, limiting team frustration early in the process.

3.) The Learning Curve Is Steep

Every new technology has its own learning curve. Even with the most intuitive of applications or platforms, there is a level of acclimation that takes time. An increasing number of self-service applications exist, but even the straightforward user experience they provide only goes so far. The communication skills of the people involved in the big data analytics implementation and any training materials determines if the initial adoption takes hold.

Additionally, there is even a learning curve with the overall analytical mindset. If you and your employees are not experienced with the often askance thinking of big data analytics, some of the early intelligence sessions might not elicit deep results. Working with veteran analysts and data scientists lowers the lag period between achieving fledgling insights and more mature findings.

4.) Your System Isn’t Scalable

Data is never going to be static. So much structured and unstructured data is accumulated almost effortlessly that we guarantee you will outgrow your current databases. That growth brings pangs along with the added intelligence.

Your analytics platform needs to balloon along with your data volume and user traffic. That requires your big data analytics implementation team to foster a smooth transition, one which accounts for expanded usage and volumes without sacrificing computation speed or the ease of querying. This will be a reoccurring problem unless your data analytics tools are deployed with serious scaling in mind.

Improving Your Big Data Analytics Implementation Further

Since there are so many components required by successful big data analytics implementation, it helps to have the assistance of professionals with extensive experience. Putting trust in an untested hand for a very serious transition often undercuts the entire process, leading to less effective analytics in the long run.

Yet with the demand for data scientists unsatisfied (83% of people in the field feel there is a shortage), many companies will be faced with shaky transitions. Business leaders will turn to alternative methods of sourcing to attract data scientists and big data experts. In the end, it is those companies willing to stretch the boundaries of how they find candidates who will find obstacles to big data implementation less influential on their future performance.

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