The Decision Maker

7 Challenges of Implementing a Big Data & Analytics Solution

Posted by Austin Wentzlaff on Apr 13, 2015 12:59:00 PM

7 Challenges of Implementing a Big Data & Analytics Solution

Most credit union leaders are familiar with the concept of Big Data and business intelligence, but many fail to fully understand the significance they have on their credit union and its future.  Big Data & Analytics can provide credit unions with the ability to make better decisions that positively affect member relationships and ultimately their top and bottom lines.  There are several obstacles in the Big Data & Analytics process that need to be overcome in order to achieve success.  These obstacles typically take an extensive amount of time to conquer, especially the first time they’re encountered.  Credit union leaders should consider the following challenges before implementing a Big Data & Analytics solution:

  1. Data Quality – In a credit union, data is coming from many disparate sources from all facets of the organization.  In order to overcome this, a data warehouse is essential.  However, when a data warehouse tries to combine inconsistent data from disparate sources, it encounters errors.  Inconsistent data, duplicates, logic conflicts, and missing data all result in data quality challenges.  Poor data quality results in faulty reporting and analytics necessary for optimal decision making.
  2. Understanding Analytics – The powerful analytics tools and reports available through integrated data will provide credit union leaders with the ability to make precise decisions that impact the future success of their organizations.  When implementing a Big Data & Analytics solution, analytics and reporting will have to be taken into design considerations.  In order to do this, the business user will need to know exactly what analysis will be performed.  Envisioning these reports will be difficult for someone that hasn’t yet utilized a Big Data & Analytics solution and is unaware of its capabilities and limitations.
  3. Quality Assurance – The end user of a Big Data & Analytics solution is using reporting and analytics to make the best decisions possible.  Consequently, the data must be 100 percent accurate or a credit union leader will make ill-advised decisions that are detrimental to the future success of their business.  This high reliance on data quality makes testing a high priority issue that will require a lot of resources to ensure the information provided is accurate.  The credit union will have to develop all of the steps required to complete a successful Software Testing Life Cycle (STLC), which will be a costly and time intensive process.
  4. Performance – Implementing a Big Data & Analytics solution is similar to building a car.  A car must be carefully designed from the beginning to meet the purposes for which it is intended. Yet, there are options each buyer must consider to make the vehicle truly meet individual performance needs. A Big Data & Analytics solution must also be carefully designed to meet overall performance requirements. While the final product can be customized to fit the performance needs of the organization, the initial overall design must be carefully thought out to provide a stable foundation from which to start.  Major customizations are extremely expensive.
  5. Designing the Solution – People generally don’t want to “waste” their time defining the requirements necessary to properly design Big Data & Analytics solution.  Usually, there is a high level perception of what is wanted out of a Big Data & Analytics solution. However, they don’t fully understand all the implications of these perceptions and, consequently, they have a difficult time adequately defining them.  This results in miscommunication between the business users and the technicians developing a Big Data & Analytics solution. 

The typical end result is a Big Data & Analytics solution that does not deliver the results expected by the user. Since the Big Data & Analytics solution is inadequate for the end user, there is a need for fixes and improvements immediately after initial delivery.  The unfortunate outcome is greatly increased development fees.

  1. User Acceptance – People are not keen to changing their daily routine especially if the new process is not intuitive.  There are many challenges to overcome to make a Big Data & Analytics solution that is quickly adopted by an organization.  Having a comprehensive user training program can ease this hesitation but will require planning and additional resources.
  2. Cost – A frequent misconception among credit unions is that they can develop a Big Data & Analytics solution in-house to save money. As the foregoing points emphasize, there are a multitude of hidden problems in developing a Big Data & Analytics solution. Even if a credit union adds a data “expert” to their staff, the depth and breadth of skills needed to deliver an effective result is simply not feasible with one or a few experienced professionals leading a team of non-BI trained technicians. The harsh reality is an effective do-it-yourself effort is very costly.

Implementing a Big Data & Analytics is a significant undertaking that should fully thought out before initiating. Fortunately for credit unions, there are industry experts with extensive experience dealing with the challenges associated.  With the help of CUSO collaboration, Big Data & Analytics is no longer out of the reach for credit unions.  Credit unions now have the ability to leverage their data to make fact-based decisions that positively affect their institution and position them to remain competitive in the ever-changing financial services industry.

 Article originally posted by Austin Wentzlaff, OnApproach, on CU Insight.

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