7 Challenges of Implementing a Big Data & Analytics Solution

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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 Big Data & Analytics solutions.

7 Big Data & Analytics Challenges Facing Credit Unions

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 Big Data & Analytics solutions, 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. Credit Union Performance Requirements

Implementing a Big Data & Analytics solution for your credit union 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 making the vehicle truly meet individual performance needs. A Big Data & Analytics solution must also be carefully designed to meet overall credit union 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 Big Data & Analytics Solutions for Credit Unions

People generally don’t want to waste their time defining the requirements necessary to design Big Data & Analytics solutions for credit unions properly. Usually, there is a high-level perception of what is wanted out of these solutions. 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 solution that does not deliver the results expected by the user. If a credit union’s 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.

6. User Acceptance

People are not keen on changing their daily routines, 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.

7. The Cost of Big Data & Analytics

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 is 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 are 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 strategy is a significant undertaking that should fully be thought out before initiating. Fortunately for credit unions, there are industry experts with extensive experience dealing with the challenges associated. Thanks to industry collaboration, Big Data & Analytics is no longer out of reach for credit unions. Credit unions now can 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

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