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Unpacking 5 Major Challenges in Business Intelligence Platform Management

Norbert Havas

Director, Enterprise Analytics Consulting

Managing the performance and utilization of business intelligence (BI) platforms, as well as user acceptance and adoption of analytics across the organization, is often difficult for businesses. This is largely due to the diverse set of software products and analytics tools within a scaled enterprise, which can be challenging to integrate across different departments or user groups. To create a successful BI strategy, there are five frequent challenges that BI platform owners must address and overcome.

Choosing from an Overwhelming Number of Analytics Solution Applications

Believe it or not, there are pros and cons to choosing from a large number of analytics applications. One of the benefits to having a wide variety of options available is the ability to choose a BI dashboard that offers analytics suited for your unique business goals. However, selecting the right dashboard can be difficult for BI users due to either being uncertain about their choice of app or spending too much time researching options. Without any guidance, it can take hours to find the right match, especially if users are new to the company. Additionally, BI solutions across departments can easily stay in silos and lead to the unnecessary creation of analytics dashboards for business questions that have already been covered by existing apps.

Adding an additional layer on top of existing analytics platforms helps enterprises give users unified access to all available analytics tools so users can find the right tools and functionality easily when they need it. 

Managing a BI Platform Transition Effectively

For enterprise businesses, switching from one BI tool to another is not unusual, but it comes with a variety of pain points. Even after implementing a new BI tool, there’s always lead time. As new projects start within the new system, organizations still need to access reports from the past, including applications that haven’t yet been migrated or never will be. BI managers should understand that conversion for most dashboards can be a slow process, making it necessary to use and manage multiple dashboards simultaneously.

To manage BI transitions more easily, businesses should implement additional layers within their existing tool. For example, using an application store allows users to switch from one tool to another and replace dashboards with the latest version of BI applications seamlessly. Customizable platforms with access rights overview is very helpful to control change management.

Allowing Continuous Feedback Flow within Dashboards

One of the biggest challenges of creating BI reports is considering the user’s perspective while using the dashboard. Ensuring a seamless user flow is easier said than delivered. To do so, it’s important to gather data about the experience and merge it with usage and navigation path statistics. Direct feedback from application users can greatly enhance the user experience and help when building well-adopted analytics solutions. For instance, providing opportunities for users to rate, share reviews or give open-ended feedback offers unparalleled insights for improving analytics environments on-demand.

Managing a Growing Number of AI Assets Built into Business Processes

Now that artificial intelligence (AI) is becoming a core element of improving business processes, more and more organizations are keen on implementing different AI assets to replicate human intelligence and augment decision-making. However, enthusiastic innovators often overlook one important factor – the supervision of all of these AI investments across the company. A catalogue of machine models allows stakeholders to own all AI assets across their entire lifecycle and enables the following:

  • Automation of model testing process and deployment
  • Creation of data science platforms
  • Assurance that models are reviewed and adapted to the changing business environment
Governing Data Science Platforms

Controlling data science development processes can be a struggle for management, as they are usually using different platforms for each stage. While well-formed data science processes are based on CRISP-DM methodology, they’re typically missing an end-to-end, compliant development process to review and govern all the steps within one system. By putting such a process in place, organizations can reap the following benefits:

  • Streamlined IT business collaboration
  • Audit tracking and controlling over the analytics production releases
  • Agile processes to maximize business output
  • Data science collaboration due to the unified platform

When creating a BI strategy, it’s important to set yourself up for success from the get-go. By considering these challenges, you can gain a comprehensive framework to address your analytics goals and drive your most critical focus areas in the right direction, including increasing user acceptance and adoption of analytics, eliminating data and application silos and tracking for consistency, and improving time-to-value.

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