Choosing between Custom Dashboards and Off-the-Shelf Business Intelligence Tools

Business Intelligence (BI) tools for reporting are becoming increasingly popular with a growing number of solutions available.  There are many cases where BI tools are the ideal choice; however, for a large enterprise with access to development resources, a custom dashboard solution is perhaps a more cost-efficient solution and offers more flexibility for integration with other applications.

We have helped many organizations select dashboard solutions and helped them optimize existing solutions and enhance their reporting processes.  These clients ranged from midsized manufacturing companies to large corporations.  When helping clients to choose between a custom dashboard and an off-the-shelf solution, we use a comprehensive and customized analysis process. Here’s an overview of what we consider when selecting business intelligence tools.

1. Cost of Scaling

Fortunately, 2020 has seen the license cost per user decrease significantly for most business intelligence tools. Cost per user typically ranges between $10/month to $100/month depending on the features and capabilities. This may seem tiny at first, but can cost thousands of dollars as the number of users grow.

It is very important that you take the time to assess the potential impact of near-term growth scenarios.

Some questions to consider include:

  • Are there any joint ventures, acquisitions, or expansions planned in the future?
  • What will happen when your company grows – will your data warehouse requirements grow, too?
  • Can your data remain on-premises or does it need to be uploaded to a public cloud before it can be shared?
  • What are the exit costs?
  • How much bandwidth will you need to manage your data plumb-lines, and how are your ETL processes going to adapt to the requirements of BI tool

2. Connectivity and Storage Options

These days, most BI tools offer connectivity to a slew of data sources. You must do a proof-of-concept (“POC”) project to make sure that your specific data sources are well supported.

Whatever storage options you choose, you need to know the cost of scaling – vertically or horizontally. On-premises infrastructure means continuous monitoring of hardware performance and investing in upgrades at a reasonable frequency to improve performance. While an on-premises solution gives precision control over your data, leveraging public cloud infrastructures to scale up or down could result in cost-savings in the long run.

Not all BI tools are capable of handling on-premises and cloud data storage equally.

The important thing to note here is that not all BI tools are capable of handling on-premises and cloud data storage equally.  Some may support on-premises data for local use only where sharing dashboards would require the data to be uploaded to the cloud; some might work only with on-premises storage.  Neither is a deal-breaker. To make an informed decision, you need to keep your company’s long-term vision in mind.

Case Study:

We recently helped a manufacturer select analytics solutions to monitor their production operations, and business reporting. The client was in the process of transferring their data center from on-premises to cloud and restructuring their in-house IT resources. Part of the solution included retrofitting production equipment with sensors to measure overall equipment effectiveness (OEE).

With 20+ years of industry-specific analytics solutions, one of the specialized production equipment monitoring vendors offered machine monitoring solutions and ready-to-use industry specific KPI dashboard that would have make the implementation and onboarding seamless.  However, they offered limited integration options with off-the-shelf BI tools and supported on-premises data storage options only.  Furthermore, the vendor’s machine monitoring solution was only compatiable with 50% of the production equipment.

For our client, the management of the on-premises server was a show stopper and we helped the develop detailed selection criteria and manage the RFP process

3. Cost of Optimization

If your dashboard is straightforward, and you’re dealing with a relatively smaller user base, the cost to enhancements and maintenance may not be significant.

On the other hand, if you have a complex dashboard with a lot of KPIs crossing multiple departments, business units, and many databases, the cost of making changes or optimizations to cater to changing business needs can be expensive.

Case Study:

In one case, a large organization set up a complex BI dashboard to monitor operational expenses across multiple business units and was receiving data feed from numerous sources.  

The original team that had developed the dashboard was not around anymore and some critical business rules were not documented – an all-too-common situation. In these situations, the smallest of small changes may necessitate a review of the database setup, an analysis to understand the details, and end-to-end regression testing to validate data. This can lead to significant costs.

4. Integration Flexibility

Let’s say there is a new JavaScript framework in the market that everyone is raving about and your development team would like to incorporate it to see how your dashboard functionality can be enhanced.  Most BI tools have their own front-end studio, which mostly do not allow inclusion of JavaScript libraries and limit users to only use the visualizations offered by them.

While modern BI tools offer great look and feel out-of-the-box, you may face some rigidity if you’re trying to enhance user experience beyond what’s offered. As mentioned earlier, this may not be a deal breaker and your requirements can be perfectly satisfied by what a modern BI tool can do. However, it’s worthwhile considering these requirements based on your target end-state and your organization’s technology roadmap.

5. Support for unstructured data

Some BI tools only support relational databases, which may provide limitations when it comes to integrating with platforms such as Hadoop, which allows for distributed processing of large data sets across clusters of commodity hardware.

Case Study:

One of our clients was using a BI tool which supported only relational databases and their loyalty team wanted to analyze Twitter data in real-time. They ended up writing a robust ingestion framework in Python with internally developed NLP (natural language processed) algorithms that were not very effective. Your business may not need to tap into unstructured data at this very moment; however it’s good to be aware of such factors as you assess any potential vendor.  


There are pros and cons to both off-the-shelf BI tools and custom dashboards. The optimal solution for your company depends on your current and future requirements.  Before diving into implementation, take the time to review key considerations to manage future expenses associated with ongoing maintenance and optimization.

At Mantrax, we specialize in helping organizations select between off-the-shelf BI tools and custom dashboards.  We also specialize in end-to-end implementation services for both options.  Contact us for more information.