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4 Hidden Costs of Open Source Analytics Tools

By Michelle Gardner | August 1, 2017
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When it’s time to add or update embedded dashboards and reports in your application, application teams have a number of software build vs buy considerations to factor into their decision.

The first instinct for many software companies is to build exactly what they want with the help of open source analytics tools, free data visualization tools, and other open source BI components. This code-intensive approach works for some organizations—especially the ones that think their users don’t have complex requirements. Even as applications grow and companies modernize their analytics offerings, the primary reason for using open source analytics is to maintain complete control over the look and feel of the application (and because developers often declare that “building is the best way”).

>> Related: Why “Build or Buy?” Is the Wrong Question for Analytics <<

While building an analytics solution can reduce the upfront hard costs and, to an extent, the evaluation time required, it also comes with a number of ongoing soft costs. These include research and development of components, project management overhead, and ongoing maintenance of the embedded analytics, which all add to the long-term hidden costs of open source analytics tools.

4 Hidden Costs to Consider for Open Source Analytics Tools

1. Time Spent in R&D

Your development team will spend a majority of their time gathering requirements, then mapping those requirements to a variety of free BI tools and open source analytics components. Between 30 to 50 percent of your entire project time will be dedicated to researching and evaluating different components—replicating work that’s already been completed by numerous analytics vendors.

2. Architecting (and Re-Architecting) So Everything Works Together

If the open source analytics component you want to use to, say, bind data doesn’t work with the one you like for charting, you’ll have to go back to the drawing board. Likewise, every time the free data visualization tools or other free BI components have an update, you’ll need to rework your integration efforts to ensure everything still works together.

3. Costly Components

Building an analytics application means finding the right mix of open source and paid components that will power your solution. Some components will be free, but this is far from the rule—and if there’s a capability you really need, you’ll likely have to pay that price. What’s more, if one component eventually breaks, your whole open source analytics solution may fall apart.

4. Ongoing Maintenance

About 90 percent of software life cost is related to its maintenance phase, according to one report. Relying on open source analytics components means that, if one component is updated or breaks, the whole solution may fall apart, rendering your application unusable until it is fixed.

Software companies that choose to build a solution with open source analytics commit to staffing significant resources in development, support, and keeping up with advances in data visualizations and business intelligence over the long term. Since open source analytics tools and free data visualization tools are constantly evolving, the upkeep of each analytics component adds to the overall investment.

So when does it make sense to build a solution with open source analytics tools? If BI is part of your company’s core competency, then you should definitely build. Also consider this route if your users’ needs will always be limited in scale (for instance, if all they need is a static dashboard and reporting that you have no plans to charge more for).

An Easy Way to Avoid Hidden Costs

While a tool can be open source, hidden costs can turn a free product into a product that uses many of the company’s resources – from development to maintenance.  To help you decide how to proceed, we’ve put together a free ebook that will help you evaluate what factors should guide your Build vs. Buy decision. In addition, it may be worthwhile to investigate what a “buy” tool looks like – see a demo of our product here!

 

 

About the Author

Michelle Gardner is the Content Marketing Manager at Logi Analytics. She has over a decade of experience writing and editing content, with a specialty in software and technology.

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