Analytics Solution: Should You Build or Buy?


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When faced with the need to embed analytics into an application, most software providers arrive at the crossroads of the “build versus buy” decision.


Why Build Or Code?

The first instinct for many application developers is to build the necessary reporting functionality with the help of code libraries or charting components. What invariably happens over time is that users ask for more functionality, more flexibility in their analysis, and more methods to gain insight without your help. Very few customers want to simply extract data into an excel file. Most customers these days want to be able to build custom dashboards and visualizations as they learn the product. With increasing demand, it becomes difficult to build analytics in a scalable way.  

Application providers who stay on the “build” track are committing to staffing significant resources in developing, supporting, and keeping up with advances in data visualizations and business intelligence over the long term.

Building Analytics Cost Employees

Why Buy?

Many software organizations are under pressure from customers or competitors to improve analytics capabilities, and they do not have the time or resources to build on their own. In fact, in every survey conducted with software providers, the top reasons for embedding with a third-party product are:

  • Cost to build and maintain capabilities on their own – It can be expensive to initially develop, provide ongoing support, and continually enhance analytics capabilities.

  • Need to get to market faster – There is usually a small window of time available to satisfy customers, differentiate a product offering, and stand out in the marketplace.

  • Desire to have internal resources focused on core application functionality – Delivering functionality with a third party makes the development team more efficient and frees up resources for your core product.

Evaluating the build and the buy options requires an understanding of the targeted functionality to be implemented, the level of integration required and a cost/benefit analysis.

Defining the Time Frame

As a general rule of thumb, we take 3-5 years as the time frame in which we compare technology implements. So how will building your own analytics solution compare to embedding an analytics solution in this timeframe?





Benefits Summary

  • To deliver all the needed functionalities the “Build” options will take 3x to 4x the time compared to the Buy option.
  • High dependency on internal team distracting them from focusing on core competency
  • The need for constant coding updates and maintenance puts added pressure on developers in the team
  • Longer go to market lead to longer time to value, 
  • This slows down customer acquisition and adoption. 
  • In turn, reducing the ability to retain customers and lower selling prices for your product. 
  • Dependence on internal development resources to implement new functionality limits the predictability of delivering over the long term
  • The “Buy” option delivers a great product with seamless interaction within a month. 
  • There is a very low dependency on the internal team, letting them focus on the core competencies of the product. 
  • Updates are performed by the embedded analytics company and integrated into your product automatically, reducing pressure on developers. 
  • You go to market sooner, in as little as 1/4th the time as the Build option, 
  • This gives faster time to value
  • Which increases customer acquisition and adoption along with customer satisfaction
  • Resulting in higher customer retention, and higher average selling prices for your product.
  • Relying on embedded analytics with a wide range of functionality improves the clarity of your roadmap and your ability to meet changing customer demand.


Compared to coding on your own, utilizing a third-party product will get more capabilities in less time. The faster path to value usually drives the “buy” decision. If you are building quantitative ROI models, that difference in time will show up as achieving a breakeven point earlier in the project lifecycle.

ROI ON Embedded Analytics

By building a cost-benefit analysis over time, you can calculate the ROI for each buy or build option. Here is the ROI formula.

ROI [%] = Benefit / Costs  - 1 


  • Benefits – This is a combination of strategic benefits (e.g., revenue increase) and operational benefits (e.g., cost reduction).

  • Costs – This is your investment to develop and maintain the solution.

  • “-1” – The formula assures that a positive ROI is achieved only when benefits exceed the costs.

If it's tough to understand the exact benefits and costs that an embed solution would offer.

To some, “build” may seem like the obvious choice for embedding analytics functionality in their application. However, even if it looks like the less costly option from an investment standpoint, it may not be the most worthwhile option. Let’s look at this graph.

Embedded analytics build vs buy chart

Suppose the desired functionality requires one full-time developer to go to market in 8 months (equivalent to $100,000). And, it takes one-third of their time to support and enhance the capabilities in subsequent years ($40,000 annually). Adding up the technology, UX/UI, platform and management cost. We end up with a total of around 259K for year 3

Now with the equivalent “buy” you don't have to any of the above-mentioned costs. It is only technology and licensing costs, which amount to approximately half the cost of building your analytics (or lower) depending on company size and the number of users for year 3. The development time goes down from 8 months to one month. With lower initial costs and quick go-to-market, you break even much sooner than if you decided to build. 

This is probably an oversimplified example, but the point is to assess both the benefits and costs when building a business case based on a comparison of ROI.

For more information on the pros and cons of embedding analytics check out The Ultimate Guide to Embedded Analytics.


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