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.
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?
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.
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 180K 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 cost and quickly 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