Build vs. Buy – A decision-making framework


Without careful evaluation and validation of the analytics or data science use cases usually “BUYING” a product may end up leading to a sub-optimal solution. Additionally, few products are customizable basis nature of client’s own data, industry nuances, or any specific requirements.

Whether you decide to build or buy analytics services you must align the solution with your business goals.

In this article, we are sharing a build versus buy framework to help you consider the opportunity costs and make an informed decision for a custom analytics and data science solution.

You are tasked with solving a specific problem; one that your company may be facing or pain points that your customers are looking to overcome. And you have decided to solve that problem by using a data-led solution and with the exponential growth in computing technology many specialized software vendors is rising and many innovative products are available on the market which leads to the question

Key Factors to Consider Whether to Build or Buy Software

Core competencies of the analytics vendor are the key points to consider when deciding if you want to build or buy. Implementation of analytics projects consist of various factors adding to the timeline of the project as companies invest a lot of time and resources into building software, while buying software can also be expensive in other ways.

Maintenance & Support

People usually think that the service level of internal analytics teams is better than that of vendors. For “building” automated machine learning models, the internal analytics and ML specialists may possess more control over the solution, thus there is better maintenance and support quality. However, contract for services provides better insights Hand it is easier to add new features or enhance a solution depending upon fast-changing business environment.


ROI is the bottom-line look at your ‘Build’ vs. ‘Buy’ decision. Implementation cost is usually high in case of customized solution, but total cost of ownership is low and always generates better ROI as it includes the cost of developing, maintaining, and upgrading the analytics solutions over time. The cost of buying an analytics software consists of the purchase price and the cost of licensing and maintaining the software. By comparing the price gap between these two approaches, you can get a sense of which option is more cost-effective.


The time required to build software in-house can be significantly longer than the time required to buy software from a third party. If the organization needs a quick solution, in this case, purchasing an existing solution may be the better option.

Advantages of buying based Solution

  • Lower costs: As explained earlier, the costs involved in building an in-house solution usually tend to be massive. When you buy a third-party solution, you don’t have to spend on maintenance, operational, or R&D costs.
  • Faster time-to-market: With nothing to build, upgrade or maintain, your effort in testing software is significantly reduced.
  • Access Mobile Devices with No Maintenance: Maintaining thousands of mobile devices in “Build” mode is not a simple task. Keep in mind that, to stay effective, your in-house lab will have to keep buying and making new mobile devices available for testing 24×7.
  • Access to better support: A third-party solution worth the money will give users access to dedicated industry experts who are accustomed to solving complicated problems and figuring out how to cater to customers’ expectations.
  • More Features: Third-party vendors will regularly implement new features to stay competitive and accommodate newer developments in the domain. With that handled, your team no longer has to think “But can the platform support this?” when trying out a new framework or testing technique.


The “build vs. buy decision” is not a science, but more of an art. In this decision-making process, nothing is absolute.

When making the final decision to build or buy the software, there are some key factors to consider. Cost, the time required, quality, flexibility, and risk are all essential considerations. The decision depends on the specific requirements of the organization in question How long do I want my project to take? What will it cost me? Is this project going to be worth it? Will it provide value for our organization by improving productivity and efficiency?

Check out our various solutions on how Transorg analytics build a customer service model for its clients.

Want to learn more about TransOrg’s value proposition, write to us at





Leave a Reply

Your email address will not be published. Required fields are marked *