We recently covered an article on how data science platforms are crucial to be integrated into the company’s workflow. Not only does it integrate and explore data from various sources, but it also allows coding and building models helping in generate interesting insights. While we discussed key factors to be kept in mind before investing in data science platform, in this article, we shall discuss if you should buy the data science platform at all or build one on your own.
Build Or Buy?
There is no doubt that data science platforms have become a crucial part of organisations to accomplish data crunching and speeding up the process of deploying models instantly. But the question that often arises is should the data science platform be built from scratch or bought from external service providers. While most companies today have highly skilled professionals who have the capability of building solutions from scratch, there are pros and cons to both sides of the story.
The dilemma is often deciding between paying licensing fees for pre-built software platforms or investing in a team that will create custom software to solve issues specific to the company. There are a lot of other factors that come into play such as time required, cost, staffing, expenses to get your solution up and running, the urgency of business requirements, among others.
Some experts believe that it may make sense to build a custom solution when there is no solution in the market to meet your needs, whereas others are of the opinion that building your own platform can have advantages such as flexibility and more compatibility with the existing environment. But the path can often be expensive and time-consuming. There cannot be a one-size-fits-all approach and below are certain points that can be kept in mind.
Key Determining Factors
Cost: Cost is an important determining factor and it is advisable to quickly compare the cost of buying a platform and building one. It is often observed that building an analytics solution and platform from scratch can be an expensive affair compared to buying one.
Time: Considering the time required to deploy a platform is another key consideration. It goes without saying that developing, designing and implementing a custom platform can take a significant amount of time. It may result in the loss of opportunities in conducting other important tasks. Whereas deploying a ready-made solution is often easy and hassle-free.
Flexibility: When buying a platform there are often chances that it may not integrate with other tools that the team uses. Whereas building your own platform can give the flexibility to build a model that is full-featured and aligns with the company’s workflow.
Boosting existing infrastructure: It comes as a key consideration while building a data science platform. For instance, if a company has run successful deployments in a specific language or specific data warehouse, it may be easy for them to build a solution on it to leverage existing technology stack.
Cross-pollination of ideas: If that is the end goal, purchasing a platform can be more beneficial as compared to building in-house as it brings the best practices from other vendors and cross-pollination of ideas to get the best benefits of a software vendor.
New Features: Software and data science platform providers constantly update their offerings and continuously bring improvements in their products by adding new features and implementing new technology. This has a definite benefit over building your own platform where bringing iterations at a faster pace can be quite challenging.
Support: Lastly, one of the major advantages of buying a data science platform is that it comes with a support system in case you are facing challenges in deploying it. It saves much of the time that may be lost in debugging and reworking on the challenges faced while installation and deployment.
As mentioned in the above points, it can be seen that buying a data science solution has more advantages than building it from scratch. It wins in key determining factors such as cost, the time required, getting the industry best practices, cross-pollination of ideas and support bestowed. Whereas building a data science platform wins, thanks to the flexibility and improving the existing infrastructure and expertise.
Making a choice often requires more strategic moves and intense decision making. Often, the hybrid solution also works effectively where a software framework can be bought, on top of which a platform could be built with specific requirements. It is obvious that data scientists like to build things from scratch and they may often prefer the latter option but it is necessary for them to consider the pros and cons judiciously.