It is important for most companies today to have data science platform integrated into their workflow. Data science platforms allow integrating and exploring data from various sources, while coding and building models generating insightful results. It also helps in deploying those models into production. It not only helps in the better analysis but eases up the work for data scientists. There are many data science platforms available today, like Alteryx and H2O, which make it easy for companies to deploy analytics and data science.
Some of the key requirements for data science platform is flexibility, scalability, availability of open source tools, among others. While data science platform offers many benefits, it calls for a lot of investment. In this article, we list five such key factors and questions that a company should keep in mind before investing in a data science platform.
Reasons For Investing In A Data Science Platform
It is extremely crucial to first sort the reasons for investing in a data science platform after having carefully analysed why wouldn’t you instead build one. It is important to have a clear business problem statement that you would want to solve and figure out ways of collecting data before investing in data science platform. If the business problem or the reasons to deploy data science platform is not very clear, it would not bring out any productive result. It is, therefore, necessary to analyse reasons before investing in it.
Availability & Accessibility To Data
Just setting up a data science platform isn’t enough. It is important to make sure that there is a significant amount of reliable data in workable condition to perform analysis. It is also important to analyse if your company can collect and process such information, as it may come as an expensive affair considering the time and money spent in collating the data itself. Without workable data, the analytics platform would be of no much use.
Total Cost Of Ownership
Integrating a data science platform can be a costly affair, costing millions of bucks. Not just the software cost, but there are other costs involved in terms of efforts and services that go into installing and implementing data science platform. It is, therefore, necessary to completely understand the costs involved before investing. Based on the budget, companies can go for data warehousing, which is ideal for companies that require data storage and analytics, or visualisation systems or both.
Availability Of The Right Team & Resources
While most data science platforms bring about automation, there is still a need for the right team to be in place. For instance, data analysts and data scientists are required to work on new insights and predictions by updating data processing rules and models as per the business needs. The entire should be set up properly as integrating data science is much more than integrating just another tool for your data science team. Deploying a data science platform will drastically change the way business is conducted.
Insights For Decision-Makers & Non-Technical Users
Data science platform should be able to ensure that it can optimise the communication between data scientists and decision-makers. While most data science platforms may need a tinkering from data science professionals as mentioned above, it is best to analyse if executives and non-technical users are able to generate their own reports without having to interact with data models that power it. It will ensure more holistic use of the data science platform.
Some of the other key considerations to keep in mind before deploying data science platforms are analyse the risk factors, its ability to deploy multiple versions of the same model for testing, if it allows effective collaboration, use of multiple languages and packages, among others.