As everyone knows, getting started on increasing the role of predictive analytics and machine learning is hard work, and it's even harder to get started if you haven't yet chosen a data tool. The complexity of choosing such a tool depends upon your needs and your organization's policies and processes, but one item is essential no matter what: an RFP. But putting together an RFP is hard work, too. That's why we've put together a template you can use to give you a head start on the process.
Whether you’re a small team of data scientists or a large organization trying to make data analysis available even to non-technical roles, the predictive analysis and machine learning tool you choose will help to define how you work with data now and in the future. The key thing to do is to balance addressing your immediate needs along with anticipating your future requirements.
Immediate Needs: What You Know That You Know (or Don't Know)
Immediate needs depend upon your team, your technology, your industry, and your specific organization. Teams have different skill levels and types of expertise, such as the coding languages they use and the techniques they are familiar with. Technologies range from data lakes to visualization solutions, and new technologies emerge all the time. Industries have regulatory requirements that evolve as different regulatory bodies weigh the role of policy in different sectors of the economy. And your specific organization has its own processes and use cases that will drive adoption of and value creation from data analytics.
Future Requirements: The Unknown Unknowns
Future requirements are much more difficult to define. Which skills will be required in three years? And how difficult will it be to recruit talent with those skills? Which new technologies will emerge, and will your existing technology be compatible with them? What will the regulatory and economic environment look like? And how will your own organization’s culture and processes evolve?