Data science, big data and AI are current buzzwords, and many companies are rebranding their business intelligence products with these definitions to get a more significant market share. Although some of these companies have indeed created valuable products and service bundles, others are just coating their old offerings in a shiny new package to answer clients’ demands.
With such a broad offering available it has become difficult to choose the right vendor, and an appropriate structure needs to be put in place as an algorithm to follow during the selection process.
What do you want to achieve?
It is recommend that you start any data science acquisition process with an inwards analysis. Why do you need these tools? Which gaps are you trying to fill? Do you already have a defined scheme and just need the answers? Give accurate definitions of what you are trying to achieve, like better segmentation, less churn.
It is a strategic mistake to buy a data science solution just to keep up with trends and annihilate the fear of missing out on this opportunity before having a clear understanding of what success would mean for this project.
Minimal standards
Before contacting any vendors, it is wise to create a baseline through an initial report of the existing state. Based on this first assessment, you should create a list of minimal must-haves for the selected vendor which will help you rule out a lot of undesirable offers.
Don’t settle for anything less than your minimum list and come up with an evaluation system for the additional features or ‘nice to haves’. By creating an algorithm, you are protecting yourself from emotional decisions based on the vendor’s marketing strategy.
What cost structure can you afford?
Budget is the cornerstone of any project, and there is no way around this. An expert from InData Labs explained the alternatives in this case, which are summarised as:
- Hiring data science experts. This is the costliest option since it means going through the hiring process and adding more people to your payroll. Only advisable if you intend to make data science one of your revenue streams.
- Staff augmentation. Raise the skills of your existing staff by bringing in experts to help them leverage some time-sensitive issues.
- Project-based. A cost and time effective approach which is highly suitable for clearly defined projects or to kickstart a collaboration with a vendor, during a trial period.
- Hybrid centre of excellence. This partnership between in-house teams and external specialists can be done through an ongoing process inside a CoE. An excellent choice for companies that are considering the opportunity of having an in-house team at some point in the future.
- Outcome-based.
