Finding the Use Cases
So you’ve gathered the data, maybe hired some data scientists, and you’re looking to make a big impact.
The next step is to look for some business problems that can be solved with analytics – after all, without solving some real business challenges you’re not going to add much value to your organisation!
As you start to look for analytics use cases to work on, you may soon find yourself inundated with a range of possible projects. But which ones should you work on? How do you prioritise?
Mango have spent a lot of time over the last few years helping organisations to identify, evaluate and prioritise Analytic Use Cases. Picking the right projects-—particularly early on in your data-driven adventure-—will have a significant impact on the success of your analytic initiative. This article is based on some of the ways in which we coach companies around the building of Analytic Portfolios and what to look for in projects.
Evaluating Analytic Use Cases
The prioritisation of analytic use cases will be largely driven by the reason your data initiative was created and what ‘success’ for your team really looks like.
However, for this post, I’m going to assume the aim of your initiative is ultimately to add value to the organisation, where success is measured in financial terms (either saving money or adding revenue).
Generally, you’ll probably want a mixture of tactical and strategic initiatives – get some quick wins under your belt while you’re working on those bigger, longer-term challenges. However, when you’re looking at projects to work on you should consider a number of aspects:
- The Problem is Worth Solving
This might sound obvious, but a big factor in assessing an analytic use case is the potential value it could add. Delivering a multi-million pound project that decides what colour to paint the boardroom isn’t going to win many fans.
Ensure you understand:
- How delivering this project would add value to your organisation
- Exactly how that value will be measured
- The Building Blocks are in place
Understanding the ‘readiness’ (or otherwise) of a project to be delivered is a major factor in determining whether to prioritise it. Key aspects to consider include:
- Data – is there enough data of sufficient data to solve this challenge?
- Platform – is the technical platform in place to enable insight to be derived?
- Skills – do you have the skills required to implement the solution?
- Deliver – is there a mechanism in place to deliver any insight to decision makers?
- The Analytic Use Case is Solvable
The world of analytics is awash with marketing right now, promising silver-bullet solutions based on Machine Learning, AI or Cognitive Computing. However, the simplicity or otherwise of a potential solution should be considered when prioritising a use case. You don’t want to end up with a portfolio of projects whose solutions are at the periphery of what’s currently possible.
- The Business is Ready to Change
This is–without doubt–the primary factor in the success (or otherwise) of an analytic project. You could have the best data, write the best code and implement the best algorithm – but if the business users don’t behave differently once the solution is implemented, the value you’re seeking won’t be realised.
Before you build, make sure the business is willing to change their behaviour.
Evaluating possible projects in this way can help you to build a portfolio of Analytic Use Cases that will add significant, measurably value to your organisation. Moreover, making the right decisions early can help you build momentum around data-driven change, leading to a more-engaged business community ready for change.
Mango Solutions can help you navigate this process successfully. Based on insight and experience gained over 15 years working with the world’s leading companies, we have developed 3 workshops to help overcome some of the common challenges and roadblocks at different stages of your journey.
Find out which of the three workshops would be valuable to your organisation here.