Guide one: building strong use cases

You see zeros and ones, we see a goldmine. Data is hugely powerful for businesses in Marketing, Sales, Procurement, you name it. But start a data project without strong objectives and you’ll be in trouble.

To win at the data game, you’ve got to:

  • Ask the right questions
  • Set the right goals
  • Measure success the right way
  • Think about your users
  • Tell a story with your data

Luckily for you, your friendly neighbourhood data hub is here to help. In our first guide, we’ll show you how to build kick-ass use cases with data.


Step 1: get some perspective

First things first — forget the data. Start very basic. What is it that you’d like to know? What are the questions you want answered? The ones you might be asked by your stakeholders, distributors and customers. These could include:

  • How can we grow?
  • How can we cut costs?
  • How can we improve customer service?
  • How can we become more efficient?

And what are you trying to achieve? If you’re exploring your customer data, for example, you’ll know if your primary goal is to:

  • find new clients
  • grow existing clients
  • retain current clients

So focus on this goal first.


Step 2: track your performance

There’s nothing worse than knowing you’ve done a great job without being able to prove it. So think about how you can measure your success. Consider your business priorities and your key performance indicators.

These could be:

  • Revenue-focused
  • Sales-based
  • Cost-related
  • Customer-based
  • Satisfaction-based

Every business model’s different, so consider the levers you can pull to make a demonstrable impact on business success. Think about where your revenue comes from and how it can increase or decrease. Think about your costs and consider whether the bulk of costs are spent on people, technology or property.

Focus on the performance trackers that matter, so that the use case will have a real-world impact on your business, and not just look pretty.


STEP 3: think about your users

Who will use your data tools? You need to design solutions that work for them.

As an example, a tool that’s designed for the Board and C-Suite will need to be very different to a tool that’s designed for a hands-on operations manager.

So think about how the user will use the tool…

  • What questions will they want answered?
  • Will they want to explore the data or just see the end results?
  • Will they access it every day or just now and again?
  • How often will they want to see updates?
  • How far back will they want to see historical trends?

The answers to these questions will impact the design of the data tool so it’s important to understand the expectations and requirements of your business users.


STEP 4 — Tell a story with your data

People love a good old story. It provides context, insight, interpretation — all the things that make data meaningful and analytics more relevant and interesting. So when you come to building your use cases, develop hypotheses or stories that you can test with the data.

Think about your users, and the trends they’ll want to see. Then start building your story:

  • Imagine if we could identify areas of underperformance or untapped business opportunities
  • Imagine if we could identify areas for potential revenue increase
  • Imagine if we could predict future business performance
  • Imagine if we could create a new data tool for your distributors to use…
  • to compare their sales/purchases to other distributors
  • to show sales/purchases by customer demographic/location/type
  • to predict future sales/purchases

It’s crucial to understand what your business users will be interested in so you design a tool that’s useful and valued by them.


So there you have it, 4 steps to data use-case bliss. To get all of this down on one page and plan your use cases like a pro, take a look at our website and download our Dataᶟ data map. This will help you spot opportunities for data quickly.

Next time…we’ll share our guide for extracting data…

We help our clients bring data together from lots of sources. This can be really complex and involve getting data from external sources too, depending on your use cases. We’ll share our handy guide in our next issue.