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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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How To Define A Data Use Case – With Handy Template

2 July 2021

good data strategy will help you clarify your company’s strategic objectives and determine how you can use data to achieve those goals. The data uses that you identify in this process are known as your use casesIn other words, these use cases are your key data projects or priorities for the year ahead 

When I work with companies to develop their data strategy, we usually identify between three and five data use cases – any more than that and your data strategy risks becoming cluttered and unrealistic. 

Data use cases will be different for every company, and will be driven by your overarching business strategy. However, some examples include: 

  • Understanding and improving employee engagement  
  • Delivering a more personalised customer experience 
  • Optimising prices
  • Developing smarter, more personalised products or services 
  • Preventing fraud 

Whatever data use cases you’ve identified, this article – and the accompanying Data Use Case Template (see below)  is designed to help you flesh out your use cases, define your data projects in more detail, and build a thorough data strategy. It’s a good idea to have the template open as you read through this article (

download it from here

). 

Within the template, there are 11 sections to complete. They are: 

1. Link to strategic goal

Because data should always be used in a strategic way, this section is about linking the use case to a specific organisational objective. So, if we take our first example use case above, understanding and improving employee engagement, this may link to a strategic objective to boost our employer brand.  

Objective and business questions;

Here you want to define your data-related goal in more detail and identify what questions you need to answer. Continuing with our employee engagement example, this section might say something like: 

The objective is to move beyond the big annual staff survey to create a near real-time picture of employee engagement in our business. This will help us answer the following questions: 

  • How engaged are our employees at any one time?
  • What can we do to increase employee engagement?
  • How successful are our employee engagement initiatives – to what extent do they impact engagement? 

2. Measures of success (KPIs)

Here you want to define what success looks like for this use case, and how you plan to measure progress. So, our fictional example may involve KPIs such as regular pulse surveys, Employee Net Promoter Score, absenteeism, and employee turnover. 

3. Use case owner

Who will be responsible for this use case? If you don’t have one person responsible for making it happen, it may never get done. The owner of our example use case may be a HR manager. They’ll need to work with others, of course, but the HR manager is the one with ownership of this use case. 

4. Users and data customers

Data customers are the people who’ll be using the data and learning from the insights generated. In this example, our use case owner is clearly a data customer/user as well, but other customers will include leadership teams and managers across the business. 

5. Required data

Here you drill down into the data that you need for this project. This may encompass structured data (e.g. databases and spreadsheets), unstructured data (e.g. social media posts), internal data and external data. In the interests of data diversity, it’s a good idea to combine different data sets to create as rich a picture as possible. You’ll also need to identify whether you already own the data. If not, can you collect the data yourself or access it from third parties? 

So, in our employee engagement example, we could combine internal and external data by using internal surveys and cross-referencing our Employee Net Promoter Score against external industry benchmarks. And we could combine structured data and unstructured data by looking at absenteeism rates and freeform answers from employee interviews and surveys. We already have some of this data to hand (absenteeism rates), but we’ll need to set up a method to conduct frequent employee pulse surveys. 

6. Data governance

This area encompasses all the things you need to do to keep data safe, and ensure it’s used appropriately. As such, data governance includes data quality, ethics, privacy, ownership, access rights, and security.  

For our employee engagement use case: 

  • We’ll need consent from employees to gather and use survey data.  
  • In the interests of ethical data usage – and to ensure more accurate, honest results – survey data should be anonymised. 
  • As most of the data is our own internal data, we don’t have any ownership or access issues to worry about. 

7. Data analysis 

This section is all about turning data into insights. There are lots of analytics options, including text analytics, image analytics, predictive analytics, and many types of business analytics. 

One useful method for our employee engagement use case is text analytics. This can be used to analyse survey responses, interviews, and even emails or social media posts (if we wanted to go that far) to extract insights on how employees really feel about the company. 

8. Technology

Any data project will have implications for technology and infrastructure. So here you need to identify what those implications, challenges and requirements are. In very simple terms, this means identifying what software and hardware you’ll need to collect and store data, analyse the data and communicate results. 

For example, we might need to invest in a third-party employee engagement platform that can conduct regular, short pulse surveys with our employees. 

9. Skills and capacity

What skills do you need to make this happen? And do you have those data skills and capacities in-house? If not, do you need to train staff or will you outsource certain tasks? Perhaps you’ll need a hybrid of in-house and external skills.  

To continue our very simple use case example, let’s say the suggested pulse survey software platform comes with an easy-to-use analytics element that allows people in the business to slice and dice the data, without any data science knowledge. So the need to recruit new skills is minimal. However, people will need to be trained on how to use the system.  

10. Implementation and change management

Every project will encounter implementation challenges, so this is your opportunity to identify potential roadblocks and implementation requirements, and ensure that your plan doesn’t get derailed. 

For example, when implementing new employee pulse surveys, staff and leadership will need to be educated on why this system is being implemented and how it will benefit the business.

Where to go from here

Repeat this process and fill out the template for each separate use case that you’ve identified. This will help you evaluate and prioritise your use cases, so you know which ones to tackle in which order.  

Then, once you’ve fleshed out each of your separate data use cases, and prioritised them in order of urgency, you can begin to complete your data strategy. I have a template to help you with that, too – check out my related article ‘How to Complete a Data Strategy – With Handy Template’.  

You may also be interested in reading about: 


Data Strategy Book | Bernard Marr

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