Business value is by no means a new topic. Still, with all of the exciting use cases produced by the Data Science market, it needs to be considered now more than ever to ensure that the projects that are being funded are aligned to a value creation process.
Purposefully taking time to articulate the business value of what the team is doing creates clarity, and makes it far easier to align to a single vision for the outcomes of that project.
Often AI projects end up in the ‘radical’ change bucket instead of the ‘incremental’ bucket where many other projects end up, why is that the case? We’re going to explore this with the lens of Data Science, to enable clarity – the discussion around business value without creating some goalposts would be possibly never-ending!
You may think that creating value (or at least illustrating it) is the job of whoever is managing the project – which is not untrue – but for a truly successful project, this should be deliberately considered by all members of an AI squad. Purposefully taking time to articulate the business value of what the team is doing creates clarity, and makes it far easier to align to a single vision for the outcomes of that project.
To start to define project value, looking at what drives it at a fundamental level is an excellent place to start:
- Competitive advantage (how might we get an edge over our rivals?)
- New products/services (how might we offer more value to customers?)
- Brand awareness (how might we increase brand sentiment?)
- New ways of working (how might we optimise the workforce?)
- Captial increases, or cost decrease (how might we save cost in operation?)
- Efficiency gains or optimisation (how might we make a process more efficient?)
What these all boil down to is empowering employees in some form. An increase in efficiency may enable an employee to focus on other aspects of their role, or a new product the could allow employees to more effectively serve their customers. Potentially the work you are doing may directly affect customers, which can also affect employees within a business. An example of this may be creating an intelligent marketing campaign, which makes it easier for employees to drive sales and hit targets, which is a frame of reference to analyse if the project you are undertaking is a useful endeavour.
“Who does this affect, and how does it change the way they work?”
A good first question to ask yourself and your team is “Who does this affect, and how does it change the way they work?” from that simple statement it can help to formulate precisely what impact you will have on the business (or the businesses customers). Another thing to consider here is alignment to the vision of the employee(s) you are aiming to target, more on this further on. It may be difficult at the start to agree on a single answer to that question, but as your team starts to form that answer, it enables you to very succinctly explain the value of the work you are undertaking. This approach helps you communicate the value to anyone that asks, regardless of their level of understanding of Data Science as the question frames it purely in a people lens.
You can also use this type of logic to prioritise and understand what is most important to your organisation, and to illustrate the outcomes of the project you have undertaken. Articulating value needs to be deliberately crafted, you can let the work speak for itself, but usually, these types of projects are complex and advanced. Allowing this type of work to speak for itself may result in only a small amount of people understanding. Consider the audience; roughly audiences can are split into four categories within a business (for both projects within your own business, and in consulting considering the company you are working for):
- The project
- Program/portfolio the project sits in
- Business unit
- Entire enterprise
Depending on who you are presenting to, there are very different visions, concerns, and priorities at each level. Your narrative for the project needs to suit each category within the business; for example, the project may care about the tech used or the business unit may be interested in the capabilities that are being added for future use. On top of this, the entire enterprise cares about the bottom line effect of the efficiency changes; and all of theses lens will be taken on your project, so best to deliberately craft them!
Asking the question mentioned earlier “Who does this affect, and how does it change the way they work?” at each of those levels can help you to prepare for exactly what you need to communicate to different types of stakeholders. The objective here is to communicate in the language that stakeholders understand at different levels, and promote the success of your project. If this isn’t considered, some very technically accomplished projects can be forgotten as they haven’t clearly articulated their value – whilst the tech may have been fantastic, the tech is hard to understand from the outside.
As data science projects move further and further toward becoming mature enterprise projects, taking the first step to define specific business value and the drivers behind it may be the differentiating step in your project reaching success.
Jeremiah is an Director at PwC leading a Data Advisory team and founder AI Specialist Blog. He has received the ACS ICT Professional of the Year (2019), Top 25 Analytics Professionals Australia (2021, 2018). He has written articles for the AFR, IBM, and LearnDataSci.
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