This blog was authored by: Haydn Durrant | 5 April 2024

 

 

“If leaders really want to create a data driven culture, the journey starts with them! Leaders in a data led business ask data led questions, challenge assumptions and relentlessly focus on measurement and testing.”

 

 

Having worked on data and AI initiatives for over 25 years, it continues to amaze that in 2024, organisations continue to struggle to realise and demonstrate the business value of their investments in data. Everyone appears to agree that investment in data is important, expensive, and problematic in equal measures.

 

 

A fundamental problem is that the focus of data projects continues to be on data, analytics and reports rather than business outcomes and the necessary insights to support their measurement and achievement.

 

 

An inescapable fact is that data, analytics, machine learning models or reports in and of themselves do not deliver any business value. It is only when people use them to influence their decisions and actions that value is realised. Therefore, when we are designing and delivering data solutions it is crucial that we spend as much time and effort focussing on outcomes, actions and decisions as we do on data and analytics.

 

 

This situation is often exacerbated by a lack of sufficient senior business sponsorship and direction for data and AI initiatives.  These are often led and delivered by technology or data teams that can suffer from insufficient alignment to business strategy or engagement from those within the business whose decisions and efforts drive business performance. These teams, whilst invariably excellent at technology delivery, data engineering and training models, tend to focus on the areas where they are most comfortable and proficient. Databases, models and reports and their conversations with the business rarely stray outside of these areas.

 

 

It is encouraging to see that there is a growing acceptance that organisations need to develop a mature data culture at their core, driven by executive sponsorship, often via a Chief Data Officer. This increases the likelihood that teams and departments will take more ownership of their own data and what they do with it.

 

 

So, for data teams looking to jump on board, who might be better at talking about data and reports than business outcomes and benefits, and who may struggle to convince their business stakeholders that it’s worth investing the time and intellectual horsepower to define business value driven requirements, here are 10 reasons to help make the case:

 

 

1. Increase likelihood of delivering measurable value, enable the measurement of success and ensure alignment with business objectives.

 

Addressing the most obvious and important point first, you should do this to ensure that you can demonstrate that the investment in data is delivering business value, and that this is aligned to and supporting the delivery of business objectives and priorities.

 

 

Data-driven insights can help to:

 

• Decide what to do, when and where.
• Enable alerting on important business events, enabling more timely and effective resolution.
• Help to determine whether a problem or opportunity exists or ensure that we are addressing the right problems.
• Enable us to measure progress and provide evidence that initiatives are providing the expected benefits.
• Understand what went wrong (or right) and why

 

 

2. Support the journey to a mature data culture by developing data fluency skills.

 

A distinguishing attribute of an organisation with a mature data culture is that its people intuitively understand how to use data to drive decision-making that is aligned to strategic objectives and key performance areas. Within these organisations, this capability is cultivated and invested in as a core staff competency.

 

 

The lack of this competency can make discussing, analysing, and confirming business value in requirements discussions challenging. But by persevering with this, teams can begin to acquire valuable exposure to this way of thinking and the development of this skillset can also begin.

 

 

So, this discussion proves to be valuable both for the project team, who will deliver the solution, and the business users who will be responsible for using it to drive benefits realisation.

 

 

3. Enable data teams to unlock more creativity and value.

 

Often teams within immature data cultures (if I can get away with calling them that), can find it an annoyance when a data team begins to ask what business value a particular reporting requirement will support. “We know what we need so just focus on the data and the reports and we will worry about how we use them”. Or possibly “these are the reports we have always had, so what’s the value in us taking the time to explain them to you?”

 

 

When the data team understand the intended business context and usage underpinning the requirements, they can make better use of their own knowledge, experience, and expertise to suggest and help make decisions around the simplest or most effective solution.

 

 

4. Provide a firm basis for scoping, prioritising, and budgeting decisions.

 

There are various reasons why a data initiative may need to make difficult decisions around scope to align with time or budgetary constraints.

 

 

If we have a clear understanding of business value aligned to business requirements, we can make better prioritisation decisions around what must be delivered now and what can be deferred for a future release. We are also better able to assess and manage the impact of prioritisation decisions.

 

 

5. Improve solution design and quality of delivery.

 

It is a recognised truism that it is much quicker and cheaper to fix a problem or misunderstanding at the analysis stage than it is when the “finished product” is delivered.

 

 

Even in cases where a solution is being prototyped by the business and productionised by a data team, solution design that is informed by clear business requirements is more likely to be “right first time” and less likely to result in costly changes.

 

 

There are several additional ways in which this activity enables more effective solution delivery. For example, test planning and execution can be far more focussed, ensuring we are testing and confirming the key elements of a solution that are critical to enabling benefits realisation. Also, it enables us to sense check acceptance criteria.

 

 

6. Enable focus and basis for business readiness planning.

 

We have already highlighted that successful data investments rely equally on business benefits realisation as they do on solution delivery.

 

 

Understanding at the requirements stage what is required from the business to achieve success forms the basis for effective business readiness planning and execution. This is an important consideration as it can affect the feasibility of the business case.

 

 

7. Provide better success stories.

 

Driving benefits realisation results not only in greater business success, but also in more compelling business success stories. In the early stages of a maturity journey, these stories are vital for demonstrating the value of the initiative, thereby winning hearts and minds, and growing support for ongoing investment and engagement.

 

 

8. Build more productive trust between data teams and the business.

 

Sometimes it can feel like the business are from Mars and data engineers are from Venus.

 

 

Establishing successful and productive collaborations between these teams through business value conversations can build trust and more productive working relationships.

 

 

9. Make a stronger case for Data Governance.

 

A clear understanding of how data can support the achievement of business value can highlight key data quality and master data management issues.

 

 

The project might realise the data isn’t as valuable as it could be, because there isn’t the consistent definition, master data isn’t managed or data quality is poor.

 

 

Often data governance initiatives can feel like they are attempting to boil the ocean for little business benefit. This can make it difficult to secure the required engagement from the organisation to make the initiative successful. Being able to align data governance initiatives to business outcomes can increase support and success.

 

 

10. Highlight key business champions who can drive benefits realisation.

 

Last but not least, discussing business value with the business often highlights those people who already “get it” in terms of what the business problems are and how data can better help deliver the solution. Furthermore, these champions often demonstrate the drive and influence required to ensure that benefits realisation is successful. These champions are essential in the early stages of a journey to a mature data culture.

 

 

So, there we have it, whether we call it benefits planning or business value analysis, finding a way to have a successful conversation between the data team and the business users on this subject is essential to success.

 

 

This is a journey that we are all on within the data community. It would be great to hear your own experiences. Do these things get discussed on your data projects? What level of resistance (if any) do you encounter and how do you overcome it? Please feel free to reach out to discuss your organisations’ data journey.