Its not about the Data..

Why So Many Data Projects Underwhelm—And What We Can Do About It

Over the past 26 years, I’ve worked across a wide spectrum of data initiatives—from warehouse builds and cloud migrations to analytics delivery and platform transformations. While the technical execution of these projects has often been the primary focus, I’ve come to realise that the greatest business value emerges not from the technology itself, but from how well it’s aligned to solving real, specific problems. In this article, I reflect on why so many data projects fall short of their potential, and why a shift in mindset—from technical delivery to business outcome orientation—is urgently needed.

The title of Lance Armstrong’s 2000 autobiography was “Its Not About the Bike” . Later revelations and disgraces notwithstanding, the point made was that his success was a result of his hard work and mental strength, rather than on the quality of his machinery. This idea is very applicable to the challenge of achieving measurable business success from data initiatives. Success here is not primarily about the data or the technology, it’s about maintaining a clear focus on solving business problems.

In the majority of data initiatives that I have been involved in, the focus has been upon technical delivery of a capability to the business (an analytical environment or suite of reports) or of a technical outcome (migrating from one technology to another). I would say however that the rare minority of projects which were approached with a specific business problem or outcome in mind have delivered the most measurable business value.

An example was for a well-known global logistics brand where the head of customer billing suspected that there was a category of traffic that was being under-billed to customers. The evidence was hidden within the legacy billing system and he didn’t have the capability to extract and analyse the data. A proverbial needle in the haystack. We spent time with him and mapped out the logic that needed to be applied to identify the category of traffic, a kind of logical marble sorter, and then extracted a snapshot of data from the billing system that we then applied the logic to before presenting back our findings. Straight away we identified a substantial £M value of unbilled traffic. This was a very quick financial win which was used to justify the investment in a strategic data initiative overnight.

In my experience, success stories like this are rare and from discussions I have had and articles I have read I would say that this remains an industry wide issue..

The focus of data delivery (and therefore investment) is reporting, data and technology rather than on business outcomes and what we need to do in order to successfully achieve and measure them. This is a wider challenge than data and the starting point shouldn’t be data (or technology).

Data has no inherent business value. Its only when it is used to influence a decision that leads to meaningful action that measurable value is realised. I think of the data value chain like this.

Measurable Business Benefit / Outcome <> key decisions <> questions & insights <> reports & analytics <> data

Its critical to work from left to right and to have every link in the chain well defined and delivered to achieve measurable business success.

So why do we find ourselves in this situation? These are my thoughts based around my own experience and observations.

Historically data initiatives have been owned and driven by IT and as a result the language used has centred around technology, reports and data. There is an unwritten but accepted rule between IT and the business that when they work together on data projects this will be the focus and everyone is comfortable with this accepted status quo. In general, the business are not inclined to explain what they do, the challenges they face or to engage IT in joint problem solving and “techies” are more comfortable focusing on their area of expertise.

The data industry remains for the most part geared around delivering software solutions. Recurring revenues are secured through the utilisation of cloud technologies and professional services revenues through the configuration and support of these technologies. Whilst technology is a key enabler to success, there is too much emphasis on this, leading to a situation where:
• Professional services companies have underinvested in their available skills and experience base to help clients plan for and deliver business value.
• Clients engage professional services companies as technical delivery partners rather than as strategic partners.

I think also that this situation is exacerbated by an inherent expectation within those coming into the data industry (quite rightly) that they need to focus their professional development on technical delivery skills if they want to progress and accumulate personal brand value. So for this reason, data people are not acquiring the skills and experience to have the business value conversation with the business. This leaves them in a position where they are substantially, (if not wholly) unable to challenge the client or business on the benefits associated with the requirements they add to the development backlog.

There is also an investment required on behalf of the client yto impart the necessary business insight and knowledge to those within the data team. This translates into increased time and cost as part of the delivery investment and can be exacerbated if key members of the data team move on to other projects and are replaced by others who then need to be trained up.

Considering the key decision makers within the business, those who can really drive the value from a data project, they may not possess high levels of data literacy. This is to say they do not possess the skills to wrangle insights out of data and to present compelling findings to reinforce and support their decisions. As a result, the quality of the requirements that they are able to provide the data team and their skills to successfully realise the benefits from delivered data solutions may be low. I think this challenge is now recognised and improving as organisations are beginning to invest in developing these skills. However, it remains a significant contributory factor to underwhelming project performance and is exacerbated because as we have seen earlier, the data team are unable or unwilling in many cases to challenge these requirements.

It is also worth considering how well businesses set strategies that decompose down into measurable targets across the organisation. The prevalence of initiatives and ideas like OKRs suggests that there’s a widespread recognition that organisations need to get better in this space. This potentially represents a challenge for data teams who would ideally like to use a business planning cycle like this to drive their own delivery priorities.

Finally I would say that in order to successfully deliver a data value chain that results in business success, we need to iterate, fail, learn, refine and evolve. Data projects tend to proceed with the expectation that they will get it right first time. This is unrealistic. As we progress, we will discover limitations with the data, we will acquire insights that raise new questions that had not previously been considered or planned for delivery. As with all agile endeavours, we need to adopt a growth mindset and establish expectations with all stakeholders that, whilst we will demonstrate measurable progress on every pass, there is an inherent risk that the time and cost associated with data projects will increase.

If we want data initiatives to deliver the business value they promise, we must stop treating data as the starting point and begin with the problem we’re trying to solve. That means shifting our mindset—from technical delivery to outcome orientation—and investing in the skills, conversations, and iterative practices that make transformation possible. Data alone doesn’t drive success; it’s the decisions, actions, and business clarity built around it that do. Until we embed this thinking into how we plan, deliver, and measure our data programmes, we’ll continue to fall short of the impact we’re capable of achieving.

If you’ve ever led, sponsored, or delivered a data project that felt underwhelming despite best efforts, I would love to hear your thoughts and experiences. Let’s challenge the status quo together.