The odds are not in your favour; most investments in data fail to generate value. I recently spoke with a CEO of a large organisation, who confidently and factually stated, “I have never seen a data project succeed.” Unfortunately, the stats support his sentiment. Running a single data project is unlikely to succeed, and neither is running two. The stats show that you would need to run seven full-scale data projects to have a chance that one will pay off. There is a lot of time and resources required for data projects, so running seven would be an expensive venture. In the UK, we spend £24 billion on data projects annually, and about £19 billion (80%) was lost to unsuccessful projects. That means more money is spent on unsuccessful data projects yearly than Israel spends on its military.
Why are these investments failing?
Organisations today are overwhelmed by the abundance of data at their disposal. From ERP systems to customer data, e-commerce analytics, and social media metrics, the sheer volume of information often leads organisations to adopt two misguided approaches:
1. The technical data project: Data and technical teams frequently believe that immense value can be unlocked by first concentrating on cleaning and organising the data, replacing the tools for data collection, addressing data quality issues, and creating advanced storage and access mechanisms. This belief is erroneous and has consistently proven to be ineffective. A recent survey revealed that while data professionals equate success with a well-organised database and high-quality data pipelines, business leaders don’t share this enthusiasm.
2. Immediately pursuing seemingly “interesting” use cases: To sidestep the issues arising from the first approach, some data teams hastily choose what they perceive to be an engaging use case, generating genuine enthusiasm and excitement. Sometimes this works. However, this often results in the opportunity being small, challenging to implement, and rarely utilised. Selecting a use case because it is interesting usually leads to costly, inefficient implementations that fail to deliver on their promises.
Both approaches are inherently flawed due to their lack of strategic focus and misalignment with the organisation’s overarching goals. As a result, executives struggle to link the outcomes of the data initiatives to the strategic priorities and business results that matter to them. Without a strategic organisational focus to craft a compelling and comprehensive value story, data and analytics will be perceived as an inefficient cost centre that fails to demonstrate a return on its investment.
The 2022 Gartner Chief Data Officer Agenda Survey revealed the stark reality of this situation: a staggering 71% of respondents reported not achieving a measurable return on investment (ROI) from their data and analytics (D&A) initiatives. The repercussions of this failure can be disastrous.
The implications of this failure are staggering.
Organisations must prioritise investments that generate opportunities and value in a world characterised by increasing business disruption and uncertainty. Failing to demonstrate the value of data can result in reduced budgets, missed business opportunities, eroded confidence in data, and even job losses.
Consider the Chief Data Officer (CDO). Brought in specifically to generate value from data. They are now described as one of the most unstable c-suite positions with an average tenure of about two years – hardly enough time to get their feet under the table. Shortly after their appointment, the excitement around the transformation that they promise begins to dwindle, and the honeymoon often ends abruptly around the 18-month mark. It is no coincidence that 18 months is the average duration of a warehouse project addressing quality, governance, and data management.
Nonetheless, there is hope. CDOs or data leaders who aim to demonstrate value early on, without the time, expense, and risk of failure, must reconsider their initial focus.
An approach to addressing the issue.
The traditional approach of starting with data and technology doesn’t work. To borrow the metaphor “data is the new oil,” companies did not begin investing billions in drilling wells, constructing pipelines, building tankers, and commissioning storage facilities before the internal combustion engine was invented. There is no chicken-and-egg dilemma here; the engine came first. The demand for oil surged when Henry Ford fulfilled his promise to create an affordable car, which prompted the construction of more pipelines.
Data leaders must intentionally shift their attention from data and technology to value creation. The aim and ambition must be to create a “value story”. Practically, this means identifying key stakeholders and working closely with them to connect stakeholders’ mission-critical priorities to D&A initiatives, detailing financial and nonfinancial stakeholder outcomes and impacts to get a complete value story. By prioritising value creation, data leaders can drive successful, data-driven initiatives more effectively.
Organisations that have taken the time to do this can avoid expensive technology implementation programs that fail to meet expectations. Instead, they can ensure that their data investments yield tangible benefits. Organisations like this have a more informed and business-aligned understanding of their data’s value. They know its utility for current and prospective customers, its stand-alone commercialisation potential, and its capacity to enhance existing business operations. This represents the true value of data.