For organisations seeking to transform through data, how adopting a more holistic approach could accelerate progress and increase success rates

Despite significant investment in data capabilities and technologies, enterprise-wide adoption in the use of data and advanced analytics is still sparce. A recent survey of Fortune 1000 organisations[1] indicated that whilst 92% of organisations have measurable business impact from data and analytics investments, only 24% described the organisation as data driven.?In the same survey, 80% cited organisational and cultural barriers as the key challenges yet business awareness of the importance of data as a strategic priority and growth opportunity has never been higher.

So what could be behind the disjoint between organisation’s ambition and belief in the transformational potential of data versus the reality of scaled adoption and use?

1.?Being overly ambitious and tied to a detailed roadmap to secure business sponsorship and support

A recent HBR article on the struggles organisations face in achieving successful digital transformations[2] highlighted one cause of failure as being over-optimistic in expectations of timings and scope of the outcomes.

Data and analytics teams need to create enthusiasm and belief in the transformational impact of data to secure the attention and prioritisation of resources. The creation of a detailed benefits case and roadmap of the use cases to be delivered is often used to provide the desired clarity and justificaiton. Yet at the outset, often too few of the challenges and barriers that will need to be navigated are sufficiently understood. As a result, as the project progresses, the realisation gradually dawns that the initiative will require significantly more time and money to solve than initially communicated, if it succeeds at all.

The challenge is therefore how to secures engagement, focus and momentum without becoming beholden to a detailed roadmap of use case delivery, benefit realisation etc that in reality maybe little more than a comfort blanket.

2.?Focusing on getting to a proof of value pilot rather than solving the problem end to end

In an eagerness to get to an early proof point of success and validate the analytical approach, the typical focus of the analytics squad is about getting to a pilot or MVP of the model solution.

While this is a critical step in both building momentum through proving value and developing knowledge on the analytical solution, it often leads to a lack of focus on what is required to put the solution in to production.

The result is that the organisational enthusiasm and confidence created through the pilot value is then quickly dissipated and credibility undermined as the time, resource and investment required to deliver a productionised solution is slowly discovered and has to be resolved.

3.?Believing the evidence of a successful pilot outcome will be enough in itself to secure scaled adoption.

The enterprise use and adoption of data means that stakeholders often need to adapt existing capabilities and processes, and even fundamentally challenging how the organisation makes decisions or delivers its processes.

A factor here is that business leaders at all levels have got to their current positions by making sound experience-based judgements and decisions. So, when the outcome of an analytics initiative brings facts and recommendations which challenge and threaten to change the perceived wisdom of business leaders, it’s unsurprising when there is resistance or delay in adoption.

This challenge to the underlying experience and capability of decision makers can mean that even the most enlightened leaders will need to take the time to query and understand this new wisdom before adopting and embracing. So finding the bandwidth to understand an analysis that is often not simple to articulate and confronts long held experiences and belief can be a step to far. At worst, stakeholders may seek to actively deprioritise or undermine the outcome as they feel unable to adapt or face into a potential threat to what has made them successful to date.

With these challenges, increasingly the business value potential and sophistication of technology platforms and data science teams is beyond the ability of the organisation to absorb, deploy and scale. So what can data teams and organisations do differently to accelerate the transformational impact form data?

1.?Frame business needs and use cases as hypothesis-based backlog of "problems to be solved" rather than defined solution outcomes with benefit commitments and milestone roadmap.

• Rather than describing and scoping the initiatives as solutions, framing them as problems to be solved helps position the work as a journey of iterations rather than a destination. By scoping the initaitive as a problem rather than a solution also increases empowerment and ownership by the team members[3].
• Spending time upfront with business leaders to help them understand the journey, and being transparent about the unknowns, helps to manage expectations and build their involvement and engagement in solving these challenges.
• Rather than building detailed roadmaps and business cases, adopt an approach that measures progress through Objectives and Key Results. Through evolving and refining OKRs along with an approach of using agile funding envelops[4], this helps balance the need for clarity and control while navigating a journey of discovery and continual improvement.

?2.?Scope and structure the initiative as a problem to be solved from an end-to-end perspective, not just getting to a proof of value pilot.

• Dedicating focus upfront on understanding and developing what will be required from an end-to-end solution - from a thorough understanding of the user need, interface, and any process or skills changes, to what will be required for technical implementation, productionisation and ongoing maintenance of the solution.
• Adopting a "thin-slice" development approach that both tests the usability and feasibility of the potential productionised outcome, but equally importantly, allows greater understanding of the problems that are still to be solved to deliver the production ready outcome.

3.?Proactively help decision makers adapt to how data and advanced analytics can fundamentally improve their capabilities and success.

• Secure early involvement with the decision makers to confirm the problem being solved and focus on jointly framing and defining the hypothesis to be tested. This is also a way to build rapport and trust with the stakeholders, as well has taking them on the journey regarding the uncertainties and challenges to be navigated. If there isn't a desire from the decision owner to be involved in forming and testing such hypothesis, then the wiser option may be to refocus on a different business area where there is greater openness to the adoption of data.
• Invest time and resources to build foundational awareness of how data analysis should create evidence and facts and so inform decisions. This needs to go beyond the typical focus of a data literacy program often centred around building self-service familiarity in data visualisation tools and explainations of Machine Learning/ AI. For example, helping business unsers to improve skills in robust scoping of analysis to clarify “what would be done differently”, framing decisions as probabilities of outcomes, being able to test for data analysis pitfalls such as skews, biases, and the limitations of the data.

The transformation of organisations through data is by definition a complex and involved change process, and success is certainly far from guaranteed. Yet unless analytics teams lead their organisations in taking a more holistic, considered and realistic approach then the potential of advanced analytics and AI will continue to be far beyond most organisations ability to absorb and embrace.

[1] https://www.wavestone.us/insights/data-and-analytics-leadership-annual-executive-survey-2023/

[2] https://hbr.org/2022/09/3-stages-of-a-successful-digital-transformation

[3] https://www.imd.org/ibyimd/strategy/six-ways-to-engage-with-your-teams-to-frame-and-solve-strategic-problems/

[4] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/agile-funding-an-investment-management-approach-to-funding-outcomes