Avoiding 'False Starts': A Guide to Successful Data and Digital Transformation

In the world of sports, few moments capture the core of false starts better than Usain Bolt’s shocking disqualification at the 2011 World Championships. Bolt’s premature launch in the 100 meters final led to his immediate removal from the race - reminding us that false starts, in any context, can be costly and disruptive.  

In an organisational setting, false starts often occur when businesses have to restart their strategy or foundation due to missing any number of key details. Examples of these details may be as simple as discounting the time for security reviews and compliance requirements that impact delivery commitments. It is important to note that quick wins are required, but those wins should be just as foundational as the large and complex ones yet to be prioritised. 

“Engineering is easy… It’s people that are hard.” This profound truth encapsulates these challenges perfectly. Organisational limitations and change management need to be at the forefront of a transformation journey. Underestimating this will result in missed deliverables, cost overruns, loss of trust, failed adoption, and inevitably false starts. Businesses that want to not just avoid this but also stay innovative and competitive, must be proactive and iterative in both delivery and enablement. Just like athletes perfecting their timing, organisations need to ensure they are aligning their initiatives and overall foundational platforms and patterns with their business’ Objectives and Key Results (OKRs): Practicing and preparing every day for whatever the unique challenges that will arise that the pattern still solves. 

With aligned patterns, changes in use cases will still utilise the data foundation transformation being delivered. Business priority changes should not impact the foundation. Thus, the team can balance quick wins on the foundation to prove out value, build trust, and momentum, all while in parallel working on more complex and “big win” use cases that take more time and evolve. 

Aligning value and cost for a byte of success 

A crucial aspect when avoiding false starts is ensuring that every transformation initiative is closely aligned with the business’ OKRs. This involves linking technology platform investments into shared OKRs to ensure all investments are tracked towards achieving the top business goals.  

An additional way to assure successful delivery is through self-funding. As foundations are rebuilt, data leaders must consider cost savings and other efficiencies available in their ecosystem. For example, bringing in a new platform will either decrease the burden on another (lowering license costs) or making at least two other platforms obsolete. Specifically, bringing in a better query engine may reduce costs enough to renegotiate other vendor contracts enabling the team to “self-fund” another new platform. 

This new platform may later also help deprecate multiple others as it scales and picks up use cases. All this becomes possible without new funding: self-funding as teams stay within their Annual Operating Plan (AOP) budgets. This strategic shift and focus, regardless of the size of efficiencies, enables technology teams to move faster towards value creation and delivery versus being a classic cost center.  

Building the foundation for transformation 

For businesses that want a successful data and digital transformation, a solid foundation is crucial. This includes setting up modern entitlement services (who has access to what systems and what they are authorized to access), integrating enterprise data catalogs (tracking what data is in the enterprise, who owns the data, and the context around it), and treating data as an asset (managing data like an actual product). Shortcuts are certainly possible to get quicker wins, but a longer-term plan should be in place to maintain flexibility, such as open architectures to minimise the debt and potential switching costs as technology and businesses continue to evolve. Maintaining this balance means businesses may harness technology’s transformative power to drive faster growth and innovation, without sacrificing the future because of material debt taken on to do it.

Even for a foundational platform like a metadata catalog, as part of the overall data catalog capability, telling stories for investments should shift. Narratives may include actual analysts’ headshots from the company seeking to “shop for data”, note how unified analytics and data democratisation is enabled faster with open format migration, and avoiding failed compliance audits and fines resulting from an easier path for integrations with the proposed changes. These stories are paramount to pitching “how” the platform should be built and “how” it’s inefficient today. The impact to the business and end customers is always the “why”. Any supporting data should be in the appendix and may be spoken to as needed. A good story, however, will make the appendix mostly irrelevant. 

Crafting stories from digits 

Once upon a time, data was viewed as a mere collection of numbers and figures, often daunting and incomprehensible, let alone debated about how trustworthy they are. However, as businesses have embarked on the journey of digital transformation, data storytelling has emerged as a transformative practice. Storytelling breathes life into data, turning it into “insights” versus “facts”, with compelling narratives that resonate with audiences and feel more actionable. This goes beyond improving visualisations, but rather crafting a narrative story around the impact that the data driven recommendations will lead to. The story should include more than the forward-looking opportunities, but also include missed opportunities: The impact that could have already happened if the recommended changes were in place for a recent or past business event.

Storytelling humanises data by weaving together insights and trends, peer benchmarks, and use cases that impact the people being engaged that make it personal and persuasive. Visual elements play a massive role in bringing this all together – offering intuitive representations to clarify and simplify messaging that was once more difficult to understand.  

It’s no surprise that data-driven stories can boost audience engagement by a staggering 300%. Storytelling empowers the presenter to adjust on-the-fly without losing sight of the goal. This enables better understanding and alignment by maintaining engagement. As a storyteller improves, their potential to achieve trust, alignment, and funding remains unparalleled. 

To avoid false starts, data storytelling must go beyond presenting facts; it must elevate to the emotional resonance of business. It’s crucial for businesses since decisions are often not made based solely on “speeds, feeds, and budget”, but rather for strategic direction. Even for a technology vendor, the role of data storytelling with customers helps stay ahead of competition by ensuring the technology is a strategic asset, offering more than just the initial improved capabilities and experiences. Again, every capability from IT to experiences for an end user should be thought of as a foundational step to the next. Present the athlete, or in this case the company or customer, in victory and describe the training program if asked.

Remember, people love top athletes, but rarely care about their workout schedules. Top athletes love the process more than the race, but still need to be there for the fans. Keep and grow the fans. Change is emotional and hard, and delivering data that elicits the right conscious and subconscious emotions is the art of the storyteller that drives successful and sustained change. Done right, you will avoid false starts. Miss a detail, and even the greatest can be removed from the race.