By Nik Acheson, Field Chief Data Officer at Dremio
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.
Read the full article via Digitalisation World.
