Every experienced data practitioner will be familiar with the purpose of a data strategy… but why do so many data strategies fail to deliver? Despite best intent, the data strategy often fails to drive the required investment to support execution and the initial flurry of excitement gives way to apathy and loss of momentum. Why do so many data strategies lose credibility amongst key business sponsors and stakeholders and how do you avoid your data strategy joining the litany of shelfware?
There are plenty of referenceable online materials including guides and templates that will assist you in generating appropriate content and structure for your data strategy. However, even with compelling domain content and structure, including a commendable vision supported by a credible plan for execution, the data strategy may still fail to land.
The biggest challenge with any data strategy is securing and maintaining executive sponsorship and business stakeholder buy-in. By its very nature, data is omnipresent – it flows across organisational boundaries and is used for many different business purposes. Therefore, consistent interpretation and execution of data strategy across diverse business sponsors and stakeholders is vital to leverage value. If delivered effectively, data strategy can be a powerful enabler to the business in partnership with technology.
In this article, we’ll take a look at two considerations that in our experience are often overlooked when preparing a data strategy - business relevance and applicability. Ignoring these can lead to failure during execution.
We summarize 10 key steps to ensure your data strategy aligns with business needs and secures necessary investment:
01. Define the Business Scope
Does the strategy cover the entire enterprise, group, department or function? This step is key as it will determine both your principal executive sponsors and business stakeholders. It is important to secure a mandate for the data strategy so this step will identify the executive sponsors across the respective business domains as well as identify local data strategies and initiatives that will require complementary alignment.
Even where business domains are identified as out of scope, it is important to understand dependencies on common or shared data assets as this may influence the new data strategy.
02. Create an Executive Sponsor Community
Having defined the business scope, it is important to create an Executive Sponsor community (e.g. Executive Data Council). The Executive Sponsor is the senior executive who has strategic responsibility for the business domain. At an enterprise level, this will typically include CxO roles.
This community collectively accounts for the business scope of the data strategy and plays an influential role both in its development and execution by maintaining continuity, communicating change and building coalition that is vital to ensuring the success of the data strategy and avoiding conflict and divergence.
03. Align with the Business Strategy
What is the vision for the business? How is it competitively positioned? What are the predominant threats? These are all questions that should be addressed in the business strategy and are of primary concern for the Executive Sponsor community. The data strategy should therefore clearly articulate how the data strategy enables the business strategy and how it creates value.
For example, the data strategy may articulate how current data management capabilities constrain adoption of artificial intelligence as well as the steps required to accelerate adoption and expedite the business strategy to promote hyper-personalisation of customer services.
04. Establish a Common Language for Data
It is essential to share a mutual interpretation of scope, requirements and outcomes. This is particularly relevant when implementing enterprise strategic data initiatives that involve multiple business units, departments and/or functions.
Quaylogic recommend the use of a Data Management Capability Model that defines a common language for data management capabilities, functions and disciplines as well as the structure that defines how these functions and disciplines relate to one another. The Data Management Capability Model employs terminology that can be understood equally by the business and data practitioners.
05. Create an Outcome-based Plan for Delivery
The delivery plan, typically 3-5 year, is a common component of any strategy and should clearly articulate the outcomes that demonstrate business relevance and applicability. Ultimately, this ensures that the data strategy is presented in terms that are understood by the business and align with the priorities set out in the business strategy.
It is also important to identify the interdependencies between delivery phases; for example, data governance workstreams may be prioritised to address mandatory ‘defensive’ regulatory demands but will also deliver foundational capabilities that are critical to support ‘offensive’ business strategies with dependencies on data marketplaces, advanced analytics and artificial intelligence.
06. Enforce Executive Sponsor Collective Accountability
As well as detailing the delivery accountabilities of the data practitioners, the Data Strategy should also be clear about the collective accountabilities of the Executive Sponsors. Their collective endorsement of the data strategy and responsibilities in respect of delivery prioritisation as well as adoption and embedding of capabilities across the business units, departments and/or functions are critical.
These accountabilities should be enforced through the afore-mentioned executive governance body (e.g. Executive Data Council).
07. Use the Data Strategy to Support the Business Cases for Funding
Too often, the business cases supporting a phased delivery tend to focus only on the immediate context and miss the broader strategic picture. Funding is often incremental, drawn down in tranches across the lifecycle of the data strategy delivery plan. It is important to maintain coherence of the business relevance and applicability across the funding requests. Each business case needs to articulate the context and applicability with the broader data strategy and, most importantly, requires the endorsement and support of the Executive Sponsor community.
08. Promote Data Strategy Awareness / Develop the Data Culture
Once the data strategy has been endorsed it must be communicated to promote awareness. Given the diverse demands of the business units, departments, and functions, all business stakeholders must acknowledge the business relevance and applicability of the data strategy.
This collective awareness is a key step in developing a Data Culture based on a common understanding of the vision, goals and delivery plan. This is particularly important for the diverse business stakeholders that have very different roles in respect of shared data assets (e.g. Data Owners and Data Users.)
09. Deliver Incremental Business Value
Once you are in delivery execution, be sure to promote the value derived from the planned outcomes. For example, whilst it is challenging to demonstrate business value associated with uplifted data governance capabilities in isolation, when applied to critical business processes, services and/or customer journeys, it is possible to identify tangible efficiency gains, service level improvements and increased customer retention etc.
Be transparent with the business on defining outcomes and work with the Executive Sponsor community to communicate and celebrate the wins.
10. Provide Transparent Assurance & Performance Measurement
In addition to identifying the business outcomes, the data strategy should articulate the measures of success in terms that are understood by the business. The data assets that support critical business processes, services and/or customer journeys are likely to be subject to a data risk framework and associated data controls. Assurance provides the appropriate processes for attestation of capabilities to demonstrate the effectiveness of these controls.
Similarly, measures of success should be articulated as business metrics subject to periodic reporting and dashboarding. This assurance and performance measurement secures credibility and maintains executive sponsorship and business stakeholder buy-in.
Unlocking the Full Potential of Your Data Strategy with Quaylogic
The effectiveness of any data strategy is dependent on diverse criteria including the integrity of the vision, executional credibility of the plan and skills / capabilities of the deployed resources as well as delivery discipline to manage dependencies, risks, issues etc.
In this article we have addressed two considerations that are often overlooked – business relevance and applicability – that are critical for ensuring the successful delivery of your data strategy.
Quaylogic has considerable experience in documenting, preparing & executing data strategies, as well as assisting organisations to ensure that their data strategies remain both relevant and applicable to the business. Quaylogic experience in engaging and influencing senior stakeholders across diverse commercial and public sector organisations has secured credibility to ensure sustainable execution of outcomes-based data strategies.
Are you ready to unlock the full potential of your data strategy? 🚀
Contact Quaylogic today to get expert support and guidance on documenting, preparing, and executing your data strategy or speak directly with our data strategy leader, Nigel Matthews Matthews, for personalized assistance.
Our team is here to help you navigate the evolving landscape and achieve transformative outcomes for your business. We're dedicated to your success and ready to support you every step of the way. 📊🏆
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