In today’s fast-paced and competitive landscape, the ability to become a data-driven business is no longer a luxury - it’s a necessity. While this isn’t a novel idea, it bears repeating: the key to leveraging data effectively lies in crafting a simple, compelling narrative that guides an organisation’s approach to technology investment.
A well-defined narrative helps businesses translate raw data into actionable insights, enabling them to seize new growth opportunities, make smarter decisions, and streamline operations.
- Develop a Data Strategy - Providing a clear roadmap to align data initiatives with business goals, enabling better decision-making, operational efficiency, and innovation is the key output. Without a strategy, data remains an underutilised resource, leading to missed opportunities and fragmented efforts. A robust data strategy ensures data quality, compliance, and accessibility, and should not be a 'Reference' document, but should be the foundation of technology investment, architecture decisions and planning.
- Foster a Data-Driven Culture - A data-driven culture requires leadership commitment to champion data use at all levels, making data accessible and centralising information as a 'single version of truth' across departments. Organisations should invest in data literacy through training, align data initiatives with business goals, and facilitate cross-functional collaboration. Agree data owners 'Early' and implement standards for responsibility and decision-making. Provide a user-friendly toolset, ensure you set data quality controls, and celebrate data-driven successes, helping you build trust and engagement through success stories. Continuously refining your process processes based on feedback from key Business SME's.
- Invest in Data Infrastructure - Develop and assess business requirements to determine appropriate storage and processing solutions, designing scalable architectures, such as cloud-based systems, for flexibility and cost-effectiveness. Design seamless data integration across platforms, Prioritising security, and regulatory compliance. In my experience, Opex migration to cloud services is rarely 'Cheaper' than on-premise services. Have that conversation early and compile a 3-year projection. Planning for future growth by selecting scalable solutions, and staying informed about technological advancements ensures the infrastructure can adapt to evolving data needs. Strategic investment in data infrastructure should be done by technical process with application/data owners 'Informed', to ensure you control the risk.
- Ensure Data Quality and Governance - Assure data quality so we can prove our critical 'Lifeblood' Information is accurate, consistent, and reliable. Quality-assured data serves as the foundation for effective decision-making and compliance with regulatory standards. Achieving this requires clear policies, defined data ownership roles, and robust data management practices to preserve data integrity throughout its lifecycle. Integrating data quality measures into governance frameworks and architectural standards is essential. Regularly reviewing management quality checks against decisions ensures that risks are mitigated and controls are properly enforced, strengthening adherence to established standards.
- Leverage Advanced Analytics and AI - Perhaps the most talked-about part of creating a data-driven orgisation. The key thing (as with any IT Programme), Is to ensure Business Case alignment, that translates into data/systems architecture, well-communicated roadmaps, and a clear risk profile. Done right the Data Analytics and AI capability has the potential to allow automation in data processing, identifying patterns, and predicting trends, leading to improved customer experiences and optimised processes. AI tools can process data faster and more accurately than conventional methods. How this translates into value is the impact to strategic and operational output. Ensure the Output meets the needs of the Business Objectives throughout the process, use 'Leading Measures' to assure Alignment & Quality, and celebrate success as widely as possible.
- Measure and Iterate - Starting with the Obvious, setting clear objectives aligned with business goals is vital. The best way to build the narrative is to produce a summary, 1-page document from which to 'Tell the story'. Develop specific Key Performance Indicators (KPIs) to evaluate success, such as model accuracy, processing speed, user engagement, and return on investment for FTE time recovered. Implement an iterative development process that involves deploying AI models, collecting performance data, analysing outcomes, and refining models based on feedback and new data. Engage stakeholders throughout to ensure alignment and gather diverse insights. This structured approach enables continuous enhancement of AI and data programs, driving greater impact and efficiency
In my experience the best way to 'Fail' to reap the benefits of an investment in Tech, it to 'Start before you are Ready'. The following is a summary of what I would suggest is vital before you initiate activity:
- Leadership Buy-In: Support from executives is vital to secure funding and drive cultural change. There should be no 'Surprises' concerning the cost of projects, especially considering 'Run' Projections. Take the business along on the journey and help them understand 'What this means for me'
- Data Accessibility: Without centralised, well-integrated data systems, insights will remain fragmented, with diluted benefits. Ensure you have the full view of WHAT is expected from the investment by considering: Data Availability, Permissions and Security, Ease of Use, Centralised Access Points, Clear Integration Requirements, Data Quality Standards
- Skilled Workforce (This is 2 fold):
Employee Data Literacy Employees need the skills to use analytics tools and interpret data. Modular, department-specific training ensures staff receive the right level of support to make data-driven decisions confidently. Have a plan, Communicate and stick to it!
Team and Supply Chain Skills Internal teams or supply chain partners must have the skills to support data programs. If gaps exist, plan to onboard the right partners at key stages. In public sector projects, allocate ample time for procurement and awards to avoid delays. As part of the pre-program review ensure your environment and data are Documented, Accessible, and in no way vulnerable to a 'Single points of failure'.
- Technological Investment: Outdated or insufficient infrastructure will hinder progress. As part of the pre-programme review ensure your environment and Data is Fully Documented. Have a roadmap with the milestones planned for Infrastructure and Data that act as Critical Path Dependencies. Double-check and get some advice if you are unsure. Most of all Document and track programme risks regularly.
