In the ever-evolving landscape of data, your company's success hinges on transforming unstructured information into insightful data you can use. From enterprise resource planning (ERP) and customer relationship management (CRM) systems to various IT platforms, data is a formidable asset that, when leveraged correctly, can truly drive tangible business success.
In this article, I'll explore the importance of a data strategy, its role in organisational success, and a step-by-step guide for business leaders to develop an effective data strategy roadmap.
What is a data strategy?
That, of course, may be one of the first questions that spring to mind. Well, a data strategy is a goal-oriented procedural plan that evaluates how your company collects, stores, analyses, and utilises data.
It aims to overcome common obstacles such as siloed data and inefficient data management practices, turning data into a valuable asset aligned with business goals. This approach integrates data management with overall business strategy, governance, compliance, and security.
Why is a data strategy important?
The next question that you may ask is why should I even do this? Simply put, a well-thought-out strategy enables better decision-making, minimises risk, and can identify new business opportunities. Additionally, it simplifies data protection, strengthens security, and optimises collaboration between teams and departments.
Even if your company has dealt with big data for years, the complexity and sheer volume of data require a clear strategy. This provides clarity, optimal resource utilisation, and a focus on the most efficient projects and – bigger picture - really does go a long way in enhancing a company's agility and competitive edge.
How do I develop a data strategy roadmap?
Here's my suggested six-step process to create a robust data strategy roadmap…
Step 1: Describe your vision
When developing your data strategy, delve into the specifics of your company's market position, business model, and the future envisioned by both employees and management. This step lays the foundation for aligning your data strategy with the overall goals of the organisation, ensuring that the following steps are tailored to your unique situation.
Step 2: Identify data sources
The identification of data sources is a critical phase where IT administrators, data engineers, and strategy development teams come together. By documenting all internal and external data sources, your organisation gains a comprehensive understanding of the data landscape. This step is pivotal for preventing the creation of data silos and promoting a cohesive approach to data accessibility across departments.
Step 3: Define integration points
Understanding the distinction between structured and unstructured data is paramount. This step involves linking data and sources to create cohesive information workflows. Data engineers and scientists play a vital role in answering questions about integrating data, choosing suitable tools, and utilising the information to drive profitability. Successful integration ensures that data becomes a valuable asset for decision-makers across an organisation.
Step 4: Tools for data-supported decision-making
The transformation of information into business intelligence is explored in this step. Subject matter experts and data scientists collaborate to identify how data can enhance key activities, optimise resource utilisation, and cut costs. Addressing questions about marketing, sales, and delivery channels allows organisations to leverage data for strategic decision-making.
Step 5: Planning the implementation
With a clear understanding of available data, information creation, and business knowledge extraction, organisations can now develop a concrete plan for implementing the data strategy. This step involves decisions on integration solutions, choosing between purchasing or developing in-house software, selecting analysis tools, and identifying necessary employee skills. The implementation plan ensures that the strategy becomes a reality, aligning with business goals and objectives.
Step 6: Data strategy
The final step involves formulating ideas, concepts, and expected results into a transparent and actionable data strategy. Assigning key responsibilities and defining analysis processes are critical components here. Additionally, clarifying the format of result delivery, understanding data analysis workflows, and establishing performance monitoring mechanisms ensure that the strategy is not only comprehensive but also measurable.
What role does document management play?
A document management system (DMS) emerges as a crucial component in the data strategy landscape. By managing the entire lifecycle of documents and data within an organisation, a DMS streamlines processes and replaces manual tasks with automated workflows. The system's capabilities extend to capturing, reading, and indexing information, facilitating annotation, editing, or improvement of data, all the while providing robust workflow and automation tools. Finally, the inclusion of comprehensive audit logs and analytics capabilities ensures accountability and transparency when it comes to data management.
The development of a data strategy roadmap for business leaders and IT decision-makers involves a careful journey through various interconnected steps. By prioritising each phase and recognising the synergies between them, you can cultivate a robust data strategy that ensures that the data from your business becomes a strategic asset for informed decision-making and sustained competitiveness in the digital age.