Data Strategy practices
It turns out that "data strategy" is a bit of a mystery for many people. I've been chatting with all sorts of folks lately, and I've realised that a lot of them aren't quite sure what it actually means or how a data strategist can help. That's why I put together this blog series: to demystify data strategy and show you how to make data a real powerhouse for your business.
These days, it seems like everyone's talking about data. But let's be honest, turning all that raw information you've got (e.g. financial transactions, social media mentions, performance reviews, etc.) into something useful can feel like a real puzzle. The secret? A solid data strategy that actually unpacks all data strategy practices and their elements and aligns them with your business objectives and what you're trying to achieve.
Think of this blog series as your guide to cracking the data code. It serves as your roadmap to deconstructing data strategy elements to discover data's potential as a valuable business asset. This series will provide step-by-step guidance while incorporating real-world examples and actionable tips along the way.
But first, let's address the elephant in the room. What is data strategy?
According to Gartner, "A data strategy is a highly dynamic process employed to support the acquisition, organisation, analysis, and delivery of data in support of business objectives." (Gartner, 2024). In other words, a data strategy is more than just a plan or a framework. It’s a blueprint for a roadmap that guides a business toward efficient and effective data usage, which supports its vision and helps achieve key objectives. Effective data strategies focus on identifying data challenges and their solutions. The strategy transforms data from being a burden into a valuable asset that helps your organisation achieve SMART goals. A successful data strategy highlights the approach to managing your data and its monetisation while leveraging it through data management best practices.
What Does a Data Strategist Do?
The scope of a data strategist role is broad and often confused with data analysts, scientists, or engineers. While these roles share overlapping technical skills and contribute to an organisation’s data capabilities, the Data Strategist focuses on creatively maximising data value through planning, business and communication expertise. Their responsibilities can vary by domain, e.g., a marketing data strategist differs from one in tech or AI. Additionally, a data strategist can work across domains as a generalist, depending on their knowledge of tech, the industry and tools. For example, I am a general data strategist with broad experience across various tools and technologies. While all aim to leverage data for business goals, their objectives and skills vary by role, allowing for adaptation to business needs.
In a nutshell, a data strategist is the data vision's architect. They work with business leaders, technical experts, etc., to make sure everyone is aligned and understands the business needs and what they're trying to accomplish. They then sketch out the big picture and piece it together by breaking it down into actionable steps, highlighting resources needed and addressing limitations for each step. They act as vision translators, facilitating understanding between the technical and business sides, while constantly managing the implementation, ensuring your data systems are built to meet real use cases and business needs to deliver measurable results.
Ever wondered what a data strategy actually looks like in action?
Now that you know what a data strategy is and what a data strategist does, let’s dive deeper. To help you visualise how data strategy translates into action, I've put together a summarised handy illustration of framework that breaks it down step-by-step, highlighting the objectives, the key focus areas and their actionable elements. It's like a sneak peek into how I usually get the ball rolling when developing a data strategy. Ready to see it in action? Let's dive in!
With everything above in mind, let's explore data strategy practices which you can imagine as being the "chapters" of the data strategy playbook. A successful data strategy relies on mastering what I believe are the five major data strategy practices. Each addresses a critical aspect of leveraging data as a strategic asset. These practices ensure your organisation can align data with business goals, evaluate data capability, utilise optimal data systems, unlock opportunities and innovation and govern data effectively. Together, they form a comprehensive approach to transforming data into measurable business outcomes that can support your business vision.
Now that you understand what data strategy is, what a data strategist does, what a data strategy might look like, and the main five practices of data strategy, it's time to explore these five key practices that, in my opinion, form the backbone of a successful data strategy.
I've created five blogs, and in each blog in this series, I break down one of these practices, offering insights into its core elements, actionable frameworks, summarised use cases, actions, and results. In addition, I provide real-world use cases and hands-on knowledge to apply in your own organisation. Click the links below to explore each practice in detail.
Ready to take your data strategy to the next level?
Let’s get started!
1
Blog
Hit the bullseye by aligning your data strategy with business objectives. Set SMART goals and use relevant KPIs to measure and achieve results.
2
Blog
Evaluate your organisation’s data maturity to understand your position and identify steps to improve processes, systems, and data-driven decision-making.
3
Blog
Explore strategies for building scalable, efficient data systems, whether transitioning to the cloud or improving your current setup.
4
Blog
Leverage analytics, AI, and machine learning to transform data into impactful solutions that solve key business internal and external challenges.
5
Blog
Learn the importance of data governance for ensuring security, compliance, and quality while fostering a data-driven culture.