I recently completed a data strategy course on LinkedIn, eager to expand my toolkit and craft better strategies for clients.

While the course was engaging, a nagging problem emerged: the author’s conflation of “data strategy” with “data analytics strategy.” While analytics are crucial, they’re just a piece of the data puzzle.

Let’s debunk the myths and explore the true scope of data strategy.

Myth #1: Data Strategy = Data Analytics Strategy

This is the most prevalent misconception. Analytics, while valuable, are a means to an end, not the end itself. A data strategy is your organization’s guiding principle for data usage.

It defines how you collect, manage, govern, and leverage data to achieve your business goals. This includes objectives like:

• Improving operational efficiency: “What data do we need to track inventory levels and optimize production?”.
• Making data-driven decisions: “What customer data can help us identify churn risk and implement retention strategies?”.
• Enabling data-driven innovation: “How can we leverage historical data to develop new products or services?”.

It’s about what data you need and how it serves your business goals, not just building dashboards.

Myth #2: Data Strategy is an IT Issue

Historically, data has been viewed as an “IT thing,” locked away in server rooms and managed by tech teams. This approach siloed data and hindered its true potential. But data is a business asset, produced, used, and maintained by business units.

IT plays a crucial supporting role, but the business, with input from various stakeholders, should drive the data strategy. Recognizing this shift is crucial.

Myth #3: Data Strategy is a Subset of IT Strategy

No way! While IT infrastructure supports data management, the data strategy itself should be distinct and aligned with the overall business strategy. It should guide data governance, access, and usage across the organization, not be confined to IT operations.

Data Strategy vs. Data Analytics Strategy

Data analytics strategy sits within the larger data strategy umbrella. It focuses on identifying the specific data, tools, and techniques needed to analyse data and generate actionable insights. This may involve aspects like:

• Choosing the right analytics tools and platforms.
• Defining key performance indicators (KPIs).
• Developing data analysis pipelines.

While essential, data analytics is just one step in the data journey.

The True Scope of Data Strategy

A robust data strategy focuses on three key areas:

1. Extending and Supporting Business Strategy: Align data with your business objectives, whether it’s identifying new markets, optimizing operations, or enhancing customer experience.
2. Enabling Business Operations: Ensure data supports day-to-day tasks, like credit checks, inventory management, or customer service.
3. Facilitating Change: Guide data collection, access, and usage as your business evolves and adapts to new opportunities and challenges.

Remember:

• “What data do I need for a credit check?” is just as important as “What data helps me identify new markets?”
• A data strategy might incorporate a data analytics strategy, but it’s broader, encompassing the entire data value chain.
• Business, not IT, owns the data strategy, though collaboration is essential.

This article is just the beginning of the conversation. What are your thoughts on the importance of a holistic data strategy and its relationship with analytics and IT? Let’s keep the discussion going!