Data strategy is the blueprint for managing data as a business asset. It helps organisations extract and protect data value, and operationalise and govern its use.
There are 3 primary objectives of data strategy:
Extracting value from data
Protecting the value of your data
Operationalising and governing data use
Here’s how it works, with real-world examples:
Extracting value from data
Data is more than just numbers and records; it is a critical asset that fuels four core business functions.
Informed decision-making: Data provides leaders with the insights needed to guide strategic actions. For example:
A publisher leverages deep insights from its core revenue streams—such as subscriptions, advertising, and licensing—to balance resources efficiently and maximise ROI.
A retail chain analyses sales and weather data to predict product demand, ensuring shelves are stocked with the right items during seasonal peaks.
An airline utilises data from flight delays, customer feedback, and competitor analysis to optimise schedules and pricing.
Operational efficiencies: Streamlined processes and resource optimisation, most notably through the use of AI, rely on accurate and quality data. Some practical applications include:
A logistics company utilises AI to analyse delivery routes, reducing fuel costs and improving delivery times.
A healthcare provider uses patient data to automate appointment scheduling, reducing administrative workload and minimising no-shows.
Insights and innovation: New products, services, and business models emerge from data-driven insights. Consider these examples:
A fitness app analyses user activity data to create personalised workout plans, enhancing customer satisfaction and retention.
An automotive company gathers IoT data from vehicles to identify trends and improve future designs.
Customer-centricity: Delivering personalised, customer-first experiences starts with understanding data. Businesses that harness data effectively can significantly improve customer engagement:
A streaming service analyses viewing patterns to recommend tailored content, increasing user retention.
An e-commerce platform uses purchase history to offer targeted promotions, driving repeat sales.
However, extracting value from data is anything but easy. It requires robust data capabilities and foundations - think data analysts to interpret data, data cleaning processes to ensure quality and accuracy, data storage and processing capabilities to handle speed and scale.
Without these, the potential of your data remains untapped - too much of these, and you're wasting your valuable investment!
Protecting the value of data
Every piece of data within a business represents a value exchange. Organisations must respect these exchanges and implement the right safeguards to protect data assets.
Trust exchange: Customers, employees, and suppliers share their data with the expectation that it will be protected and used responsibly. For instance:
Customers provide their addresses and payment details when shopping online, trusting the business to secure them.
Employees share health records for wellness programs, expecting confidentiality.
Resource exchange: Businesses invest significant time, money, and resources into collecting, cleaning, and transforming data into actionable insights. As a result, they expect a return on this investment. Examples include:
A financial institution investing millions in fraud detection systems that analyse transaction data in real-time.
A tech company allocating resources to clean and standardise data for AI model training.
Every use or transfer of data must respect these value exchanges, requiring organisations to acknowledge the risk associated with that data use and implement the right controls to safeguard data. Failing to protect data can lead to reputational damage, legal penalties, and loss of customer trust. Protecting its value not only fulfills ethical and legal obligations but also ensures long-term business resilience.
Operationalising and governing data use
Striking the right balance between extracting and protecting value is essential, and this is where operationalising data comes into play.
In today’s data-driven world, manual processes alone are insufficient to handle the scale and complexity of modern data ecosystems. To ensure data is working efficiently:
Policies and governance: Build strong foundations with clear permitted and prohibited data use policies and governance. No longer is role based access controls enough - we need to focus on purpose and use.
Automation and technology: Invest in the right systems and technologies to automate data processes, while retaining flexibility to innovate, improve and adapt. Automation and technology for data operations is crucial as it ensures scalability.
Adaptive workflows: Establish workflows and automation that align data usage with business objectives, ensuring your governance is dynamic and adaptive.
When data is operationalised effectively, it becomes a powerful asset driving both value and protection seamlessly.
Striking the right balance between extracting and protecting data value is essential. This is where operationalising data use becomes critical.
Data strategy is about building a framework that extracts value, protects assets, and operationalises processes. Whether launching a new product, improving customer experiences, or safeguarding sensitive information, a well-crafted data strategy ensures your data is working for you, not the other way around.
By balancing innovation with responsible governance, businesses can harness the full potential of their data while maintaining trust and compliance in an increasingly digital world.
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