A comprehensive data strategy is essential for guiding an organization’s transition to a data-driven culture.

With the rise of analytics technologies and platforms, whether new or updated—such as data clouds, customer data platforms (CDPs), data clean rooms, etc.—it has become more important than ever to remember that a strong data strategy is foundational to all of your undertakings if you intend to become a data-focused business. The size of your company shouldn’t be an obstacle to having an effective data strategy. What’s important is not to rely on intuition alone to guide your business decisions.

In this article, I’ll be addressing three classic questions about data strategy: what, why, and how.

What is a data strategy?

It’s crucial to distinguish a data strategy from mere operational planning. 

A data strategy is not: 

• a list of analytics tools you are planning to acquire next year;
• the number of employees you hope to hire as part of your analytics team next year; or
• an arbitrary list of data sources that will be presiding over your dashboard.

In simple terms, a data strategy is:

• A plan that will enable you to acquire, manage, govern, and exploit the data you need to make better decisions and improve your company’s performance. 

Its main goal is to set free the intrinsic value of an organization’s data and prepare those data for advanced applications, including for sophisticated artificial intelligence (AI) and autonomous systems. This “value” can take on a variety of different forms depending on the business context, such as: 

• improving customer experience;
• optimizing marketing investments and return on investment (ROI);
• informing your strategic planning so that you can orient your strategy (competition, market, and benchmarking);
• improving your products’ adoption rates;
• facilitating powerful predictions and information based on AI; or
• supporting the deployment and operation of autonomous agentic systems for automating complex tasks.

Why have a data strategy?

Given the accelerated digital and business transformations resulting from recent world events, having a data strategy is of critical importance.

The crisis surrounding the COVID-19 pandemic in particular forced many companies to speed up their digital transformations and adapt their business models in order to ensure the agility and flexibility of their operations. The main goals of these initiatives are customer satisfaction and cost reduction. 

Data is a major pillar of any transformation, even today. After all, you can’t improve what you can’t measure. That’s why transforming data into valuable business assets is a well-documented priority for many CFOs and CDOs. 

For marketing directors, it will become hard to justify investments in digital marketing in a world in which the loss of digital identifiers (cookies or mobile advertising identifiers) renders media attribution more complex. It’s essential to establish a data strategy that takes this new reality into consideration if you intend to continue measuring business performance.

To exploit artificial intelligence (AI)

The more organizations exploit artificial intelligence (AI) for innovation and efficiency, the more a robust data strategy becomes essential. A wide range of applications are highly dependent on access to data:

• Machine learning models 
• Generative AI applications
• Autonomous AI systems
• AI agents

All of these require data that are accessible, high-quality, and well-governed, which is exactly what a data strategy aims to ensure! A data strategy creates the foundation of reliable data that is needed to make AI projects successful and safe.

How do you implement a data strategy?

Designing a data strategy generally involves six key phases:

1. Identify the business function

Where would you like to effect a transformation (marketing, product, customer service, operations, finance, sales, etc.)? Identify a business issue that could be resolved with a data-driven solution, taking into consideration potential applications of AI, including areas where agentic AI might create value.

2. Establish clear business objectives 

Clearly identify your key performance indicators (KPIs) in order to determine whether the data strategy has tangible repercussions. Your strategy needs to facilitate an improvement in something—something that is quantifiable. For example, you could have as an objective:

• improving customer satisfaction; 
• increasing customer loyalty or value by X%; or 
• reducing operational costs by X%.

3. Take stock of the situation 

Evaluate your level of data maturity. List existing data, the availability of data sources, their quality, their governance, and your current capacities. This crucial stage will allow you to get an idea of the scope of the task at hand and design a strategy based on modules and realistic deliverables.

4. Orient your strategy towards the future 

Determine how you can obtain the data you are missing while maintaining solid groundwork in terms of data governance:

• validity
• precision
• ownership
• accessibility
• privacy
• security
• regulations
• etc. 

Consider what technology is needed to collect, sort, store, and transform your data as well as the experts you’ll need to transform these data into insights and tangible actions—that is the goal, after all! Take the time to evaluate the risks and identify your weaknesses. 

Integrating artificial intelligence requires structuring and preparing data specifically for training and deploying AI or machine learning (ML) models, and to support the needs of agentic systems. Pay attention to the risks related to AI, such as bias, as well as governance and the potential impacts of the actions of autonomous agents.

5. Perform a proof of concept

Conduct a pilot study that potentially involves an AI or agentic AI component to validate your hypothesis. Test your governance protocols and measure the results on a small scale before a wider-scale deployment.

6. Measure, then start over 

Rework your strategy again and again. A data strategy, especially one that supports the dynamic development of AI and autonomous systems, requires measurement, refinement, tracking, and continual iterations based on performance and the evolution of needs.

Ready to implement your data strategy?

By this point you probably get the point: Intuition is no longer enough to lead a company. Today, it’s data that guides decision making.

• Do you have a clear plan for collecting and enhancing your proprietary data?
• Do you track relevant key performance indicators?
• Are your marketing technology (MarTech) tools aligned with your data activation goals?
• Can your structure handle the integration of artificial intelligence or agentic systems?

At adviso, our teams are passionate about data. Tell us about your issues so we can help you build an effective data strategy.