Developing a Data Strategy (B2B)
In an era where data and AI determine competitive advantage, a solid data strategy is no longer a luxury but a necessity. For medium and large (SME) companies in B2B markets, a well-structured data strategy can lead to better decisions, more efficient processes, and higher revenue.
A data strategy defines how data is collected, managed, analyzed, and applied to achieve business goals. It is the bridge between raw information and strategic action.
What is a data strategy?
A data strategy is a plan that outlines how an organization uses data to achieve its business objectives. It describes the vision, goals, processes, and tools for data collection, storage, analysis, and application.
Core functions of a data strategy
- Guidelines for data collection and management
- Policy frameworks for data security and compliance
- Processes for data analysis and AI integration
- KPIs for data-driven performance
Example: An industrial wholesaler uses a data strategy to analyze customer behavior and optimize inventory management, resulting in 20% lower stock costs.
Why SME companies benefit from a data strategy
- Better decision-making: Data-driven insights reduce risks.
- More efficient processes: Automation based on data increases productivity.
- Competitive advantage: Faster response to market trends and customer needs.
- Improved customer relationships: Personalized communication and offers.
According to McKinsey, companies with a mature data strategy achieve on average 23% higher profitability.
Would you like to develop a data strategy that truly adds value to your B2B organization? Schedule a consultation with Sales Improvement Group.
The building blocks of an effective data strategy
| Building block | Action | Result |
|---|---|---|
| Vision & goals | Link data usage to business strategy | Targeted investments |
| Data collection | Determine which data is relevant and how to collect it | Reliable dataset |
| Data governance | Establish rules for data quality, privacy, and compliance | Consistent and secure data |
| Analytical capability | Invest in tools and skills for data analysis | Insights to steer decision-making |
| AI integration | Develop applications for prediction and automation | Faster and better decisions |
From data to action: implementation steps
- Map the current situation – What data do you already have and how is it used?
- Define goals – What do you want to achieve with data (e.g., revenue growth, cost reduction)?
- Perform a gap analysis – Where are the gaps in your current approach?
- Select technology – Choose tools for data collection, storage, and analysis.
- Set up processes – Define who is responsible for which tasks.
- Training and adoption – Ensure employees understand and embrace the new way of working.
- Monitor and improve – Evaluate and optimize regularly.
Use cases
Use case 1 – Rolls-Royce: predictive maintenance with AI
Rolls-Royce developed a data strategy in which sensor data from aircraft engines is analyzed in real time. By applying AI for predictive maintenance, they were able to prevent failures and reduce maintenance costs by 25%.
Source: Rolls-Royce Case Study
Use case 2 – Heineken: customer insights for marketing optimization
Heineken implemented a data strategy to integrate customer data from various channels (retail, hospitality, online). With AI, customer segments were refined and campaigns personalized, resulting in 20% higher response rates.
Source: Heineken Digital Transformation
Use case 3 – Siemens: smart factory data-driven decision-making
Siemens developed a data strategy for their “smart factories,” linking production data with supply chain data. These insights increased production efficiency by 30% and resolved delivery issues faster.
Source: Siemens Digital Industries
Checklist: is your data strategy ready for AI?
- Are data goals linked to business objectives?
- Is data quality ensured through clear governance?
- Are the right tools and technologies in place?
- Are employees trained in data analysis and AI?
- Is there a plan for continuous optimization?
4 or more “yes” answers? Then your organization is ready to integrate AI.
Frequently asked questions
- What is included in a data strategy?
- Vision, goals, processes, governance, technology, and KPIs for data usage.
- What are the 5 essential components of a data strategy?
- Vision & goals, data collection, governance, analysis, AI integration.
- What is the difference between a data strategy and a data management strategy?
- A data strategy is broader and focuses on goals and application; data management focuses on maintenance.
- What is the National Data Strategy?
- The government’s plan for responsible and effective use of data in the Netherlands.
Contact us
Would you like to get more value from data and AI to accelerate your B2B growth? Contact Sales Improvement Group.