How to create a data strategy in 7 steps.

Many companies struggle to get their data game accelerated into industrialization mode. Once first pilot projects have been succesfully launched it's time to move from explore mode into scaling the use-cases to a broader environment. Having a clear data strategy is not an option but a must if companies what to succeed using data as a source for creating customer value and continuous improvement. Strategy turns resources into assets and activities into results. This is how a data strategy can be implemented following a 7-step-program.

1. Identify the business objectives and goals that the data strategy will support.

... that could for example mean to aim to ...

• ... improve marketing efforts by better understanding customers
• ... or increase operational efficiency by identifying bottlenecks in production process

2. Determine the key data sources (internal of external) that will be used to support these objectives.

... that could for example mean ...

• … that the company might use customer surveys and transaction data to better understand its target market.
• … or to use sensor data from its factory equipment to identify inefficiencies in the production process.

3. Develop a plan for how the data will be collected, cleaned, and organized, in order to make it usable for analysis and decision-making.

This might involve processes for ensuring data quality and consistency, as well as developing data pipelines to automate data ingestion and preparation and could mean that ...

• … a company decides to develop a data quality control process that includes regular audits and outlier detection.
• … or uses a data lake to store and organize its data for analysis.

4. Identify the key stakeholders who will be involved in the data strategy, including executive sponsors, data analysts, and business users.

This will help to ensure that the strategy has the support and buy-in necessary to be successful and could mean ...

• … that the CEO, CMO, and head of data science might all be key stakeholders in a data strategy for improving marketing efforts.
• … while the COO and head of engineering might be key stakeholders in a data strategy for increasing operational efficiency.

5.  Develop a governance plan for the data strategy, including roles and responsibilities, data ownership, and data security and privacy.

This will help to ensure that the data is managed and used in a responsible and ethical manner and could mean ...

• … for example, the governance plan might specify that the data science team is responsible for maintaining the data pipelines, while the legal team is responsible for ensuring compliance with data privacy regulations.
• ... or the plan might include rules for data access and usage, such as requiring data access to be approved by a manager and requiring all data usage to be documented and reviewed.

6. Create a roadmap for implementing the data strategy, including timelines, budgets, and resources.

This will help to ensure that the strategy is executed in a timely and efficient manner and could mean ...

• … for example, that the roadmap might include milestones for completing the data collection and preparation phase, as well as the development of specific data-driven marketing campaigns
• ... or that the roadmap might include plans for training and support to ensure that business users have the skills and resources necessary to make use of the data.

7. Monitor and evaluate the success of the data strategy and make adjustments as needed.

This will help to ensure that the strategy remains aligned with the business objectives and goals, and that it continues to deliver value over time and could mean ...

• … for example, the company might regularly review metrics such as customer retention and conversion rates to assess the effectiveness of its data-driven marketing efforts, or use process mining techniques to identify areas for improvement in its production process.
• … the company might conduct regular surveys or interviews with key stakeholders to gather feedback on the data strategy and its impact on the business.