This guideline is based on my personal experiences in tens of data analytics & data science projects and consulting works for the last decade.  It should be fit for different organizations including corporations, non-profit organizations and institutions.

Here is my suggestions on possible actions throughout a 5-year periods:

Year 1:  Building the Foundation

In this stage, it is preparing the proper resources for the establishing the infrastructure and developing skills for the staff.  There are corresponding activities suggested in this stage:

1. Conduct a comprehensive assessment of the organization’s current data science capabilities, including data management processes, data quality, and data infrastructure.
2. Identify and prioritize key areas where data science can provide the most value to the organization, such as fundraising, program evaluation, and operational efficiency.
3. Build a small data science team, including data scientists and data engineers, to begin developing data models and infrastructure.
4. Launch a pilot project in a targeted area to test the effectiveness of data science and identify any challenges.
5. Invest in staff training and skill development to build a data-driven culture throughout the organization.

Year 2: Strengthening Data Exploration and Analysis

At this point, it is time to ensure the analytical capabilities being built and ready to implement analytical exercises among different areas.

1. Expand the data science team to include additional data scientists, data engineers, and data analysts.
2. Roll out the pilot project to other areas of the organization and begin scaling up data science initiatives.
3. Build a data governance framework to ensure data quality, privacy, and security.
4. Invest in data infrastructure, such as cloud computing and data warehouses, to support the growing data needs of the organization.

Year 3: Scaling and Optimization

In general, data projects should be started small and expanding by stages.  At the third year, it is suitable to expand data science initiatives to support decision making across the whole organization.

1. Continue expanding the data science team and build out additional infrastructure to support data science initiatives.
2. Develop a data-driven fundraising strategy, leveraging data science to identify potential donors and optimize fundraising campaigns.
3. Build out a comprehensive data analytics platform to provide real-time insights into program effectiveness and operational efficiency.
4. Integrate data science into the organization’s strategic planning process, ensuring that data is used to inform key decisions.

Year 4: Applying Advanced Techniques and Innovation

At the stage, it is expected the data science team well-developed on skills and techniques.  It is a great time to apply more advanced techniques and introduce innovative solutions.

1. Invest in advanced data science techniques, such as machine learning and predictive analytics, to improve program evaluation and decision-making.
2. Build out a data visualization and reporting platform to communicate insights and recommendations to key stakeholders.
3. Expand the organization’s data partnerships and collaborations to access external data sources and expertise.

Year 5: Maturity and Strategic Impact

With the matured environment, it is the stage to drive additional value and competitive advantages by data analytics.

1. Continue to refine and improve data science initiatives and infrastructure to ensure ongoing value to the organization.
2. Develop a comprehensive data science roadmap for the next 5 years, outlining key priorities and initiatives.
3. Monitor and evaluate the impact of data science on the organization’s mission, goals, and outcomes.

Overall, this strategic plan is designed to build a strong foundation for data-driven decision-making and ensure that data science is integrated into all areas of the organization’s operations.  However, each organization should face their unique problems and resource limitations.  The time-line and suggested tasks must be refined based on the organization needs.