The connection between Maturity and Impact

Benefits of financial measurement are:

• Tangible Quantification: Financial metrics provide a clear, quantifiable way to assess the success of a project. They offer a standardized yardstick to measure investments against returns, making it easier to compare different initiatives and make informed decisions.

• Shareholder Expectations: In an era marked by heightened scrutiny from shareholders and investors, organizations are under immense pressure to demonstrate returns on investments promptly. Financial impact serves as a succinct way to communicate to stakeholders the value generated from data projects.

• Resource Allocation: Organizations often have limited resources and need to prioritize projects that promise the highest return on investment. Measuring projects by their financial impact aids in allocating resources strategically to initiatives that align with business goals.

• Alignment with Strategic Goals: Businesses are increasingly leveraging data projects to achieve strategic objectives, which are often tied to financial performance. Measuring financial impact helps ensure that data projects are aligned with the organization's broader strategic vision.

• Justification for Investment: In a competitive landscape, organizations need to justify the resources invested in data projects. Demonstrating financial impact lends credibility to the project's validity and helps secure ongoing funding.

• Benchmarking and Decision-Making: The financial impact provides a benchmark for comparing the success of different projects over time. It aids decision-makers in identifying successful strategies and refining approaches for future initiatives.

• Bottom-Line Focus: The primary goal of many businesses is to generate profit. Measuring data projects by their financial impact aligns with this overarching objective, underscoring the importance of data-driven efforts in contributing to the organization's financial health.

While the financial impact remains an integral aspect of measuring data projects, it's essential to acknowledge that this approach might not capture the full spectrum of benefits that data initiatives can yield. Holistic evaluation should also consider non-monetary gains like improved customer satisfaction, streamlined operations, enhanced data security, and long-term innovation. In essence, while financial impact measurement provides a valuable perspective, it should ideally be complemented by a more comprehensive evaluation framework that encompasses both tangible and intangible outcomes.

However, one aspect that is typically never measured, is the complexity of a project. This means that a project that delivers substantial impact for an organisation, might not be easy to execute. Reasons for that might vary. Data projects can fail for a variety of reasons, and these failures can occur at any stage of the project, from conception to execution. Here are some common reasons why data projects fail:

• Lack of Clear Objectives: Data projects often fail when there is no clear understanding of what the project aims to achieve. Without well-defined objectives and goals, it's challenging to measure success or even determine the project's direction.

• Poor Planning: Inadequate planning can lead to project failure. This includes not defining the scope, not allocating the right resources, or not establishing a realistic timeline.

• Insufficient Data Quality: Low-quality or unreliable data can significantly hinder a data project's success. If the data used for analysis is inaccurate, incomplete, or outdated, it can lead to incorrect conclusions and ineffective decision-making.

• Inadequate Skill Sets: Data projects often require a range of skills, from data engineering and data science to domain knowledge. A lack of the necessary skills on the project team can lead to incomplete or suboptimal solutions.

• Resistance to Change: Resistance from employees or stakeholders who are not comfortable with data-driven decision-making can be a significant barrier. Cultural resistance to change can undermine the adoption of data-driven approaches.

• Technology Challenges: The choice of technology and tools for data projects can be critical. If the selected tools are inappropriate for the project's needs, or if there are technical challenges that are not adequately addressed, the project may fail.

• Scope Creep: Expanding the scope of a data project without proper assessment and planning can lead to budget and timeline overruns, making it difficult to achieve the project's original objectives.

• Inadequate Communication: Poor communication within the project team or with stakeholders can lead to misunderstandings, misaligned expectations, and ultimately project failure.

• Data Privacy and Compliance Issues: Legal and ethical concerns related to data privacy and compliance with regulations can pose significant challenges. Failure to address these issues can lead to costly legal consequences.

• Lack of Leadership and Accountability: Without strong project leadership and clear accountability, data projects can lose direction and momentum. Project managers and leaders need to ensure that everyone is working towards the same goals.

• Unrealistic Expectations: Expecting immediate and miraculous results from a data project is unrealistic. It's important to set realistic expectations about what can be achieved with the available data and resources.

• Inadequate Testing and Validation: Insufficient testing and validation of models, algorithms, or data pipelines can lead to errors and unreliable results. Rigorous testing and validation processes are crucial for ensuring the accuracy and reliability of a data project.

• Inadequate Maintenance and Monitoring: Data projects often require ongoing maintenance and monitoring to ensure that they continue to deliver value. Neglecting this aspect can lead to project obsolescence over time.

To mitigate these risks and increase the chances of success, organizations should invest in proper planning, data governance, talent development, and a culture that values data-driven decision-making. Additionally, ongoing evaluation and course correction are essential to adapt to changing circumstances and requirements.

Therefore, we believe that it is necessary to score the (financial) impact together with the maturity of data within an organisation. On the next page, we will discuss this connection further.