Building a Data Strategy: Key Steps for Aligning Data with Business Goals

data strategy must begin with a clear understanding of what the business aims to achieve. This involves identifying specific problems the company wants to solve and aligning the data strategy to these objectives. As Libba notes, "What is the business trying to accomplish?" is the fundamental question that guides the development of a data strategy. This initial discovery phase is vital and often overlooked, yet it sets the foundation for all subsequent efforts. Ensuring that executives and teams are aligned on these goals helps prevent miscommunication and misaligned efforts down the line.

Challenges in Data Tracking and Management

Libba shares valuable insights into the importance of tracking data effectively, highlighting the potential pitfalls when this is not done correctly. She recounts a specific challenge where a lack of proper tracking led to inefficiencies and incorrect assumptions in a project. "If it can be measured, you probably want to record it and measure it," she advises, emphasizing the value of comprehensive data tracking in building strong processes. This approach not only supports better decision-making but also helps in setting realistic targets and measuring progress accurately.

Communication and Stakeholder Management

Effective communication is a recurring theme in the discussion, with Libba stressing its role in the success of data projects. She highlights the need to adapt communication to different stakeholders, ensuring that each group receives the information pertinent to their needs. "The way to start with it is understanding what each of the key stakeholders need from you as the leader," she explains, advocating for direct and concise communication. This approach helps maintain transparency and encourages trust among teams, which is essential for overcoming challenges and achieving project milestones.

The Role of Subject Matter Experts

Involving subject matter experts is essential in filling the gap between data science and practical applications, according to Libba. She shares an example where a lack of subject matter expertise led to significant errors in data analysis, illustrating the importance of having knowledgeable individuals who understand both the business context and the data intricacies. "Get your subject matter experts and engage them," she advises, pointing out that these experts can validate outcomes and ensure that data-driven decisions are grounded in reality.