In my experience, avoiding information overload and adopting a lean mindset is crucial for effective analytics. By focusing on the essential data and questions, we can avoid getting bogged down in unnecessary details. Defining clear objectives, identifying critical datasets, and selecting appropriate methodologies and tools are essential steps in this process. This logical and organized approach ensures that our analytics efforts are efficient and yield actionable insights.

Don't pick data sources. Virtually infinite data could be surfaced without knowing what it’s for. Start with the use case, whether it be a question or a model you're building, and work backward to understand what data is needed. If the data you need could be sourced in multiple ways, balance completeness with accessibility. The key word is “shift left.” Generally, data that's closest to the system generating it is most accurate and complete, but may not be readily accesible. If the source system data is readily accessible, that's likely the ideal source.

Start by identifying the problem or KPI you want to address, then decide on data sources. Different roles require different KPIs; a CMO’s needs differ from those of a digital campaign manager. Once the KPIs are defined, identify relevant data sources. Often, this data must be integrated, cleaned, and enriched before it can be used effectively.

When faced with a sea of data in strategic marketing decisions, I prioritize by focusing on the sources that directly align with my goals. First, I ensure data accuracy and reliability, favoring sources like CRM systems, website analytics, and customer feedback that have consistently proven trustworthy in my past projects. I look at historical performance metrics that reflect key business objectives, such as conversion rates or customer retention. Additionally, I cross-check data with external market trends to get a full picture. By filtering out irrelevant or outdated data, I can concentrate on what truly drives results and informs actionable strategies.