What are the key steps to ensuring data analysis is relevant to your business goals?

Crafting SMART goals is undoubtedly a smart approach. However, it's equally crucial to delve into the underlying reasons why these are your goals and what are the best ways to measure them. Ask yourself if these metrics will truly offer the insights you seek, for instance, measuring conversion based solely on customer entry may provide an incomplete picture without factoring in the abandonment rate.

You need to define your business goals first. In simple words you can’t measure it if you don’t know what to measure. Then identify the KPI’s. Each phase of business has its own KPI’s. If your business goal is to increase product sales, then you need to work closely on measuring the KPIs related to customers, sales, NPS and etc The data should be collected according to your specific KPIs

A top-down approach should be followed for defining the questions - starting from the problem to be solved post the analysis. - Start with a broad question that summarizes the main problem or opportunity to be explored. - Break down the question into smaller, more specific questions that address different aspects of the problem or opportunity. - Prioritize the questions based on their importance, feasibility, and availability of data. - Refine the questions to make them clear, concise, and measurable. (SMART) Often organizations fail to answer questions due to a lack of data needed for analysis, but the first step should be identifying how the data should be or what attributes should be available to run an analysis.

Although, metrics are a trusted method of obtaining data regarding the questions for analysis. But often, when focusing on certain metrics the team efforts can be misguided into pumping just the one number and leaving other parts of their process in the dark. So a scenario analysis is an essential first step before introducing any metrics as the north star for a team's performance. Other things to keep in mind: - Metrics should help answer the questions aligned to the business goals, inform data driven decisions, and enhance the performance monitored. - Avoid metrics that are irrelevant, ambiguous, or misleading. - Comparing metrics with the industry norms can help identify any deviations needed in the analysis approach.

Define Goals: Clearly understand your data analysis objectives. Assess Data Types: Choose tools compatible with your data formats. User Proficiency: Select user-friendly tools or provide necessary training. Integration Needs: Ensure tools integrate seamlessly with existing systems. Analytical Depth: Opt for tools with required analysis capabilities. Visualization Features: Pick tools offering effective data visualization. Scalability: Consider tools that can accommodate growing data needs. Budget Constraints: Balance cost against features and benefits. Security Compliance: Prioritize data security and regulatory compliance. Support Availability: Check for reliable customer support and resources.

Interpretation of data is an art that most people lack and can only be rightly done by an artist. But if you are not master at interpreting data then the best technique is to get the feedback of multiple relevant people and then conclude it by yourself from the core business perspective, keeping all the personal liking, sentiments and emotions aside.