How Business Analytics Teams Can Drive Data-Driven Decisions

Most organizations with early-stage Business Analytics teams struggle at some point with the same challenge: how to become more data-driven and evolve beyond using analytics purely for descriptive purposes—such as generating reports to review business performance. Having set up and managed several Analytics teams from inception, I’ve witnessed this struggle firsthand. The journey from reporting to truly integrating data into everyday decision-making is a transformative one.

The five-step framework I outline below will guide your organization, and every member of the Business Analytics team, toward a more data-driven future:

Step 1: Uncover Opportunities for Data Impact

The most important factor for success in Step 1, and for this whole process, is that the Analytics team should be much more than request takers. It is crucial to understand the business, the problems and how different teams in the cross-functional groups operate. Analytics team members should consistently participate in Business Reviews, planning sessions, and forecast discussions, and as many instances as possible that would enable more visibility into how the cross-functional teams think and do. With this, you can use any of these three approaches to identify opportunities:

“What’s Next” assessment: Early-stage Analytics leaders usually work with their team and ask the question, “Where do we think more data or analysis is needed?” This approach doesn’t always help the analyst get into the right mindset, which is why the responses are often too aspirational or disconnected from current enterprise capabilities. Instead, to place the analysts in the right mindset, start with question 1: “How are we already helping to make better-informed decisions?” Spend enough time listing and discussing the answers. Clarity and awareness of this question provide the perfect starting point for question 2: “What are the clear next opportunities to support the business with data to make decisions?”

For instance, if the eCommerce team has already identified which products in your catalogue are generating the highest revenue, that is already a key insight for your sales and marketing teams. Now by asking question 2, you might uncover there is no clear data on which channels are driving these products’ sales. By identifying these channels, your marketing team could tailor campaigns more effectively, leading to higher conversion rates and more strategic product promotions.

Understanding stakeholders’ levers: If the Analytics team understands the stakeholders and knows the levers they control, they can provide more targeted and actionable insights. In that case, ask the Analytics team, “Given their tools, what actions could the stakeholders do better with more information?”

For example, if the team you work with controls the depth of first offer discounts of past season products, then provide them price sensitivity information for different product types/categories so they can adjust for higher/lower discount rates to maximize sales and margins. On the other hand, the opposite is also true: avoid spending effort on insights about levers outside the control of your stakeholders.

Stakeholder Engagement: This approach is particularly effective when stakeholders already have some level of data-driven maturity, such as new hires from advanced organizations. To leverage this, identify the most analytical individuals within the groups supported by Analytics teams and engage them directly with the question, “What is one insight that would help you make more data-driven decisions?”

For example, a newly onboarded stores operations manager or someone that has already being leveraging data might have a suggestion regarding store segmentation, in addition to real estate, sales and demographic data, they could propose incorporating additional specific product attributes from transactional data to refine the segmentation as with experience comes certain “instinct” data users develop. This approach could lead to more accurate and actionable insights for optimizing assortment, operations and inventory management at door level.

Step 2: Build Insightful Analyses

Prioritize the opportunities identified in Step 1 using the method most appropriate to you (value, effort, urgency, impact, etc.). Develop the necessary analyses or dashboards, keeping in mind that the initial version is unlikely to be the final one, so plan resources accordingly.

Step 3: Execute and Iterate with Stakeholders

Collaborate side by side with stakeholders to put your analyses/reports into practice. Set up recurring sessions to review data-driven actions, gather feedback, and refine your outputs until they effectively support decision-making.

Step 4: Measure and Optimize Impact

Measure the results of your implementation. If possible, design a test to measure impact and monetize when relevant. Track report/tool usage and quickly address low adoption. Stay engaged with users to ensure ongoing effectiveness.

Step 5: Scale, Train, and Communicate Success

Ensure successful implementation by focusing on people, processes, and tools. If one of these components is misaligned, it will be painful, and at some point, the development will likely be abandoned. Avoid underinvesting in this step or rushing to the next opportunity, as this can lead to significant setbacks and deter long term trust in the process—a mistake I have observed and experienced. Manage resources from Engineering and Reporting to automate and put into production, provide user training, and create documentation. Share success stories through internal newsletters and give credit to the wider Analytics team and stakeholders.

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To conclude, early-stage Business Analytics teams aiming to successfully transform into enablers of data-informed decisions must deeply understand the performance, objectives, strategies, and tactics of the teams they support. This understanding, combined with a commitment to evolve, will help you get started on this journey. By putting the approaches in Step 1 into practice and following through with the process, organizations will see decisions increasingly driven by data, even if gradually. In the long term, this method helps embed data-driven decision-making into the organizational culture, which is the ultimate goal we should all strive to achieve.