Crafting a Winning Business Intelligence (BI) Strategy for your organization

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Have you ever wondered how much data is being collected about everything we do and how often we unknowingly rely on data-driven decision-making in our daily lives? Whether it’s booking a ticket for your next holiday, finalizing a major investment, or deciding what food to order from your favorite app, it’s all driven by data today.

For organizations, having a robust BI and analytics strategy is even more critical, as it can transform raw data into actionable insights, enabling informed decisions that align with business goals. While predictive analytics often gets the spotlight, even a well-structured descriptive analytics approach can help you make the right decisions. Without a structured BI strategy, organizations risk falling into chaos, with reporting frequently presenting a disjointed and inconsistent picture

Early in my career, I had the opportunity to work on an analytics pilot project alongside top industry consultants and the brightest minds within my organization. One key takeaway from that experience was realizing how essential a solid BI strategy is for informed decision-making and achieving a comprehensive view of the customer journey.

Building the right BI foundations and strategy requires endurance, as progress is often incremental, and continuous investment is necessary.

Based on my experience, below are the steps that can be followed to help kick-start your BI journey:

1. Identify the questions you need answered: Begin by understanding the critical questions your department needs answers to. Engage with stakeholders through interviews and surveys to uncover pain points, strategic objectives, and key performance indicators (KPIs). This ensures your BI strategy aligns with the department’s needs.
2. Finding the right data sets: Determine the data sets that are most relevant to your business needs. Identify both internal and external data sources that can provide valuable insights, whether it’s customer data, sales metrics, market trends, or operational/product data.
3. Assess data availability and quality: Once you know what data you need, it is important to extend that thinking to assess them based on their availability, quality, and priority. Determine which data is easily available, and which needs cleaning or integration, and prioritize it based on how it affects business objectives. Reliable, high-quality data is essential for meaningful analysis, and you have to strike the right balance between fixing your data and moving ahead in the journey.
4. Fixing the basics: Before initiating work, it’s essential to identify any data gaps that are critical to your success. You may need to undertake an exercise to address data quality issues before starting to report on the data. This foundational work is often a prerequisite for achieving reliable outcomes and ensuring seamless data integration across entities.
5. Create a 360-degree View: Establish the capability to connect and correlate data sets end-to-end for a comprehensive analysis. Use consistent identifiers for end-to-end linkage, standardize views, and create aggregates for commonly queried data. This approach helps streamline the creation of visualizations and reduces costs. In many BI and analytics projects, a significant portion of the effort goes into linking data, and a standardized approach can prevent each use case from doing their own thing.
6. Speaking a common language: While this may sound basic, it’s important to start by creating a glossary of all the key terms and metrics that are going to be used. Establishing common definitions helps bring everyone onto the same page, promoting consistency across various functions within the organization.
7. Define a Minimum Viable Product (MVP): To avoid overwhelming your sponsor, begin by clearly defining what success looks like for the initial release and which key questions it will address. This approach helps set realistic expectations for stakeholders regarding what they can anticipate. Your role also involves guiding customers to understand the importance of the foundational work discussed earlier and ensuring it is integrated into the MVP to guarantee the program’s success.

During my journey, I experienced how early identification of use cases, foundational work being part of the MVP, and ongoing business engagement were crucial in defining the right BI strategy for our department. I hope this article helps you start thinking in the right direction for driving an effective business intelligence strategy.