You Don’t Need AI Trends for Better Business Intelligence
As the buzz surrounding AI continues to grow, many mid-market companies find themselves at a crossroads. You have to strike a balance between the dizzying promises offered by tools like ChatGPT and the real ROI they can deliver to your analytics capabilities.
Salesforce’s State of Data and Analytics highlighted several imperatives for companies looking to leverage AI, including trustworthy data, effective governance, and strategic implementation. These recommendations are sound, but they do not address the challenges that mid-market companies face when trying to transform into data-driven enterprises.
The truth is your organization does not need perfect data before catching up to the market with your analytics journey. Instead, a pragmatic and incremental approach balances immediate gains with long-term development, building the momentum needed for lasting change.
Look Beyond the Generative AI Hype
The conversation surrounding generative AI has executives across industries worried they are missing opportunities for improvement. This anxiety is understandable but often misplaced. While generative AI represents an exciting frontier, businesses have been successfully using various forms of AI and machine learning in data analytics for decades. Regression analysis, optical character recognition, and classification analysis often provide more immediate ROI than newer generative solutions.
The surge of interest in ChatGPT and similar tools should not distract from the value your business can gain from these proven approaches. For many organizations, traditional uses of AI, machine learning, and predictive analytics will deliver more concrete business value than rushing to implement the latest generative models. Focus on the business problems you need to solve and ask what insights would drive measurable improvements in your operations, customer relationships, or financial performance.
Begin Your Analytics Journey with Modern Data Platforms
Any successful analytics or AI initiative starts with a sound foundation. Traditional data warehouses, while useful for structured reporting, create limitations for advanced analytics. They typically involve selecting a subset of fields and tables and storing only what is needed for predefined reports and dashboards, which reduces the data set and limits the information that can be extracted from it.
A data lakehouse offers more flexibility for your data scientists. A lakehouse preserves raw data in its original form while also enabling the creation of more structured data models for reporting purposes. Data scientists can access raw data for exploratory analysis, and different teams can transform the same source data to their analytics needs. By protecting your raw data, the lakehouse model creates a platform that supports your immediate reporting needs as well as future stops on your analytics journey. Blue Margin’s managed data service builds this foundation on Microsoft Fabric in a matter of weeks.
Follow the Analytics Ladder: A Practical Framework
Thinking of analytics maturity as a ladder provides a useful framework for progressing from basic reporting to insights drawn from advanced AI. Understanding where your organization sits today on this ladder is the right starting point before deciding where to invest next.
The first rung is Descriptive Analytics, which answers the question of what happened. It includes dashboards and reports illustrating historical performance, trends, and patterns. The second rung is Diagnostic Analytics, which answers why it happened. These reports involve more detailed analysis to understand the factors driving performance, often using drill-down capabilities and correlation analysis. The third rung is Predictive Analytics, which addresses what could happen. Using statistical models and machine learning, it forecasts future outcomes based on historical patterns. The fourth and most sophisticated rung is Prescriptive Analytics, which indicates what will happen and what should be done about it. Using optimization techniques and advanced AI, it recommends actions to achieve desired outcomes.
If your company is starting from ground zero, attempting to jump directly to prescriptive analytics is like trying to jump up four rungs of a ladder at once. Master the basics of descriptive and diagnostic analytics before exploring the possibilities of AI. From there, a crawl, walk, run philosophy of steady progress up the analytics ladder while continuously improving your data foundation is the approach that consistently produces durable results.
Avoid the Data Governance Trap
A strong data foundation is essential to harnessing the potential of digital analytics, but too often an overemphasis on data quality creates an artificial prerequisite companies must clear before doing anything with their data. This seemingly prudent approach can lead to a dangerous cycle of inaction. Companies initiate data governance programs, hold meetings to discuss data governance, and then lose momentum. The enormity of the task becomes too daunting, and interest dies off because no one is reinforcing the need for data quality by producing outputs.
Most organizations have areas where they have good enough data quality to start generating insights while still working on improvements. Rather than viewing data quality as a prerequisite, consider how analytics can actively drive better data quality. One manager at a manufacturing firm we worked with advocated for releasing dashboards even when data quality was not perfect. Instead of reserving two people to work on data quality, he now has over a hundred people engaged with the data and invested in improving it. When people see data in action through custom dashboards, they develop stronger motivation to address quality issues that affect results. This turns data quality from an abstract IT initiative into an organization-wide imperative.
Early Wins Build a Data-Driven Culture
Creating a truly data-driven organization requires more than the right technology. It demands cultural change. Teams need comfort with data, trust in its accuracy, and confidence in their ability to use it effectively. Early wins play a crucial role in this shift. When teams see concrete examples of data improving decisions or uncovering opportunities, they become more receptive to using it in daily work.
These early successes create a cycle of improvement. As more people engage with data, they identify quality issues, suggest enhancements, and propose new use cases. This collective momentum accelerates your analytics journey in ways a top-down approach rarely achieves. By looking beyond the generative AI hype, your organization can make steady progress toward more advanced analytics and AI capabilities. Do not let the pursuit of perfection prevent progress. If you are ready to take the next step, we should talk.
