Laura Sergio Explains How Data Analytics Powers Smarter Business Decisions

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Data is a strategic tool used to uncover patterns, improve processes, and steer decisions that directly affect a company’s bottom line. Whether the goal is to optimize pricing, reduce churn, or improve supply chain performance, data analytics offers a clearer path forward.

However, using data effectively requires the right questions, governance, culture, and alignment with core business goals. Organizations that make data analytics part of their decision-making DNA are outpacing their competitors and building resilience in the face of change.

Below are several practical ways businesses are using data analytics to make smarter, faster, and more informed decisions.

Align Analytics with Business Strategy

The most successful organizations treat data as a strategic asset, not just an operational function. Analytics works best when it’s tied directly to business objectives, whether that’s boosting retention, improving margin, or accelerating innovation.

“In the past, data was often reactive,” said Laura Sergio, a business strategy and analytics advisor. “Now we’re asking how to align it proactively, what decisions need to be made, what data informs those decisions, and how fast we can act.”

This shift from reporting to real-time decision support means analytics teams must collaborate closely with business units. Setting shared priorities ensures that data models answer the right questions and focus on value-generating outcomes.

Forrester’s 2025 forecast reinforces this approach, stating that companies that pair their AI and data strategies, execute cross-functionally, and focus on measurable business value will outperform.

Ask Better Questions and Avoid Common Pitfalls

Smart decisions start with smart questions. Yet, many companies rush into data analysis without clearly defining the problem or understanding how the data may mislead.

Common pitfalls include:

• Relying too heavily on correlation without proving causation
• Using lagging indicators to inform real-time decisions
• Ignoring selection bias or incomplete data sets
• Overvaluing visualizations without checking the methodology

Harvard Business Review warns that even data-savvy leaders can fall into these traps. To avoid them, companies are building interpretation frameworks, such as hypothesis mapping, pre-mortem analysis, and “what if” simulations, into their decision workflows.

“Good analytics doesn’t tell you what to think,” Laura Sergio added. “It sharpens your ability to challenge assumptions and spot what’s missing.”

This shift toward decision-intent analytics helps reduce risk and improve the quality of outcomes across departments.

Enable Real-Time, Self-Service Decision Making

Business moves fast. Waiting days or weeks for reports doesn’t cut it anymore. That’s why many companies are adopting modern BI tools that empower frontline teams to explore insights on demand.

Features like natural language querying (NLQ), governed semantic layers, and visual exploration tools help non-technical users get answers faster. Instead of bottlenecking in the data department, insights flow directly to decision-makers.

According to IDC, enterprises will invest more than $202 billion in generative AI tools by 2028, many of which support data summarization, decision scenarios, and automation. But this investment must be matched with data quality, metadata, and governance to be truly effective.

Organizations that embed analytics into everyday workflows, rather than treating them as standalone functions, see stronger adoption and faster cycles from insight to action.

Track Value, Not Just Activity

Data projects often fail not because the models are wrong, but because no one measures whether they make a difference. High-performing businesses take a disciplined approach to tracking impact.

They build what some call a benefits ledger, where each analytics use case is tied to a defined outcome, such as revenue uplift, cost savings, or reduction in decision time. This ledger evolves from pilot to production, with KPIs updated and non-performing models retired.

Research from McKinsey shows that most companies only realize about one-third of the expected value from their digital initiatives. A key reason is the lack of measurement discipline and accountability.

By holding analytics efforts to the same standard as product investments, companies ensure that data continues to serve the business, not just create activity for activity’s sake.

Foster a Culture of Data Literacy and Trust

Analytics doesn’t work in a vacuum. It depends on people: how they interpret, trust, and act on data. That’s why organizations are placing renewed emphasis on data literacy and culture.

Data literacy isn’t about learning SQL or becoming a data scientist. It’s about teaching teams to think critically, question trends, and understand what a good metric looks like. It’s also about using shared language, so that terms like “conversion rate” or “forecast variance” mean the same thing across the business.

Companies that invest in training, align KPIs across functions, and eliminate vanity metrics are seeing stronger uptake and more confident decision-making. Some are even embedding data coaches within teams or creating “metrics that matter” initiatives to clean up outdated reports.

Trust, after all, is the foundation. When teams trust the data and know how to use it, they move faster, ask better questions, and feel more accountable for results.

Build Use Case Portfolios Around Core Business Levers

Not every analytics project should get the same attention. To focus resources, many organizations are building use-case portfolios mapped to core value levers, such as pricing, retention, supply chain optimization, or workforce planning.

For example:

• Finance teams are using driver-based forecasting and cash flow simulation
• Marketing teams are modeling churn risk and campaign lift
• Operations teams are running demand forecasts and scenario planning
• HR teams are analyzing attrition and skills alignment

By tying analytics directly to specific decisions and linking models to leading indicators, businesses can close the gap between insight and action. This also makes it easier to track ROI and scale proven models across departments.

Final Thoughts

Analytics is no longer optional. But using it well requires strategic alignment, disciplined execution, and a culture that values critical thinking.

The organizations making the smartest business decisions today aren’t just reacting to data. They’re shaping their strategies around it, asking better questions, and building systems that improve over time.

In a business landscape defined by speed, uncertainty, and complexity, data analytics provides answers and clarity. And that clarity, when matched with strong leadership and execution, is what separates winners from the rest.