12  Business Analysis in Data Analytics

Data has become central to business decision-making. Organizations collect vast amounts of information and seek to extract insights that drive competitive advantage. This chapter explores the intersection of Business Analysis and data analytics, showing how these disciplines complement each other.

12.1 The Data-Driven Organization

Organizations today have access to more data than ever before. Customer interactions, operational processes, financial transactions, and external sources generate continuous streams of information. The challenge is no longer collecting data but making sense of it and using it effectively.

Data analytics encompasses the techniques used to examine data sets and draw conclusions. It ranges from basic descriptive statistics to advanced machine learning. Regardless of the specific techniques used, effective data analytics requires clear understanding of what questions need to be answered and how insights will be used.

This is where Business Analysis enters. Without clear requirements, analytics projects produce outputs that are technically interesting but practically useless. Without understanding business context, data scientists build models that miss the point. Without stakeholder engagement, even valuable insights fail to drive action.

12.2 The Business Analyst’s Role in Analytics

Business Analysts play several critical roles in analytics initiatives.

12.2.1 Defining the Business Question

Before any analysis begins, someone must define what question needs to be answered. This is harder than it sounds.

Stakeholders often express vague desires: “We want to understand our customers better” or “We need insights from our data.” The Business Analyst must translate these desires into specific, answerable questions: “Which customer segments are most likely to churn in the next 90 days?” or “What factors predict whether a lead will convert to a sale?”

Defining the right question requires understanding the business context. Why does this question matter? How will the answer be used? What decisions will be made based on the insights? A technically excellent analysis that answers the wrong question wastes resources.

12.2.2 Requirements for Analytics Systems

Analytics platforms, data warehouses, and business intelligence tools require requirements like any other system. What data sources should be integrated? How should data be transformed and organized? What reports and dashboards are needed? Who needs access to what information?

Business Analysts gather these requirements from stakeholders across the organization. Finance needs certain reports. Marketing needs different views. Operations needs real-time dashboards. Executives need strategic summaries. Understanding and balancing these needs is Business Analysis.

12.2.3 Data Quality and Governance

Analytics is only as good as the underlying data. Business Analysts help define data quality requirements: What does “accurate” mean for each data element? What validations should be applied? How should errors be handled?

Data governance establishes rules for how data is managed across the organization. Who owns each data element? Who can access what data? How long should data be retained? Business Analysts help develop and implement these policies.

12.2.4 Interpreting and Communicating Results

Data scientists produce models and analyses. Business Analysts help interpret what those results mean for the business and communicate them to stakeholders who may not have technical backgrounds.

A model might predict customer churn with 85% accuracy. But what does that mean for marketing strategy? How should sales prioritize their efforts? What process changes would reduce churn? Translating analytical outputs into actionable recommendations requires business understanding that complements technical expertise.

12.3 Analytics Techniques for Business Analysts

While you do not need to become a data scientist, understanding common analytics techniques helps you collaborate effectively.

12.3.1 Descriptive Analytics

Descriptive analytics answers “what happened?” It summarizes historical data through reports, dashboards, and visualizations. Most organizational analytics is descriptive: monthly sales reports, customer satisfaction scores, operational metrics.

Business Analysts define requirements for descriptive analytics: What metrics matter? How should they be calculated? What dimensions allow meaningful comparison? How should results be visualized?

12.3.2 Diagnostic Analytics

Diagnostic analytics answers “why did it happen?” It goes beyond reporting to understand causes. If sales declined last quarter, diagnostic analytics explores which products, regions, or customer segments drove the decline and what factors might explain the pattern.

Root cause analysis, which you learned in the strategy chapter, is a form of diagnostic analytics. The Five Whys and fishbone diagrams help structure the investigation.

12.3.3 Predictive Analytics

Predictive analytics answers “what will happen?” It uses statistical models and machine learning to forecast future outcomes based on historical patterns.

Business Analysts help define predictive analytics requirements: What outcomes do we want to predict? What lead time is needed for predictions to be useful? What accuracy is acceptable? How will predictions be used in operational processes?

12.3.4 Prescriptive Analytics

Prescriptive analytics answers “what should we do?” It recommends actions based on predictions and optimization algorithms.

Business Analysts ensure prescriptive recommendations align with business constraints and values. An algorithm might recommend price increases that would damage customer relationships. A scheduling optimizer might create work patterns that violate labor agreements. Human judgment must evaluate algorithmic recommendations.

12.4 Data Requirements and Business Analysis

Defining data requirements combines Business Analysis techniques with understanding of data concepts.

12.4.1 What Data Is Needed?

Start with the business question and work backward. If you want to predict customer churn, what data might indicate churn risk? Purchase frequency? Support ticket volume? Payment issues? Engagement with marketing communications?

For each potential data element, consider: Does this data exist? Where is it stored? How accurate is it? How timely? What transformations are needed?

12.4.2 Data Lineage and Provenance

Understanding where data comes from and how it has been transformed is critical for trusting analytical results. Business Analysts document data lineage: source systems, extraction processes, transformations applied, and how data moves through the analytical pipeline.

12.4.3 Data Privacy and Ethics

Analytics often involves personal data. Business Analysts must ensure data use complies with privacy regulations and ethical standards. What consents have users provided? What uses are permitted? What anonymization is required?

12.5 Collaborating with Data Teams

Effective analytics requires collaboration between Business Analysts, data scientists, data engineers, and business stakeholders. Each brings different expertise.

Data engineers build the infrastructure for collecting, storing, and processing data. They need requirements for what data to capture and how to organize it.

Data scientists build models and conduct analyses. They need clear problem definitions and domain context to develop useful solutions.

Business stakeholders provide domain expertise and will use analytical outputs. They need to be engaged throughout the process, not just at the end.

Business Analysts bridge these groups, translating between technical and business languages, ensuring requirements are clear, and keeping efforts aligned with business value.

12.6 Case Example: Customer Analytics Platform

A retail company wanted to build a customer analytics platform. They had data from point-of-sale systems, e-commerce, loyalty programs, and customer service interactions, but this data was siloed and difficult to use.

The Business Analyst began by understanding business needs. Marketing wanted to target campaigns more effectively. Merchandising wanted to understand product preferences by customer segment. Customer service wanted to identify at-risk customers. Executives wanted dashboards showing customer health metrics.

She documented requirements from each stakeholder group, identifying common needs and conflicts. Everyone wanted a unified customer view, but they disagreed about which identifier should be primary and how to handle duplicate records.

Working with data engineers, she defined requirements for the data infrastructure: which source systems to integrate, how to match customer records across systems, what transformations to apply, how to handle data quality issues.

Working with data scientists, she defined requirements for analytical models: segments based on behavior patterns, churn prediction scores, lifetime value estimates, product affinity recommendations.

Working with business users, she defined requirements for reports and dashboards: what metrics to display, what drill-down capabilities were needed, how often data should refresh, who could access what information.

The platform took eighteen months to build. Throughout that time, the Business Analyst maintained alignment between technical implementation and business needs. When priorities shifted, she facilitated re-planning. When stakeholders raised concerns, she ensured they were addressed. When technical constraints required trade-offs, she helped make decisions that preserved business value.

The platform succeeded because Business Analysis ensured that technical capabilities matched business requirements. Other companies with similar technical infrastructure failed to achieve similar results because they built technology without clear requirements.

12.7 Building Data Literacy

As a Business Analyst working with data, you should develop basic data literacy.

Understand data types and structures. Know the difference between structured and unstructured data, relational and non-relational databases, batch and streaming data.

Learn basic statistics. Understand means, medians, distributions, correlations, and significance testing. Know enough to evaluate whether analytical claims are justified.

Develop visualization skills. Understand which chart types suit which data and purposes. Know how to design dashboards that communicate effectively.

Learn SQL basics. Being able to query databases directly accelerates your ability to explore data and validate understanding.

Understand common analytics tools. Familiarity with tools like Excel, Tableau, Power BI, or Python basics helps you collaborate with data teams.

You do not need to become an expert in any of these areas. But basic competence helps you ask better questions, evaluate analytical work, and bridge the gap between data teams and business stakeholders.

12.8 Reflection Questions

1. What data does your organization (or an organization you are familiar with) collect that is not being used effectively?
2. What business questions could be answered with better analytics?
3. How might Business Analysis skills help an organization get more value from its data?
4. What data literacy skills would most benefit your career plans?