BPM Analytics
Types of business analytics: The complete guide
Global economies are increasingly becoming data-driven. So, effective decision-making hinges on a business’s ability to harness and interpret data. Business data analytics provides a structured approach to uncover insights, optimise strategies, and stay competitive in dynamic markets. By leveraging descriptive, diagnostic, predictive, and prescriptive analytics, businesses can understand past performance, predict trends, and make proactive decisions.
Infosys BPM leverages domain expertise, advanced analytics tools, and a metric-driven approach to deliver comprehensive business process analytics. From advisory to model development and insights generation, we optimise processes and unlock business value.
This comprehensive guide explores the four types of business analytics, their use cases, and the tools required to transform data into actionable strategies.
Analytics for data-driven decisions
Business analytics enables organisations to gain a deeper understanding of past performance, uncover root causes, anticipate future trends, and implement actionable strategies. Below is an in-depth look at each type of analytics, their methodologies, and their use cases.
Descriptive analytics
Descriptive analytics examines historical data to provide insights into past events and trends, answering the question, “What happened?”. It’s the foundation of all analytics, offering a clear snapshot of performance and patterns.
Descriptive analytics aggregates and analyses large datasets. It identifies trends, anomalies, and relationships in data. Techniques such as data visualisation, trend analysis, and Key Performance Indicators (KPIs) help businesses interpret and understand their operations. It uses tools like Excel, Tableau, and Power BI.
Descriptive analytics equips business leaders with actionable insights into past performance. This clarity ensures businesses can address gaps and build stronger strategies moving forward.
Use cases
Descriptive analytics has proven its value in these industries:
- Retail: Analysing historical sales trends to identify high-performing products and optimise future marketing campaigns.
- Finance: Generating profit and loss statements to provide stakeholders with a comprehensive view of financial health.
- Manufacturing: Assessing production data to identify inefficiencies and improve operational workflows.
- Healthcare: Monitoring patient demographics and outcomes to improve treatment protocols and resource allocation.
Diagnostic analytics
Diagnostic analytics delves deeper into data to explain why certain events occurred, providing critical insights into causation and correlations. It bridges the gap between understanding what happened and making informed decisions to address root causes.
Diagnostic analytics involves advanced techniques such as data mining, drill-down analysis, and correlation analysis to identify underlying factors influencing outcomes. It analyses co-occurring trends, detects anomalies, and uncovers relationships between variables. The best outcomes can be expected from tools like SQL, R, Python, and SAP Analytics Cloud.
With diagnostic analytics, enterprises can move beyond symptoms and address core issues. It guides strategic improvements, ensuring decisions are data-driven and impactful.
Use cases
Diagnostic analytics is instrumental for organisations aiming to resolve challenges and improve operations:
- Marketing: Identifying why a campaign underperformed by analysing engagement metrics and audience behaviours.
- Supply chain: Understanding delays by analysing logistics data, vendor performance, and production schedules.
- Healthcare: Pinpointing causes of treatment inefficiencies by studying patient outcomes, medical procedures, and hospital workflows.
- Retail: Determining factors driving customer churn by examining purchase history and customer feedback data.
Predictive analytics
Predictive analytics forecasts future trends and behaviours by analysing historical data using statistical and machine learning models. It identifies patterns and relationships in data. It answers the critical question, “What might happen next?”.
Techniques like regression analysis, time series forecasting, and machine learning models are commonly used. These techniques can be implemented with tools like SAS, Azure Machine Learning, and IBM SPSS.
This analytics type empowers decision-makers with foresight, helping them mitigate risks and seize emerging opportunities.
Use cases
Predictive analytics has a transformative impact across several industries:
- Retail: Forecasting customer demand during peak seasons to optimise inventory and prevent stockouts.
- Finance: Identifying at-risk customers to mitigate loan defaults and fraudulent activities.
- Healthcare: Predicting patient admission rates to allocate resources and enhance care quality.
Prescriptive analytics
Prescriptive analytics takes data analysis a step further by providing actionable recommendations to address challenges and optimise outcomes. It answers the critical question, “What should we do next?”.
This type of business data analytics combines historical data, predictive models, and optimisation algorithms to suggest the best actions for achieving goals. Techniques like scenario analysis, simulation modelling, and optimisation algorithms evaluate multiple potential outcomes, enabling leaders to lay down fruitful strategies. Organisations must use effective tools like Simul8, Gurobi, and Alteryx for prescriptive analytics.
Prescriptive analytics provides clarity on how to act in complex scenarios. It empowers business leaders to navigate uncertainty, mitigate risks, and drive sustained growth.
Use cases
Prescriptive analytics plays a pivotal role in decision-making across industries:
- Logistics: Optimising delivery routes by analysing traffic conditions, fuel costs, and delivery schedules.
- Retail: Determining the most effective pricing strategies to maximise revenue during sales seasons.
- Healthcare: Allocating resources like staff and equipment to ensure efficient patient care and reduced wait times.
- Manufacturing: Scheduling maintenance for machinery to minimise downtime and avoid production delays.
Conclusion
Business analytics is a roadmap to smarter, data-backed decisions that fuel growth and innovation. With business process analytics, organisations can unlock actionable insights, improve operations, and stay ahead in competitive markets. With the right tools and strategies, analytics becomes a powerful driver of sustained success.
