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FREQUENTLY ASKED QUESTIONS

Common Questions Simplified Answers

Data analytics is the process of examining and interpreting data to discover patterns, trends, and insights that can help make better decisions. It turns raw data into useful information, enabling businesses to understand their performance and plan for the future.

Data Analytics involves processing and analysing raw data to extract meaningful insights, trends, and patterns that can inform decision-making. It focuses on interpreting data to understand what it tells us about a particular situation or question.

Data Visualization, on the other hand, is the graphical representation of data through charts, graphs, maps, and other visual tools. It helps make complex data more understandable and accessible by presenting it in a visual format that highlights key insights and trends.

In short, data analytics is about understanding the data, while data visualization is about presenting that understanding in a clear and intuitive way.

Analytics Type Purpose Example Use Case
DESCRIPTIVE Answers the question “What has happened?” by summarizing & analysing past data. A sales report showing total revenue generated over the last quarter. Identifying historical trends, generating reports, and understanding past performance.
DIAGNOSTIC Answers the question “Why did it happen?” by delving into the data to uncover the causes of past events or trends. Analysing why sales dropped during a particular month by looking at factors like market conditions, customer behaviour, or product availability. Root cause analysis, identifying correlations, and understanding the reasons behind specific outcomes.
PREDICTIVE (Mostly using Prediction Modeling) Answers the question “What is likely to happen?” by using historical data and statistical models to forecast future events. Predicting future sales based on historical sales data and market trends. Demand forecasting, risk assessment, and identifying potential future opportunities or challenges.
PRESCRIPTIVE Answers the question “What should we do?” by recommending actions based on the insights gained from Descriptive, Diagnostic and Predictive Analytics. Suggesting the optimal pricing strategy for a product to maximize revenue based on predicted customer demand. Decision support, optimization, and strategy formulation.

Analytics implementation is crucial for businesses to achieve the following:

  • Decision Making – e.g. A retailer can use sales data analysis to identify which products are most profitable and focus on promoting those items.
  • Improved Efficiency & Productivity – e.g. A manufacturing company can use analytics to streamline its supply chain, reducing downtime and waste.
  • Enhanced Customer Experience – e.g. A company can analyse customer feedback and purchasing patterns to personalize its marketing campaigns, resulting in higher customer satisfaction and loyalty.
  • Competitive Advantage – e.g. A company can use market trend analysis to anticipate shifts in customer demand and adapt its product offerings before competitors do.
  • Cost Savings - e.g. An energy company can use data analytics to monitor energy usage and implement cost-saving measures.
  • Scalability - e.g. A manufacturing company can use analytics to forecast growth and ensure they have the necessary resources to support increasing demand.
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