Topic 1 Introduction to data analytics

Numerous businesses regularly collect data, yet this data is meaningless when it is first collected and before it has been processed. This is where data analytics play a key role. Analyzing this unprocessed data entails looking for insightful information that can be used in real-world situations. Once gained, these insights can be used to inform and direct wise business decisions.

Four primary categories of data analytics

  1. Predictive analytics: This aids organizations in identifying trends and comprehending the causes of events. For instance, it may forecast the effectiveness of a t-shirt advertisement on Facebook based on the location, income, and hobbies of your target market.
  2. Prescriptive analytics: This mixes big data and AI to make recommendations for actions and test hypotheses for improved results. It can respond to queries like “What if we try this?” to enhance outcomes.
  3. Diagnostic analytics: This analyses historical data to determine the causes of events. Finding potential problems before they arise and examining reports in greater detail to get specific information are the two components.
  4. Descriptive analytics: This type of analytics offers fundamental data such as how many, when, where, and what. This information is available from planned reports (also known as prepared reports) and ad hoc reports, which are particularly helpful for in-depth insights like knowing your social media audience.