Last updated on June 17th, 2023 at 11:43 am
Big data fuels today’s business world. Leveraging data analytics is the key to boosting business intelligence and making more efficient business decisions. When data analysts design an analytics strategy, they must consider current business performance, the collected data sources, and which metrics will be used as targets for success.
Three main types of analytics help businesses maintain a competitive edge. These include descriptive analytics, predictive analytics, and prescriptive analytics. Look at each type of analytics and the valuable insights they provide.
Table of Contents
1. Descriptive Analytics
Descriptive analytics provides a look at past performance through present-day data. This type of analytics tells business users what happened in the business. It is carried out in the preliminary data processing stage to summarize historical events and build a foundation for further analysis and understanding.
The primary methods used in descriptive analytics are data aggregation and data mining. It’s the simplest of the three types of business analytics that businesses perform in daily reporting such as inventory, workflow, and sales.
2. Predictive Analytics
Predictive analytics focuses on forecasting the likelihood of future outcomes and events within the business and is modeled on historical data. Data scientists and statisticians use statistical modeling and machine learning techniques to perform predictive analysis. The patterns found in past and current data can be used to forecast trends, detect risks, and discover future opportunities.
Businesses that use predictive analytics can confidently see into the future to save on costs and earn more revenue. Several examples of predictive analytics include retailers who use analysis to forecast inventory, manage product shipping, and maximize sales. Airlines can use predictive analytics to price tickets based on past demand and trends. Hotels can leverage predictive analytics to forecast capacity and prepare for booking surges.
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3. Prescriptive Analytics
Prescriptive analytics is the most advanced stage of the analytics process in business analytics. This type of analytics tells businesses what to do and the next step to take based on various choices. Prescriptive analytics can determine the outcome of each business decision made and recommend the best action to take based on all possible outcomes.
While prescriptive analytics is related to descriptive and predictive analytics, it focuses on actionable insights instead of just data monitoring. The successful performance of prescriptive analytics requires deep learning and complex neural networks. A prime example of prescriptive analytics is recommendation engines.
Business analytics aims to provide information and expertise that guides business decisions and creates a competitive, informative advantage. Business analytics allows users to turn big data into insights, build statistical models to make business projections, find ways to optimize business performance, advise decision-making, and leverage data to influence business outcomes.
Big data analysis empowers business users to pursue opportunities with the highest chances of success, determine the right strategy to deliver the best return and prepare for upcoming changes or market trends. Business analytics offers an understanding of the current business environment and how to become more competitive, streamlines the decision-making process, and increases revenues while decreasing risk.