What is Business Analytics All About?
What is Business Analytics?
Business analytics is a powerful data management solution and business intelligence subcomponent. Business analytics is defined as the process by which organizations utilize statistical methods and technologies for historical data analysis. Significant objectives of business analytics are to gain new business performance insights and improve decision making capabilities. Increasingly, professionals are realizing the benefits business analytics offer for their organizations. Benefits business organizations receive can range from data driven decision making to future outcomes predicted more accurately. All business professionals, from human resources to supply chain managers can benefit from business analytics.
A data analyst that performs business analytics must know the difference between business analytics vs data analytics. Data analytics analyzes data sets to identify trends and insights. Insights and trends identified by data analytics are then used for data driven decision making purposes. Business analytics focuses not only on practical data driven decision making but also on implementing changes.
There are 4 business analytics types that all business analysts must understand. The 4 types of business analytics are descriptive, prescriptive, predictive, and diagnostic analytics. Gaining a better understanding of the specific different types of business analytics used is immensely beneficial. Depending on what the objectives of a researcher are, different analytics types may be used. A combination of analytics types is often utilized to optimally benefit business analytics efforts. In fact, using all 4 business analytics types step by step is a great business analytics technique.
1. Descriptive Analytics
Descriptive analytics is the first stage of business analytics which supplies a snapshot of past and real time business conditions. Business analysts often use descriptive analytics to obtain a greater understanding of current business performance and operations. Descriptive analytics interprets historical data in order to identify trends and patterns. Key performance indicators, commonly abbreviated as KPIs, are also utilized to identify patterns and trends.
Data mining and data aggregation are both regularly used in descriptive analytics. Data mining extracts useful information from data sets that are raw and large. Data aggregation gathers raw data and translates it into a statistical analysis compatible format. With a clear view of statistical analysis compatible data, business strengths and weaknesses are clear. Analytics tools with machine learning and information technology capabilities are incredibly useful for these processes.
An objective of descriptive analytics is to make business data real world accessible. As such, through descriptive analytics business data is made accessible to decision making staff members, investors, and shareholders. Data visualization is often associated with descriptive analytics. Data visualization best practices include using data to create an incredibly accessible and engaging visual representation. The comprehensive historical data and real time data analysis supplied by descriptive analytics is a great foundation for further analysis.
2. Diagnostic Analytics
After successfully performing descriptive analytics many business analyst professionals undertake diagnostic analytics. Using data from the past and real time insight supplied by descriptive analytics diagnostic analytics seeks to understand particulars. In other words, diagnostic analytics provides business analyst professionals with insight into why specific issues occurred. Diagnostic analytics accomplishes this by supplying probabilities and likelihoods. Uncovering patterns and trends helps business analysts identify factors affecting business performance and bottom line profitability.
Utilizing these findings, business analysts can better strategize for the future. Analytics tools and techniques commonly used in diagnosis analytics include data mining and data discovery. Algorithms produced by diagnostic analytics also use data for classification and regression. Diagnostic analytics goes even further by predicting how future trends will influence business conditions.
Tip-: Diagnostic analytics are useful for analyzing business social media and applying data collected for improving future marketing campaigns.
3. Predictive Analytics
Predictive analytics is the most commonly utilized business analytics type. Due to the complexity of predictive analytics both data science and machine learning experts are often required. A significant objective of predictive analytics is to predict future outcomes based on historical data and other sources. Predictive analytics will use data from descriptive analytics to create models predicting the likelihood of different future outcomes possible. Predictive analytics is widely used by sales and marketing team professionals. Common uses for marketing and sales departments include evaluation of customer relationship data management and lead sources.
Predictive analytics is well known for utilizing statistics for forecasting. Analytics tools used in predictive analytics include machine learning modeling and real time analysis. The business intelligence predictive analytics supports in near real time benefits business data driven decision making capabilities. The detailed reports predictive analytics generates allow businesses to make complex predictions about business performance in the future.
4. Prescriptive Analytics
Prescriptive analytics is the final business analytics type and goes even further than predictive analytics does. The accurate predictions that predictive analytics supplies must be real world applied. A significant objective of prescriptive analytics is the capability for business analysts to execute real time business performance changes. Prescriptive analytics accomplishes this by supplying specific recommended actions depending on business performance results desired.
Analytics solutions that prescriptive analytics afford offer answers to complex business questions. Prescriptive analytics most often produces recommendation engines. Recommendation engines are analytics tools with real time capabilities based on sound data. Correctly using data prescriptive analytics offers massively benefits business decision making ranging from supply chain to data management initiatives.
Fact-: Using data to predict future outcomes has real world business benefits including data driven decision making and business performance enhancements.
Key Takeaways of Business Analytics Types
- The analytics process encourages data driven business decisions and business performance optimization.
- The 4 analytics process types are descriptive, prescriptive, predictive, and diagnostic analytics.