Business Analytics
Introduction
Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. It makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making.
History
The concept of business analytics as we know it today has evolved over the decades. The term "business analytics" is often used in association with business intelligence (BI) and big data, but it has a much longer history. The use of simple statistics and descriptive analytics dates back to the 19th century.
Types of Business Analytics
There are four types of business analytics: Descriptive, Diagnostic, Predictive, and Prescriptive.
Descriptive Analytics
Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most of the raw data in business is stored in the form of historical reports, which is descriptive analytics.
Diagnostic Analytics
Diagnostic analytics takes the insights found from descriptive analytics and drills down to find the cause of those outcomes. Diagnostic analytics is characterized by techniques such as drill-down, data discovery, data mining, and correlations.
Predictive Analytics
Predictive analytics uses statistical models and forecasts techniques to understand the future. Predictive analytics uses the past data to predict the future events. This branch of analytics is slowly gaining prominence in the business world and is used to make informed decisions.
Prescriptive Analytics
Prescriptive analytics uses optimization and simulation algorithms to advise on possible outcomes. Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.
Importance of Business Analytics
Business analytics is used by companies committed to data-driven decision-making. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage.
Tools and Techniques
Business analytics depends on sufficient volumes of high-quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available.
Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year. Predictive analytics is used today to not only provide insights on what has happened, but also to predict what will happen in the future.
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
The second area of business analytics involves deeper statistical analysis. This can mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.
Challenges in Business Analytics
The biggest challenge in implementing a business analytics solution is obtaining a high-quality data. Data quality management is a hard problem. Data integration is another area where companies will encounter challenges. This is because business data is often stored in various systems across a department or entire organization.