How BI & Business Analytics complement each other in helping organisations make data-driven decisions?

Introduction:

Introduction to BI: Business Intelligence is abbreviated as BI which is defined as gathering, collecting, and analyzing data that aims to support organizations in making wise decisions. It entails gathering data from numerous sources, turning it into valuable information, and then presenting it in a way that decision-makers can easily understand. Key performance indicators (KPIs) and other metrics are often explained via tools like dashboards, reports, and scorecards.

Introduction to BA: In order to find patterns and trends in data, BA, a more sophisticated variant of BI, employs statistical and quantitative analytic methods. In order to find insights that might not be immediately evident using conventional BI methods, BA entails leveraging technologies like data mining, predictive analytics, and machine learning algorithms. The use of BA enables firms to uncover opportunities and hazards that may be concealed within the data, as well as to predict and forecast future trends more accurately.

Differences between BI and Business Analytics:

1. Focus: The study of historical data to find trends, patterns, and insights that might assist organizations in making wise decisions is the main goal of business intelligence (BI). It often entails gathering data from several sources, interpreting it, and presenting it in an approachable manner. The focus of BA, on the other hand, is on the application of sophisticated statistical and predictive modeling tools to find patterns and trends in data and anticipate future outcomes.
2. Techniques: Basic data analysis techniques including reporting, dashboards, and scorecards are frequently used in business intelligence (BI). These methods are intended to give a comprehensive picture of organizational performance and assist decision-makers in pinpointing areas that need improvement. BA, on the other hand, analyses data and makes predictions using more sophisticated statistical methods including data mining, predictive analytics, and machine learning algorithms.
3. Scope: Data from all areas of an organization, such as sales, marketing, finance, and operations, are often analyzed using business intelligence (BI). Its main goal is to give a comprehensive picture of organizational performance. Contrarily, BA is more narrowly focused and can be used to examine data within a particular sector of the organization, such as supply chain management or customer behavior.
4. Timeframe: Historical data analysis and performance insights are often the main goals of BI. BA, on the other hand, is more future-oriented and concentrates on forecasting outcomes based on recent data and patterns.

BA can be used to complement BI in several ways:

1. Discover new insights: BA can assist in spotting patterns and trends in the data that may not be immediately obvious using conventional BI techniques. Strategic decisions can then be influenced by these insights.
2. Predictive analytics: Organisations can predict future trends and spot potential possibilities and hazards with the aid of BA approaches like this one. This can be especially helpful in fields like finance, where making correct forecasts can have a big impact on how well a company does.
3. Real-time analysis: BA can offer real-time analysis of data, enabling businesses to react fast to market developments or other outside influences.
4. Deeper insights: BA can offer deeper insights into particular company domains, such as supply chain management or consumer behavior.

BI can be used to complement BA  in several ways:

1. Data preparation: Data from many sources can be cleaned, transformed, and integrated using BI to get it ready for analysis. This can enhance the quality of BA’s analysis by ensuring that the data is correct, complete, and consistent.
2. Data visualization: To communicate insights and conclusions from BA, BI offers a variety of data visualization tools, including charts, graphs, and dashboards. Decision-makers can use these visuals to better comprehend the data and make more educated choices.
3. Historical Analysis: BI can offer historical information that can be used to support BA analysis. 
4. Performance monitoring: Key performance indicators (KPIs) and business goal progress can both be monitored using BI. This can assist businesses in identifying opportunities for development and assisting them in making better resource allocation choices.
5. Collaboration: By offering a standard platform for data sharing and analysis, BI may help teams inside a company work together more effectively. Ensuring that everyone is using the same data and insights, helps enhance decision-making throughout the organization.

Example of How BI and BA complement each other in helping organizations to make data-driven decisions : 

BI offers a historical view of the past performance of the company, while BA leverages that historical data to forecast the future. For instance, BI can demonstrate that sales of a given product peaked at a particular season of the year. Therefore, based on that data, BA may project future sales of that product to rise within the same time period and advise the company to step up production or marketing efforts accordingly.

Customer data serves as another illustration of how BI and BA may collaborate. Customer retention rates may have been declining over the past five years, according to BI. After analyzing customer behavior and determining the causes of client attrition, BA might use that data. Based on this study, BA can suggest modifying the company’s client retention strategy.

Overall, by giving insights into past data and forecasting future patterns, BI and BA collaborate to support organizations in making data-driven decisions. Operations may be optimized, customer happiness can be raised, and growth can be stimulated using this data. Organizations can develop a more thorough understanding of their business and make better decisions by combining these strategies.