Business Intelligence vs. Business Analytics

businessintelligence

Introduction

Business Intelligence, (acronym: BI), sometimes called dashboarding, is part of a broader area referred to as Business Analytics. Business Analytics, aside from Business Intelligence, includes the utilization of practices, skills, and technologies such as Management Information Systems, Predictive Modeling, and Big Data. Popular software in the area of Business Intelligence includes Microsoft Power BI. We suggest a wonderful Microsoft Power BI Tutorial in the software section of this article.

Benefits 

Business Intelligence/Dashboarding can be thought of as the highest form of an automated Management Information System. One benefit of dashboarding is that data is delivered in real-time. A second benefit is that it allows anyone to visualize and analyze data with greater speed, efficiency, and understanding. Finally, there is no more need to rely on an analyst.

Disadvantages 

The main disadvantage is that while dashboarding can tell you what is happening now, it gives us no way to predict what will happen in the near future. This is where Predictive Modeling and Big Data come into play.

Business Intelligence Software

Microsoft provides software called ‘Microsoft Power BI’. https://powerbi.microsoft.com/en-us/what-is-power-bi/ Their description states “Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports.”

Power Bi Tutorial:

power bi tutorial
power bi tutorial

SAS provides a product called ‘SAS Visual Analytics’. https://www.sas.com/en_ca/software/visual-analytics.html One description of the product from their site states “Interactive reporting. Visual discovery. Self-service analytics. Scalability and governance. All from a single, powerful in-memory environment. “

Predictive Modeling and Big Data

Predictive Modeling can tell us what’s likely to happen in the near future. We can make predictions by utilizing historical data. Predictive Modeling allows us to make predictions at a more granular level, (ie which customer will buy the insurance), this is typically what distinguishes it from forecasting which considers a ‘higher’ level (ie how many passengers are expected to travel in the next month). The type of algorithm for your modeling will depend  on your dependent variable (if it is continuous or classification). Logistic Regression is a common algorithm used for non-continuous response/outcome variable.

Big Data refers to the volume, velocity, and variety of data. If you work at a company that has large volumes of data, new data is constantly flowing in, and you have a huge variety of data (videos, images, comments, etc), this context requires special infrastructure, tools, and techniques to make sense of. 

Conclusion

The business analytics practices, skills, and technologies required depend on your circumstance. The overall goal for any business is to increase value without increasing complexity too greatly.  Business Intelligence is somewhere in the middle of the value/complexity scale, which makes it ideal for most companies. If you are looking for a career in this area or to gain a promotion , learning Power BI through a Power BI Tutorial makes a ton of sense. Predictive Modeling, and Big Data especially, are more complex solutions but also bring a lot more value to a business. 

 

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