Distinguishing Between Business Intelligence, Business Analytics, and Data Science

Business intelligence and analytics tools are essential for any business, and data science is critical for data-rich enterprises.  Is your organization utilizing these technologies? This post will lay out the benefits of each one and help you decide what the next step is for your business.

Business Intelligence Keeps Track of Business Data

Business Intelligence has changed a lot over the years, but it focuses on determining the state of your business and how it got there.  Using business intelligence tools, you can answer questions like “What was our growth within the last year?” or “What were sales for this product in May?”  If you have a business question that starts with “what?”, business intelligence tools are the best way to find the answer.

Business intelligence software provides a system that can store, query, and report on data collected from other software used by your business. Top-rated Business Intelligence tools like Tableau and Qlik Sense enable analysts to ascertain the state of an organization and create visualizations to display their findings. 

Business Analytics Explains Business Data

Business analytics focuses on using business data to extrapolate factors that have led the business to its current state.  Additionally, business analytics also make predictions about organizations. Using business analytics, users can answer questions like “Why did sales spike in May?”.  Whenever you have a business question that starts with “Why?”, business analytics tools are a great way to find the answer.

Business analytics features are often packaged in with business intelligence tools.  Software like TIBCO Spotfire and SAP Crystal are top-rated business intelligence/analytics tools that allow analysts to perform predictive analytics in addition to creating visualizations and reporting on data. 

Data Science Makes Large Scale Predictions

Data science is a broad category that functions similarly to business analytics but on a larger scale.  Data science is about making predictions for the future of an organization based on historical business data.  The primary difference between data science and business analytics is that data science is typically used to handle large amounts of unstructured data, such as customer data for a retail chain.  Data scientists use this data to create predictive models for business statistics such as customer churn. 

Data science platforms, such as Matlab and IBM Watson Studio, store and query vast amounts of data.  In isolation, this data may be less specific than the data used by business analytics software, so data science platforms rely on the scale of data to make predictions.  While many business intelligence and analytics tools are usable for non-technical staff, data science platforms require scripting and model building from analysts to get the most value out of them.

Next Steps For Your Business

Business intelligence and analytics tools are useful for almost any organization as they enable users to understand the state of an organization and make predictions.  If your business generates data, business intelligence tools will help you use that data to create value. 

Data science tools are more useful to businesses that generate a large amount of unstructured data and have analysts on staff to create predictive models.  Large enterprises that produce a lot of customer data and employ data analysts can get a lot of value out of data science platforms.  Small businesses that aren’t generating huge amounts of data may not benefit much from data science platforms but should continue to consider data science platforms as they grow.