The terms business intelligence and advanced analytics are often deployed as reference terms for business procedures designed to derive insight from operational data. And while that definition is generally true for both terms, if we drill down a little further, we’ll see that there are key differences between business intelligence and advanced analytics in both theory and practice.
Incorrect usage could lead to incorrect application and unintended consequences!
Business intelligence and advanced analytics: What’s the difference?
We can safely say that business intelligence and advanced analytics are both data-oriented management techniques that businesses of any size — from local food carts to global beverage manufacturers — can leverage to improve their operations.
The best way to understand distinctions between the two terms, though, is to think about the different questions they answer.
- Business intelligence is generally used to explain why something happened in the past.
- Advanced analytics is generally used to explain why something is happening in the present and what that will mean if trends continue.
In practice, the distinction isn’t always well defined, but the past-versus-future framing is a rule of thumb for understanding how you can utilize these two techniques for your own operations.
Business intelligence (BI) traditionally focuses on the use of (mostly) structured data to analyze past performance, manage day-to-day operations, and guide planning for the near future. Companies will leverage business intelligence tools when they want to collect and store data about current operations, maximize workflows, and meet their current business benchmarks.
Business intelligence tools include everything from simple spreadsheets to sophisticated systems for online analytical processing, business activity monitoring, and data mining software.
A robust business intelligence system should provide you with comprehensive business metrics, in real time (or close to it), with data and reports structured to answer specific questions about your operations, including:
- What happened in the past and how can it be explained?
- How many deviations from expected results have there been?
- How often have anomalies occurred and how costly have they been?
You can use this information to support better decision making and navigate organizational and industry-related challenges. But BI also presents a distinct set of challenges.
Working with data from different sources: Business data is often siloed across a range of databases, from customer relationship management (CRM) systems to enterprise resource planning (ERP) software to assorted Excel spreadsheets. Consolidating the information you need can be a difficult, time-consuming task.
Identifying the right indicators: Financial KPIs are a good start, but business intelligence should provide a more holistic overview of your operations—which means you need to monitor and record a diverse set of indicators that capture everything from marketing to inventory management to equipment performance.
Developing sufficient technical skills: Business intelligence applications can have a steep learning curve, and without enough training users may find their programs are delivering uneven results and minimal ROI.
These problems aren’t insurmountable, but they do suggest that establishing an effective business intelligence system for your entire company will require careful planning and at least some degree of technical support.
We’ve established that business intelligence provides an insightful summary of past and current data. But advanced analytics (AA) goes a step further by using sophisticated modeling techniques to predict future events or discover patterns that can’t be detected otherwise.
While business intelligence is focused on reporting and querying, advanced analytics is about optimizing, correlating, and predicting the next best action or the next most likely action.
Another important distinction is the type of data employed. BI data is typically structured data that can be captured using consistent metrics. Advanced analytics data includes structured data as well, but also unstructured data (like videos, photos, and other media files, internet-of-things devices, and web data) that requires transformation before it can be analyzed.
Advanced analytics tools can process this data and then perform numerous functions, including correlational analysis, regression analysis, forecasting analysis, text mining, image analytics, pattern matching, cluster analysis, multivariate statistics, and more.
This process can be used to answer specific business questions including:
- Why is this happening?
- What if these trends continue?
- What will happen next?
- What is the best that can happen?
Having answers to questions like these presents a huge competitive advantage! So why isn’t every company utilizing advanced analytics? In part, because the operational challenges of AA can be substantial BI. For instance:
Upfront costs: Managing large-scale data can require substantial expenses, whether you’re working an on-premise solution or something cloud based. The personnel needs (staff and developers) can represent a substantial investment.
Accumulating unstructured data from diverse sources: As mentioned, advanced analytics can unlock value by processing unstructured data. But getting these data “cleaned” and integrated into your system is no easy task.
Sharing and collaborating: Security and logistical concerns can hamper the flow of data necessary for advanced analytics. This problem is only compounded for remote and distributed teams.
Not many organizations will possess the internal expertise necessary to recognize and address their analytics challenges, but with solid strategy and guidance these obstacles can easily be overcome.
Why is advanced analytics so important?
The volume of business data continues to grow exponentially. In industries like manufacturing, this data is being generated through millions of sensors, connected devices, payment systems, cameras, and other Internet of Things (IoT) applications. While many companies already use and operationalize business intelligence applications within their processes to leverage data, the true potential of data is still untouched in many organizations.
Advanced analytics—and particularly predictive analytics can help you unlock the true value of your data by providing extremely accurate forecasts that incorporate changing variables and hypothetical circumstances.
Common advanced analytics techniques
Advanced analytics utilizes a wide variety of techniques drawn from statistical methods algorithms, as well as computer science to identify relationships and forecast trends.
Specific methods and applications include:
Descriptive modelingDescriptive modeling captures a range of techniques that attempt to summarize or categorize data by identifying relevant features and identifying relationships between them. It’s especially useful for understanding how complex systems operate.
Predictive analyticsPredictive analytics allows you to use both current and historical data—often mined from CRM and ERP systems, marketing automation stacks, and other databases—to detect demand trends and predict future outcomes, based on supplied parameters.
Simulation optimizationPerhaps the most sophisticated method, simulation optimization allows you to find the best possible outcomes based on assumptions about key variables. Though complicated, simulation optimization is absolutely invaluable for evaluating dynamic systems like supply chains.
Multimedia analyticsDigital data increasingly includes images, video, and even audio. Multimedia analytics helps you process this data (which is often large-scale and thus unwieldy) and unlock value through pattern recognition and classification.
Choosing the right advanced analytics solution
With so many advanced analytics tools available, it can be difficult to single out one as the best fit for your business.
RapidMiner offers an entire set of tools that can effectively solve any number of business data problems. Our technology allows companies to use previously unexplored data to inform their decisions, optimize their processes, and take advantage of the highest level of business intelligence.
RapidMiner’s flagship product is a cutting-edge, open-source data analytics and machine learning platform. Hundreds of thousands of applications are already in use in more than 50 countries, both as stand-alone applications and engines integrated into customers’ own product.
Get started on your data mining project by downloading RapidMiner Studio for free today!
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