By Brynne Henn - July 14, 2021
Today advanced analytics and business intelligence are at the forefront of decision making at the highest rungs of the business world. Although everyone’s endorsing machine learning and predictive analytics, the substance of what these systems do can be hard to grasp.
Within that confusion are companies and services that advertise as providers of the real-time upside of advanced analytics, but in fact are providing outdated and expensive BI tools.
Knowing the difference between them is critical for fixing a broken decision-making process.
Failing to grasp where traditional business intelligence ends and advanced analytics begins can undercut decision making, productivity, and evaluation. And it’s an easy mistake to make. BI and advanced analytics are both systems that leverage big data. They both rely on data mining and algorithms. And they’re both increasingly structured with artificial intelligence in mind.
Except legacy BI tools, for all their insights, don’t take full advantage of the cutting-edge abilities of machine learning. While legacy BI tools are useful for evaluating current and past performance, they don’t tackle the what-if scenarios of a business future, which is what advanced analytics can do. They use predictive analytic methods, such as statistical modeling and simulation, developed by data scientists to help stakeholders see around the curve.
Knowing the difference between BI and advanced analytics—and choosing the right data engine—will enhance decision making and find the untapped potential of your business.
All of those excel spreadsheets, invoices, and pie charts being gathered across every department of a business are technically BI, along with the reports, metrics, and visualizations that communicate changes in the data. Taken together, BI is the collective effort to draw actionable insights from the data an organization collects.
There are many types of decisions in business intelligence, but more often than not, BI tools are used to simplify decision making around improving a business and its processes. One ancient example of a BI tool, dating back to the 3rd century AD, was the familial rating systems of the Chinese Wei Dynasty. According to the KPI Institute, the algorithms of the Wei Dynasty can be considered one of the earliest-known examples of key performance indicators, one of the most commonly used metrics within business intelligence today.
Thanks to modern data science, new tools for BI and analytics are created all the time. But they all share a common purpose of simplifying BI decisions through a big data approach and historical data mining. The end goal is enhanced decision intelligence. For stakeholders, advanced analytics and AI provide data-driven recommendations on complex issues like resource allocation, allowing them to do more with limited resources.
BI tools for decision making are designed with business users in mind, with the end goal of informing stakeholders with data on complex decisions. A well thought-out BI system can create synchronized data collection in a way that provides accurate pictures of customer management patterns, team productivity, and other operational procedures. Dashboards, scorecards, and other automated visualizations are BI tools that have become ubiquitous in recent years.
Whereas BI is focused on the past and present, the field of advanced analytics is forward-looking, exploring possibilities for what’s ahead for a business.
In this proactive approach, advanced analytics can tackle the most complicated questions at the core of a business, such as: “What will happen next?” and “What if these trends continue?”
There are more and more branches to advanced analytics sprouting all the time, but the most recognizable category is predictive analytics. Many companies advertise predictive analytics solutions, but most of them rely on sophisticated statistical modeling and simulation.
Another major category is prescriptive analytics. These are analytics platforms that make recommendations for what stakeholders should—or could—do to anticipate, mitigate, or combat trends inside the company or externally. Whenever computer systems offer suggestions of paths forward, there is prescriptive analysis involved.
And thanks to recent progress in machine learning and AI, there are now analytics tools that can even make use of unstructured data, such as videos, photos, and information gathered from internet-of-things devices. Although it often involves a tedious process of cleaning up and reorganizing the data, advanced analytics tools are capable of combining structured and unstructured data to help businesses find a competitive advantage.
With the amount of data generated and collected these days, both BI and advanced analytics tools are necessary considerations for any modern data stack . But understanding how they augment each other can make or break whether you invest in one or both of these systems.
The fundamental difference between BI and advanced analytics tools is that the former is reactive to data, explaining what has already occurred in a business, while the latter is looking at data as evidence of what’s to come. Traditional BI systems are set up to run queries against the data, whereas many advanced analytics tools autonomously search for patterns and use machine learning to generate new insights on their own.
Diagnosing problems or getting to the bottom of unexplained jumps in productivity are mostly the realm of BI. Figuring out what-if scenarios and peering into the future is the task of advanced analytics.
Another key difference between them is accessibility. For the most part, BI tools are designed for all business users in mind. They are made for easy navigation, with color-coded visualizations and streamlined dashboards that stakeholders can easily consume. But advanced analytics are more complex, and, involving speculative modeling and projected variables, they often require the eyes of data scientists to make sure they’re being used responsibly.
Sisu allows analysts to forgo the time-intensive process of querying and manipulating data to run one analysis at a time like in a BI tool or dashboard. By leveraging AI and machine learning techniques, the Sisu engine starts with the metrics that matter to you and analyzes all your data in seconds to answer what changed and why.