In today’s technological landscape, businesses are more data-dependent, with vast pools of data at their disposal. What to do with it and how to make sense of it all is a challenge we attempt to solve with data science—starting with data discovery and ending with analytics that aid in critical business decision making.
Now, there are numerous ways to extract meaning from data. Buzzwords like business intelligence and advanced analytics—though quite crucial for every enterprise—can easily be misunderstood in their role in making sense of data.
For example, what exactly is advanced analytics? Is it the same thing as machine learning? And where does artificial intelligence come into play? In this article, we’ll answer those questions and more by discussing the difference between business intelligence vs advanced analytics—two strategies that are similar in nature, but with some key differences.
A showdown of analytical strategies
When comparing advanced analytics to business intelligence, one thing is clear: both approaches provide answers. The question is, how different are those answers? Let’s look at those differences in detail in the infographic below:
Business intelligence is an information provider. It offers hindsight, giving users information about past metrics and visibility into performance. To illustrate: companies can use BI to track real-time performance metrics much faster than they otherwise could, improving operational efficiency in the process.
Advanced analytics, on the other hand, offers foresight. It’s a troubleshooter, anticipating future business challenges and behavior to prescribe actions to maximize beneficial outcomes. For example, when a manufacturing professional forecasts an unplanned downtime for machinery, that’s advanced analytics. In this case, they are provided with a clear prescription on the next steps of the decision-making process.
What’s the common thread? Both attempt to uncover information that would support critical business decision making.
BI answers the question “what happened?”, historically and operationally. It also provides answers to “why did it happen?”, allowing users to dive deep into data and get visibility into what might be causing the problem.
Users can also leverage advanced analytics to find answers to questions like “what will happen?” and “what can we do to make it happen?”. It offers a more comprehensive “why” and “how” to the “what” of BI.
What’s the common thread? When comparing advanced analytics vs. business intelligence, it can’t be denied that both approaches still provide analysis that can help drive business outcomes.
“Together, we can fuel an industry culture of joint innovation in which no risk is missed, and no opportunity is left behind.”
Business intelligence utilizes the more traditional reporting and visualization methods as a form of analysis: dashboards and scorecards, multi-dimensional analysis, automated monitoring and alerts, and more. These processes are employed to gather data from different sources and deliver it in a clean, easy-to-digest format for reporting or monitoring purposes.
For advanced analytics, more sophisticated quantitative methods are used to discover trends and find patterns. Think predictive analytics, data mining, simulations, and machine learning algorithms such as regression and decision trees. When people mention artificial intelligence in the realm of data science, they usually refer to advanced analytics.
What’s the common thread? Both techniques still belong to the overarching field of data science, with roots in mathematics and statistics.
When it comes to business intelligence, users often come up with a solution in a reactive manner. Without visibility into what might occur, problems are usually dealt with as or after they arrive. Given the limited capabilities of BI, context is often lacking too.
Advanced analytics offers the functionality to anticipate future issues, which is why problem-solving is more proactive and pre-emptive. With the ability to uncover new correlations, patterns, and opportunities, solutions are often more dynamic and comprehensive too—eliminating guesswork in the process.
What’s the common thread? Both approaches still give weight to quality, each requiring sound solutions that can address a business challenge.
Business intelligence systems are usually designed to handle highly structured data and only some forms of unstructured data. But they can work with different data sources with no problem, through a traditional data warehouse.
On the other hand, advanced analytics systems can work with unstructured and free-form data: social media comments, images, videos, and more. These systems can also handle high-speed, high-volume, and complex multi-structured data from a variety of sources. In fact, most kinds of data can be collected, cleansed, and prepared for analysis.
What’s the common thread? Both techniques can work with real-time data sources. Also, both provide answers that are only as effective as the data being used. Erroneous data will still produce erroneous results.
After a business intelligence project is completed, the result is usually predefined and in highly formatted reports—dashboards, pivot tables, and the like. The goal is to analyze historical performance, so users usually go back to these reports to extract information.
As for an advanced analytics project, the output is usually a machine learning algorithm, built and trained to find hidden relationships between factors and their outcome and come up with forecasts.
What’s the common thread? For both approaches, reporting still plays a huge role. After a machine learning model is built, the data scientist still relies on reporting to visualize the output and ensure that models are performing as intended.
Both make a powerful team
In a nutshell, business intelligence tells a story about what the data is saying, and advanced analytics takes it a step further, uncovering patterns that will give insight into what might come next and what should be done about it.
So, on one hand, you’re asking, “who defaulted on their credit card and loans last quarter?” and on the other, the question is “who is likely to default on their credit card and loans in the near future?”
Still, both approaches are integral to data science. Both strategies take data and transform it into insights. Both are used to inform decision making for better business outcomes.
Instead of choosing the best approach, it’s time for companies to understand that both business intelligence and advanced analytics are critical to analytical projects. If you are considering investing in a data science platform, opt for one that offers both the capabilities to realize the full potential of these technologies. With Analance, you can visualize data in dashboards and drill through for further insights, but you can also brilliantly complement your efforts with advanced analytics and machine learning to solve greater challenges and grow to new heights.