Examples Of Business Intelligence Solutions – If someone put you on the spot, would you tell him/her the difference between business intelligence and analytics? If you’re feeling a little unsure about the specifics here, you’re not alone, the experts disagree too! There is no clear line between business intelligence and analytics, but they are extremely related and intertwined in their approach to solving business problems, providing insights into past and present data, and defining future solutions. While some experts try to emphasize that BA also focuses on predictive modeling and advanced statistics to assess what will happen in the future, BI is more focused on the present moment of data, making decisions based on current insights. But let’s take a closer look at what the experts say and how we can relate and differentiate the two.
We have already seen earlier this year the benefits of Business Intelligence and Business Analytics. Now let’s dig deeper and find out what they are all about, what makes them different and how they complement each other.
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In an article dealing with BI and business analytics, Better Buys asked seven different BI professionals what they thought about the difference between business intelligence and analytics. Every professional had a different opinion. Here are some excerpts from their comments:
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“BI looks in the rearview mirror and uses historical data. BA looks ahead of you to see what will happen.
“What is the difference between Business Analytics and Business Intelligence?” The correct answer is: everyone has an opinion, but no one knows and you shouldn’t care.
Well, what if you are interested in the difference between business intelligence and data analysis? It doesn’t matter if you run a small business or an enterprise, if you have to make decisions that will affect you in the short or long term, it makes sense to use both. BI and BA will provide an organization with a holistic view of raw data and make decisions more successful and cost-effective.
Business intelligence and analytics are data management solutions implemented in companies and enterprises to collect historical and current data, while using statistics and software to analyze raw information and provide insights for making better future decisions.
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Let’s face it: both terms provide insight into business operation and future decisions, but it comes down to differences in how they do it and exactly what information they provide.
It seems clear that there is no standard “correct” definition of the differences between the two terms. The different opinions of the experts are proof of this. So instead of trying to find the “right” answer, let’s find a useful distinction between the two that can be used simply and clearly to help you in your work. The clearest and most useful difference between business intelligence and data analysis comes down to two factors:
Keeping in mind that this is all a matter of opinion, here are our simplified definitions of business intelligence vs. business analytics.
Happened up to now. It identifies big trends and patterns without going too deep into them
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However, BI and BA have different applications. On the one hand, BI tools can help organizations identify relevant trends as well as explain various past scenarios. On the other hand, BA deals with more advanced applications such as predictive analytics and statistical modeling. This also allows the two terms to complement each other to provide a complete picture of the data. By using Business Intelligence and Analytics (ABI) tools, companies can get the full potential out of their analytics efforts and make improved, fact-based decisions.
Are you still confused? Let’s use an example from football as a metaphor to clarify everything we just mentioned.
Let’s say you’re on the coaching staff of a soccer team and you want to review the last game. You do this to see how you can correct your mistakes and replicate your successes.
Using our previous definitions, BI would be the process of identifying all the stats and plays that led to your team winning. This would identify that you have possessed the ball much longer than your opponents. This would also identify the trend that your right side of the field has been instrumental in retaining possession through excellent passing.
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You had the ball longer than your opponent and why your right side of the field did so well with passing.
These questions are important. They allow you to understand how you can replicate your success or prevent your failure afterwards. Asking the right business intelligence questions will lead you to better analysis. While using a business dashboard, all your information can be simplified in one place, making the time to make meaningful decisions much faster. But first, we need to analyze the difference more, as this will help us understand what to do in the company’s work process and how to choose the best tool to manage your insights.
As mentioned in the introduction of this post, there is no clear distinction between BI and BA. While the two terms can often be used interchangeably, there are several elements that distinguish them. Let’s look at each of them separately.
The first difference between the two concepts is in the actual definition. While both serve a purpose in the analytic process, they do not serve the same purpose. A major factor of differentiation is in the method each of them uses as a basis. While BI tells you what happened in the past and what is happening now (descriptive analytics), BA tells you what will happen in the future (predictive analytics). Let’s see a conceptual definition of both.
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Usage is another factor that can help us understand how BI and BA differ from each other. In this case, not only the end user changes, but also the purpose of use. As we have mentioned time and again, BI tools are used to report on the current and past performance of the organization, BA tools allow you to take a step forward by helping you decide on your next steps. Let’s see it with an example.
Imagine you own an online shoe store. One day you look at your sales report and notice that sales of a pair of red shoes have increased in recent weeks in New York. This information is provided by your BI tool and lets you know that you need to produce more red shoes to meet demand. Now BA can help you understand why sales just jumped in New York. By digging deeper into your website data, you see that most of the traffic is coming from a New York blogger who wears your red shoes. Thus, you decide to send some other successful shoe designs to other bloggers around the country and prepare production based on historical demand.
The end user is another factor to consider. Most BI software on the market is self-service. Which means that anyone who doesn’t need technical skills can use them, making the BI analysis process much more intuitive and easy to implement. On the other hand, BA is more technical. It relies on mathematical models, machine learning and artificial intelligence technologies to make accurate predictions, making them more difficult for the average user without prior skills to use. However, this has been changing in recent years as new tools have emerged that allow users to perform advanced analysis with just a few clicks.
Our third and final differentiator is in the app. Your data is used differently depending on whether you perform BI or BA analysis. While BI organizes information into easy-to-understand reports, BA takes it a bit further than reporting. Let’s look at the applications of each of them.
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In the next part of this post, we will look at BI and BA from a business perspective with use cases and examples, but first we need to explore the distinction between correlation and causation.
When two things are correlated, it means that when one happens, the other tends to happen at the same time. When two things are causally related, it means that one directly or indirectly causes the other to happen.
A famous example of the difference between the two is the fact that ice cream consumption and homicide rates in the city are highly correlated. Now, of course, ice cream doesn’t make people kill each other. So clearly there is no causality.
The two are linked due to the fact that homicide rates rise as temperatures rise in late summer. It is theorized that as warmer weather brings more people outside, it leads to more social interactions, some of which are violent.
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Everywhere you look you can find examples of people confusing correlation and causation. For example, that muscular guy at the gym who always likes to give you workout tips may or may not actually know what they’re talking about. The advice they give you, though related to being known by a muscle man,
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