The business world runs on data. Information is plentiful, and making the best use of the data companies collect will usually lead to sustained success. When talking about data in today’s business world, a number of terms will typically pop up. Among the most common data science terms, you’ll find business intelligence and data analytics. You’ve likely run across these terms yourself. Many organizations are using business intelligence and analytics within their processes, but how many truly know what these terms mean? The world of big data, after all, is a relatively recent development. Understanding both of them is important, so let’s take a look at how they are similar and what differentiates them.
What Are Business Intelligence and Data Analytics?
We’ll begin with a look at the basic definition of both of these terms. According to Forrester, business intelligence (BI) is “a set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.”
The definition of data analytics is “the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software.”
Similarities Between Business Intelligence and Data Analytics
While the differences that separate business intelligence and data analytics are good to know, it’s also important to understand their similarities. By doing so, organizations can know how each can provide benefits in similar ways.
As can be expected, both BI and data analytics follow similar processes of collecting data, analyzing it, and providing insights. The data collection step in particular is crucial as providing the best results will mean making sure that the information gathered is complete and free from errors.
Both of these terms also engage in reporting. This means that the data is organized and presented in such a way that allows it to be visualized. While raw numbers are important, once data becomes visual does it really start to demonstrate value, making insights easier to discover and act upon.
Business intelligence and data analytics can also identify areas where businesses are failing or at the least not operating at peak efficiency. In other words, they use the data they collect to show where pain points are, giving organizations a better view of where they may be falling short.
Business Intelligence vs Data Analytics
With those similarities noted, it’s time to take a closer look at the difference between BI and analytics. Once the differences are understood, businesses can determine how best to use the two to reach their goals and desired outcomes.
While both BI and data analytics involve using data to discover insights that will benefit the organization, there is one major difference to touch on. Put in simple terms, business intelligence deals with the present, while data analytics is more focused on the future. Let’s explore this idea further.
A focus of business intelligence is to take data and use it for better decision making. Through the use of aggregation, visualization, and careful analysis, companies can use BI to achieve better efficiency in how the organization is operating now. From data collected and analyzed, a business may figure out how to better sell to customers or provide improved incentives for employees. All the actions derived from business intelligence can be undertaken in the moment. Need your company to improve right away? Business intelligence tools can be employed for that very purpose. That’s not to say BI doesn’t have a role to play in future decision making, but the emphasis is on getting things done now. Another way of putting this is that BI engages in descriptive analytics, essentially providing a summary of historical data and placing it in a visualized form so companies can act on it.
Data analytics, on the other hand, places emphasis on the future. Data analytics engages in data mining, essentially analyzing a set of information to pick out patterns and predict future trends that can inform organizations as to what they should do. This is most commonly referred to as predictive analytics wherein predictions are made purely based on data. One can quickly see how valuable this can be to any organization out there. Think about how helpful it can be to accurately predict where a sales trend is going or where new markets may open up. With this information in hand, businesses can set themselves up for the future. Basically, business intelligence sets up the game plan for an organization to enact right away while data analytics tells an organization how to plan for the years ahead.
That’s not where the difference between the two ends, of course. Business intelligence has more of a history, with the phrase first appearing in a book back in 1865 in reference to a banker who earned more money thanks to an in-depth analysis of the surrounding business environment. The term hasn’t strayed far from that initial description, although the complexity has only increased as time has passed. Data analytics is certainly a more recently coined term, but it may be older than you think. It gained a lot in popularity in the 1960s at about the time computers were becoming more commonplace. Like business intelligence, it has become more complex as big data has transformed into a major component within the business world.
If there’s one area where business intelligence can really set itself apart from data analytics, it would be in its accessibility. BI tools come in many different types, and most are designed in a way that a wide user base can take advantage of them. Even if someone doesn’t have a lot of experience with the intricacies of data, they can still utilize BI tools effectively. Not only that, but business intelligence is geared around taking the complex and turning it into something simple. A spreadsheet filled with numbers and statistics could easily become overwhelming, but BI takes the insights derived from an analysis of the numbers and transforms it visually into something that almost anyone can understand. This is especially helpful if a team within an organization wants to communicate their findings to the executives who may not have extensive knowledge in what the team has expertise.
Data analytics is something that tends to be more complex and harder to understand except for those with experience in the field. There’s a clear emphasis on developing and using algorithms to discover hidden insights from the vast sets of data. That also means there’s a huge variety of data to comb through, from social media statistics to logistical information. It’s from this insight that prescriptive analytics can occur wherein solutions can be determined and implemented.
In the discussion over business intelligence vs analytics, there’s a lot of ground to cover. This has only scratched the surface of what each can do and what makes them different, but if there’s one thing to learn, it’s that both can play vital roles in a company’s business strategy. Business intelligence, for example, can be used to answer questions about organizational operations, while data analytics may be used to enhance data security, ensuring the data used is safe and protected. When working in tandem, you get the best out of both worlds. Take advantage of their strengths and your business will be better off for it.
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