Business intelligence, data analytics, and business analytics are some terms that are often confused with each other. People tend to interchangeably use these terms, and that is because they are probably not aware of the subtle differences between them. These small details are very important when it comes to the application of these data manipulation techniques.

In this blog, we will try to cover what these terms are, how are they different and how can you best utilize them for your business model.

Business Intelligence

The term business intelligence has been around for over a century, however, the means of employing it have been changed drastically. Today, in the age of the computer, there are tools, that can analyze different data sets. These business intelligence tools then present an insight into the state of business and what lead to this point. These tools primarily utilize the data from the past and look into different reports, statistics, and all kinds of data sets. Different visualization tools can be utilized to present a business intelligence report.

Data Analytics

Data analytics is another data manipulation technique, however, it deals with raw data in order to produce an insight into the trends that might not reveal themselves otherwise. It utilizes algorithms and software’s to present a conclusion about why a certain thing happened. Data analysts may deal with rather complex mathematical data and unprocessed information, they then apply different algorithms to cleanse this data and provide an understanding of it.

Business Intelligence and Data Analytics

While it may seem that both of these have something to do with data analysis and then presenting a conclusion that may help an organization, it is not that simple. Differences arise when you further inquire into how the process is taking place and what does the final reports indicate about the data set.

One thing that distinguishes them both is the conclusions drawn from the data provided to these models. Business intelligence utilizes the historic data to equip you with information into what lead to the present state of your organization. However, data analytics primarily focuses on the future and what is to come based on the data provided. Another way to put this difference is that business intelligence tells you what happened and data analytics tells you what will happen.

Business intelligence provides information that you can take action on right away. If you are having bad sales, or the company is losing money, business intelligence tools can pinpoint the factors that are playing a part in these problems. You can right away rectify these problems, or you can use business intelligence to identify a certain high trend and keep using the same strategies, that it presents you, for better business growth. You can use this tool to figure out what your clientele responds to and in turn act accordingly.

However, data analytics is used when you are looking to plan ahead. Data analytics incorporates data mining, it digs deep into the data to observe any certain patterns and trends in it. This equips the system to predict any future trend shifting that may benefit or harm the organization. This is known as the predictive analytics model. These predictions are based on the data set provided.

With the detailed evaluation of the past trends, the question of why a certain thing affected the organization, becomes clearer. Employing machine learning models, you can predict based on data things like, where a certain product may start selling more, or how one can tweak their product to better fit in a certain demographic. This way you can set a tactical plan for your business for the times to come.

Where does Business Analytics fit in?

Business analytics is sometimes referred to as a subset of business intelligence and sometimes people may say that it falls under the categories of data analytics. However, business analytics do have an identity of its own. It focuses on predicting what is to come if a certain trend is continued, or one can say it calculates the probabilities of future outcomes. The process involves machine learning and data mining to reach any credible conclusion.

This way we can say that it actually does fall under the categories of data analytics, however, it has a more focused approach. Business analytics is considered to be a prescriptive model. There is a subtle difference in predictive and prescriptive models. A predictive model tells you what is going to happen.

Its conclusion is based on certain trends in the data set. Albeit in a prescriptive model, based on the information extracted from the data set, it can tell you what variables you can change in order to achieve your desired results.

To understand this fine line between data analytics and business analytics let us present an example. If you run an online store and you want to look into the demographics of the traffic that comes through on your website, or if you want to look into the traffic to see if there is an underlying pattern or trend, you will make use of data analytics. This will predict and reveal these trends and equip you with the information that you can use to overcome any situation that may arise in the future.

When it comes to the system presenting you what changes you should make based on the prediction derived from data, that is the prescriptive model. So now if you want to know how you should update your website, based on the changing trends, you will use a prescriptive model.

When Should You Apply These Models?

Before we conclude all this information about these different models, let’s talk about their applications.

A business intelligence model can be referred to as a descriptive model, which means that it describes and provides detail into what happened. It can be utilized to keep on top of your current situation. It will keep you posted of any rise in sales in a certain area, or if a certain delay is occurring in the process. This way you can stay vigilant about your business model, and keep up with the current demand.

A data analytics model can be implemented when you want to look into the trends or any irregularities in your business model. You can use it to better inform yourself about any patterns that affect your business.

A business analytics model is to be put in place when you want to get ahead of the market. You can use the data insights, trends, and patterns, and then run them by business analytic models to see what consequence will your changes yield.

Conclusion

To conclude we would like to highlight that it is important to realize that these models are not substitutes for each other. They are all separate models with very distinct practical implications. All of these models can be utilized to reap the most benefit given the scenario. These all are crucial to your business growth and cannot be neglected or favored upon each other.

From knowing what happened to why it happened and what can you do about it to turn things in your favor, you need all these models implemented in your organization. For a successful and long term business strategy, data is your best bet, it depends on you how inclined you are to grow.