Why Do We Need Business Intelligence and Analytics?
How do I keep my business alive? How can I compete with other businesses or companies? How can I continue to attract new customers? How can I convince my previous customers to stay? What do I do now?
These thoughts sound like a problem regarding operational strategies. The reality is that, undeniably, the business world goes by its inherent competitive order. In as much as introducing your business to the market setting requires so much planning to attract potential customers (e.g. choosing your unique trademark), exposure to the market does not assure you of business stability. In fact, keeping up with the demanding nature of business is one of the greatest challenges an entrepreneur faces. Indeed, maintenance is key in business management. However, the competition may get too suffocating that you end up facing a dead-end. But worry not! Business Intelligence and Analytics were born from such cries for help.
Business Intelligence and Analytics is a process-based problem solver centered on data analysis and application in order to overcome the challenges involved with one’s needs when it comes to operations and management. Business Intelligence and Analytics provide a systematic thought process for the creation of tactical procedures that can help keep our businesses alive.
However, as both of these serve as instructional information devices with the purpose of guidance in decision-making, there often is confusion on the difference between the two. Some even believe that these are two interchangeable terms. Indeed, in the business setting, there is no clear depiction of the boundary between Business Intelligence and Business Analytics. Perhaps, the blur on the differences comes from their similar nature.
NATURE OF BUSINESS INTELLIGENCE AND BUSINESS ANALYTICS
As earlier defined, Business Intelligence and Business Analytics are helpers in times of critical decision-making process crisis (e.g., “How do I continue to operate my business?”). Business Intelligence and Analytics provide a systematic thought process that is heavily based on previous data and information from what the business has experienced and gathered. Having a similar general purpose, their overall nature is likewise more or less the same, which shall be further discussed as follows.
(What do these data mean?)
Business operation starts when an order of a product or service has been placed. The manufacturing and operational processes go through systematically until the order of a product or service has been realized. The number of orders received, how many resources are left in the inventory after order delivery, how much profit did a business gain — these are only some of the possible data that could be noted down. Using data organizers, the data collected can be summarized and are now easier to keep track of. The aforementioned processes are merely parts of the main transaction process from which vital data can usually be gathered. This whole setup then pertains to the system of data collection.
However, things do not stop there. Data interpretation entails asking, “What do these data mean?” From the numerical data gathered, what can we observe? With data organizers, observations can be made easier. Which month had the most number of sales? Which item is more likely to be bought? The enumerated questions then show that data interpretation can mostly revolve around these statistical concepts: measures of central tendency and measures of variability.
Data interpretation focuses on the measures of central tendency which are: mean, median, and mode. In the case of a mean, which typically pertains to an average, the interpretation drawn from this concept may be, “The daily average sales from October are X value.” In another scenario, using a mode, which typically refers to value occurs most in a set, one can draw a data interpretation such as, “The most popular item that is frequently bought by the customers is from the Z brand.”
On the other hand, the interpretation of data focused on the measures of variability is talking about how dispersed the data is from the average data. So, these are adopting the concepts of range, variance, and standard deviation. Interpretations like, “The sales in October are relatively lower by about 8% than the rest of the nine preceding months.”
Furthermore, from the numerous individual data we had from the data organizer, we can draw some interpretations by using simple and common concepts in statistics. Contrary to what common misconceptions portray, data interpretation is not just about the reporting of the individual raw data. It includes the process of carefully determining the vital information and being able to translate it into the business language. However, you do not just stop at describing the scenario your business is experiencing. It is also important to understand how these facts are going to affect your standing in the business setting. The data analysis then comes into the equation.
(What do we understand from the data interpretation?)
Data analysis is a form of data interpreting but at a higher level. From your interpreted data, one must be able to observe a pattern from the information. This is the shift from data processing to the understanding of your data.
The method of hypothesizing is one of the most fundamental methods involved in data analysis. Given different scenarios from your data interpretation, a hypothesis stated with the if-then pattern emerges. For example, given the previous scenario that the data interpretation is as follows: The sales in October are relatively lower by about 8% than the rest of the nine preceding months. The approach in the method of hypothesizing shall be, “Why are the sales in October relatively lower to other months?” This would then instigate the investigation on the probable causes of the decrease in sales. Let us say, for the sake of discussion, that during October, a variable X has been introduced. For instance, if they were selling donuts, the company decided to change from using natural sugar to artificial sweeteners. The change of sweetener could then become a factor of why there is a decrease in sales since changing the sweeteners might have caused the change of taste in the product.
However, people should note that hypothesizing which factors have affected a certain result is not necessarily always a case of a causal reaction. It is not always about cause and effect. It may sometimes be just merely correlation. Correlation determines the relationship between two variables: X and Y. Some people think that changes in the variable X cause changes in variable Y. However, this is not entirely true. Although changes in variable X may affect the changes in variable Y, other factors may have caused the changes in the variable. Correlation determines how strong does the variable X affect variable Y.
The hypothesized if-then statements shall then lead to the determination of some business patterns. These patterns make up the business trends. Business trends are the sum of all usually occurring patterns in your business operations and management. These business trends are what is given importance in the system of Business Intelligence and Analytics.
Business Intelligence and Analytics were dubbed as problem solvers. Both of these concepts help in the critical decision-making process involved with business management and operations. This instructional feature is mainly dependent on the nature of data analysis. However, Business Intelligence and Business Analytics have different focuses on data analysis. Business Intelligence focuses on historical trends while Business Analytics focuses on probable future trends. These differences shall be discussed further in another section of this article.
(How shall we deal with the data analysis?)
The nature of the application is the implementation of the testing of the hypothesis you have created from the data you have gathered. This is when strategizing the business operations and management takes place. As Business Intelligence and Analytics were previously defined, it was stated that these provide a systematic thought process for the creation of tactical procedures that can help keep our businesses alive. As aforementioned, maintenance is key in businesses. How will you manage your company to keep up with the competition? When a competitor emerges, what approach will you do? Will you maintain your business operations or will you consider a change in the operations? What will these changes do? If it does succeed in swaying new potential customers, is there any assurance that your previous customers will stay as they find the change suitable? These kinds of questions must be taken into consideration. To sum it all up, data application is simply your business strategy.
In the interpretation-analysis-application nature of Business Intelligence and Analytics, their differences are centered on the method of analysis and the way such analysis is applied.
Now that you know the similarities and differences between the overall nature of Business Intelligence and Analytics, read on to learn more about what sets each apart from the other.
BUSINESS INTELLIGENCE VS. BUSINESS ANALYTICS
Since Business Intelligence and Analytics are born from similar purposes, which is to help you determine the appropriate approach in strategizing your tactical business operations, its natures are more or less the same. However, Business Intelligence and Business Analytics are not synonymous and have vital differences, other than the different characteristics of their nature.
METHOD OF ANALYSIS
The data investigation method of analysis is a type of analysis that is centered on the idea of the past data gathered, interpreting these data, and figuring out your next step for your current operations. Therefore, there seems to be a significant focus on historical trends in this type of analysis. It revolves around the mindset that says, “This is what happened, so this is what we will and should do.” This method is focused on the past and present timeline of operations. The determination of what the next appropriate move on your operations will be is mainly based on the previous data gathered.
The data prediction method of analysis is a type of analysis that makes use of the data from the past to predict future trends. From then on, there arises a discussion on figuring out what shall be the appropriate step for the foreseen circumstances. It revolves around the mindset, “Because this is what happened, this is what will probably happen, and this is what we will do in case it does happen.” This method is focused on the past, and it promotes the prediction of the future timeline of your operations. Determining what the next appropriate move will be is a total suggestion for improvement or change that is heavily based on the hypothesized future circumstances.
Next step on current real-time operations
Since Business Intelligence is focused on the past-present timeline, any application for the determined best move for your operations will only dictate the next step to your current operations. This is important in a short-term crisis that needs immediate attention. The golden and critical time for strategizing your tactical operations shall be saved by the features of Business Intelligence.
Changing gears for another operational tactic
As a method focused on the past-future timeline of operations, any application for the appropriate move based on the probable circumstances predicted dictates a change for another operational tactic. This is focused on a long-term visionary that needs careful thinking.
WHAT ARE THE FACTORS INVOLVED IN CHOOSING BETWEEN THE TWO FACTORS (BUSINESS INTELLIGENCE VS. BUSINESS ANALYTICS)? IS THERE SUCH A THING AS GUARANTEED SUPERIOR APPROACH?
The decision-making process involved with selecting which approach to choose is one of the most critical steps in determining how you should manage your business operations. Indeed, both Business Intelligence and Business Analytics have their own advantages and disadvantages. However, maximizing its supreme instructional features is heavily based on the compatibility of your approach to your business needs. If you want a change in your business, you cannot just simply choose Business Intelligence since Business Analytics is the approach offering the shift on operational methods. Therefore, the existence of a guaranteed superior approach is purely superficial. The best approach to choose is the one that applies to your needs.
BUSINESS INTELLIGENCE AS A PREREQUISITE OF BUSINESS ANALYTICS?
Is Business Analytics a subset of Business Intelligence? Or is Business Intelligence a process of Business Analytics?
There has been an endless debate about the confusion of which approach is a subset of the other. Different proponents from the business setting have different answers. However, to have a systematized thought process in determining whether any of these claims are indeed factual, one must know the similarities and differences between the purposes of the two.
PURPOSE OF BUSINESS INTELLIGENCE
To describe, for Business Intelligence, is the method of data collection.
To explain in the field of Business Intelligence includes the method of data investigation analysis.
Controlling business operations is based on determining the new steps of the current operations for the current management.
PURPOSE OF BUSINESS ANALYTICS
To describe, for Business Intelligence, is the method of data collection.
The process of explaining in the field of Business Analytics includes the method of data prediction of analysis.
Predicting is a unique purpose of Business Analytics that sets it apart from Business Intelligence.
Controlling business operations is based on the suggestion of changes in your operations. Determining the appropriate steps for these changes is then based on the predetermined circumstances that are dubbed as the future trend.
To end the confusion of which approach is a subset of the other, it is more appropriate, then, to think of the two approaches as two sides of the same coin. The coin is the similarities in their nature, while the difference in their nature and purpose are the different sides of the coin.
In conclusion, Business Intelligence and Business Analytics are two concepts or factors that are similar in nature but different in purpose. Both of these, however, can work hand in hand to contribute to helping you achieve better business outcomes. You can also opt to utilize just one or the other in acquiring your desired goals.
If you wish to learn more about Business Intelligence and Business Analytics, as well as about how you can make the most out of these concepts for the betterment of your company and brand, then please do not hesitate to reach out to our experts at Proweaver, Inc. Our team is always ready to answer your questions and attend to your needs. We can provide you with what you have to know regarding key business strategies and other information that can help you achieve your goals.
Allow Proweaver, Inc. to be your partner in working toward business growth and success. We can help you figure out the workaround factors related to improving your business operations and management, most especially when it comes to digital marketing and online solutions.