The Art and Importance of Business Intelligence
There is a lot of misunderstanding about what Business Intelligence (BI) is and what sets it apart from data science or even simple data analysis.
When you ask a different person, you will frequently receive a different response. Database administrators may argue that business intelligence is more focused on data, while analysts may argue that it is more focused on analysis and dashboards.
What exactly does “Business Intelligence” refer to? A fundamental explanation
Permit me to begin by defining what Business Intelligence is in a single, easy-to-understand line, and then I’ll proceed to elaborate on the specifics of what it consists of in greater detail. Therefore, in its most basic form…
The practice of performing an analysis of business data in order to assist in making more informed business decisions is referred to as business intelligence (BI).
Now, I purposefully penned that statement in such a way that makes it as easy as possible to identify business intelligence (BI) from other fields such as data science or just general data analysis. I did this so that it would be as clear and concise as possible. The answer can be found right there in the phrase “business intelligence.”
In contrast to the practice of data science, which entails the testing of hypotheses and the making of discoveries using data that can be of any nature at all (this could, of course, include data pertaining to businesses), the primary objective of business intelligence is to improve the intelligence of businesses through the use of data.
Let’s add some flesh to the skeleton of what we now know about business intelligence by expanding on the first, more basic line…
The method or practice of making use of the information that is produced by a company as a result of all of the different activities that it engages in is what is meant by the term “business intelligence.”
The data is then compiled, analyzed, and presented in dashboards and reports so that the information can be comprehended on a deeper level and useful insights into an organization’s operations can be gained.
These insights can then be applied to the process of making data-driven decisions in order to assist the company in developing new strategies and expanding its operations.
To summarize, the three stages of BI are as follows:
- Make use of the information.
- Perform some analysis and data visualization.
- Make better choices through the application of your insights.
The process of business intelligence is one that never ends.
The process must be repeated. This is something that needs to be kept in mind because business intelligence is an ongoing activity. You don’t just perform a single analysis on the data and then settle on a single set of choices based on the insights you’ve gained from that.
After the initial decision or set of initial decisions has been made and put into action, it will be necessary to evaluate the effect that these decisions have had on the performance of the company.
It’s possible that this will need you to add new data visualizations (charts, graphs, tables, etc.) to your existing dashboards.
It is perhaps possible that this will need the creation of an entirely new dashboard.
Or, the findings of the data analysis can present you with a whole fresh set of questions that you had not previously considered. Or perhaps new KPIs to monitor, which will require access to a greater variety of data sources.
The most effective business intelligence software is adaptable and always undergoing development so that it can keep pace with the ever-shifting needs of the company that uses it.
After providing a high-level explanation of what Business Intelligence is, the next step is to delve a little deeper into each of the three components of the BI process, which are outlined above.
Because of this, we will have a better understanding of some of the factors that led to the development of business intelligence over a previous couple of decades as well as the functions that it performs.
What exactly is meant by the term “data” when referring to BI?
The majority of enterprises operating in the contemporary global economy produce a HUGE amount of data. The days are long gone when everything was stored in spreadsheets and databases that were stored on the premises.
Things like social media and commercial services hosted in the cloud are examples of what the internet age has brought with it. And each one of them produces a massive amount of useful data.
However, this is precisely where the greatest obstacle that business intelligence seeks to conquer lies.
Because all of this information is stored in what are known as silos,’ it is difficult to obtain an all-encompassing view of how well your company is doing across all of its operations without first consulting a number of reports compiled in a variety of locations.
In addition, it makes the process of combining data from several distinct sources quite difficult.
To have a better understanding of the situation, what we actually need to do is consolidate all of these different data sources into one place and analyze them jointly.
Because we need to be able to mix data from many sources in order to answer questions such as “what influence have my recent social media initiatives had on sales?” we need to be able to combine the data from your sales with the data from your social media.
What is a Data Silo?
An independent source of data that is not connected to any other sources of data or to a centralized data repository is referred to as a data silo.
Basically, the majority of organizations produce a massive amount of data in a variety of locations as a result of all of the numerous activities they engage in. From basic Excel files and on-premise databases to social media accounts and cloud-based SaaS (Software-as-a-Service) platforms, there are many different types of data that can be stored.
It’s also possible to have multiple data silos within a single organization, each corresponding to a distinct department. It’s possible that different departments within an organization, such as sales, marketing, and finance, each use their own unique internal systems to collect data and store it in a manner that’s distinct from the other departments’ data.
One of the fundamental tenets of business intelligence is the concept of “harnessing the data from these different data silos and, either using tools to query the data where it is or transferring that data from there to a centralized repository where it can be analyzed together in one place.”
Read this post for a list of other terminology related to business intelligence that everyone ought to be familiar with.
How to perform data analysis and representation using Business Intelligence
The final outcome of the data analysis phase of the process is typically a dashboard (or report) that contains graphical representations of your aggregated data. This dashboard (or report) is the end product of the data analysis phase.
It will very often, but not always, contain elements of interactivity that will allow the dashboard viewer to ask questions about the data that is being presented by doing things like applying filters, or changing the metrics or dimensions that are contained in the visualizations. However, it will very often contain elements of interactivity that will allow the dashboard viewer to ask questions about the data that is being presented.
Dashboards got their name from their functional resemblance to the instrument panels found in conventional motor vehicles.
A business intelligence (BI) dashboard, on the other hand, contains your company’s metrics or KEY PERFORMANCE INDICATORS (KPIs), such as your revenue, stock levels, or the amount of engagement you’re getting on social media. A car dashboard, on the other hand, displays information such as the speed you’re traveling at, the amount of fuel that’s left in the tank, or even the temperature outside.
You’ll be able to gauge how well your company is doing by using dashboards to monitor and compare your key performance indicators (KPIs). When you visualize your data, it becomes much simpler to read, process, and understand the information.
A dashboard will provide you with an overview of your performance at a glance and will assist you in determining the areas in which you are excelling as well as those in which you may want additional focus or inquiry.
The practice of data visualization is a distinct field unto itself, which is something I will discuss in greater depth in this article. Effective visualization is essential to ensuring that your data is as easy to comprehend as is practically possible.
How to extract useful information from business data
Therefore, once all of our data has been compiled in one location, analyzed, and represented visually, we will go on to the insights stage. At this point, it will be time to examine the findings of the analysis and determine what, if anything, has been discovered.
When data is visualized correctly, it may help you see trends, show how different metrics connect with one another, identify outliers or unanticipated abnormalities in the data, and compare different time periods to observe how things are changing over time.
When it comes to tracking and benchmarking performance, the comparisons of different time periods, in which one is able to observe straightforward percentage increases or reductions, are the most helpful.
Conclusion
The conclusion is as follows. You should now hopefully have a better understanding of what business intelligence is, as well as the problems and rewards associated with it.
BI refers to a procedure. And it is not the case that data is more important than data visualization or any other component of that process.
When it comes to helping organizations become more intelligent through the use of data, each step of the process is just as crucial as the next.
