Business Analytics
How Does This Apply To Business?
Can you understand the factors that are causing your business success or failure rather than just the factors that are associated with your business success or failure? If so, it’s much more likely that you will be able to predict the future in your marketplace and act accordingly. However, it’s important to note that you need to know what’s correlated with something before you can know causation.
In other words, you need to know what happened and how it happened (BI) before you have the ability to say why things happened (BA) with any reasonable degree of certainty.
That is the difference between business intelligence and analytics, and that’s why both of them are crucial. They fit together like two pieces of a jigsaw puzzle – a puzzle that helps your business to be more profitable. It is of crucial importance to define and use KPI examples that will help to establish a business goal and execute the correlation and causation of business analytics vs business intelligence. While it may sound complicated at the beginning, the more you dig deeper with a data analysis tool, the more sense it will make to establish qualified insights and make better decisions. That is all about: the difference between business intelligence and business analytics is important to understand because it helps to prepare a company for adjusting its operations into a cost-effective and insightful way. Using both into the process of creating a successful business intelligence strategy, will only make a company more competitive on the market.
Use-Case Scenarios
Enough with the descriptions and metaphors. Let’s solidify things and wrap up this post with business examples, illustrating the difference between business intelligence and business analytics.
Let’s say you work for a marketing firm that uses both business intelligence and analytics to help large e-commerce companies launch new products. In order to understand what new products would be most likely to succeed (analytics), you would need to figure out:
What products had been most successful in the past (BI)
The seasonal trends that had influenced success for past launches (BI)
Why customers bought the past successful products (BA)
For example, let’s say that your hypothetical e-commerce store sold boutique women’s fashion. You will need to work with your retail analytics to understand what products will work.
First, you would examine what categories of clothing are driving the most profits. Then, you can examine what times in the year those successful products had been launched. Finally, you could do a series of in-depth customer interviews in order to figure out why customers liked those pieces or categories more than the others.
If you did enough market research, and you had a large enough sample size, you should be able to predict with a great deal of accuracy which new products would be likely to succeed.
This could lead to surprises in the way that you think about your products because your customers often have a very different way of looking at your products than you do.
BI and Analytics Dismantle Assumptions
For example, maybe your assumption was that your customers mainly cared about the price point of your garments.
After your research, however, you found your customers were actually willing to spend more on your products if you emphasized your humane sourcing practices, such as not utilizing sweatshops.
Then, your focus would be on continuing to use that positioning in your marketing messages as opposed to worrying about the price points of your garments so much when doing a product launch.
The above example illustrates one of the fundamental important points of business intelligence and analytics. Your assumptions about your company, your customers, your marketplace, and your products, are often flat out wrong – or at the very least, incomplete. After asking the right questions, analytics are here to help – whichever your industry or sector, be it healthcare analytics or financial business intelligence, you need to use both BI and BA for success.
Business Intelligence And Analytics Industry Examples
It's quite clear that the difference between the both can be examined through real-life examples, so let's analyze few industries that can show the value of both terms.
Human Resources: What are my recruiting options?
In Human Resources it’s all about workforce: engagement of employees, overtime hours, training costs, the overall productivity, cost per hire, recruiting conversion rate, time to fill a position, retention efficiency, part-time employees, etc. When you establish the right HR KPIs for your business, you need to dig deeper into the what happened and how (BI), and then why it happened (BA), to understand how to perform in the future. Let’s have a simple look into one full-scale dashboard and see how business analytics vs business intelligence performs.
This online dashboard above is created for a simple, yet effective overview of the recruiting process in a company. It can be used by a recruiting agency, or in-house – it is meant for HR managers and professionals that need more data and insight into their process, to define future decisions and decrease costs. The goal is to find the right recruitment approach, giving you the best candidates at the lowest cost. To put it in practice, you want to define what kind of process and what happened during the recruiting process, alongside how it happened (BI), and the next question would be why it happened the way you see it on this dashboard (business analytics). Let’s say that the average time to fill a position (by a department, in days) didn’t go as planned. You can inspect more and see that the conversion rate of the recruiting professionals didn’t go as expected, and you have lost precious time and resources to keep up with the market (you have used your historical data and connected it to the present moment – found out that you are losing resources). The average costs of hiring will help you determine the patterns that are occurring as a part of the recruiting cycle. By grasping these data with an online data visualization tool, the amount of time needed to gain those insights will be reduced and could be used in other business processes.
In this example, we can define what happened, how, and then why. Don‘t be afraid to do your own analysis and create your own HR report that will help you showcase the power of business intelligence vs analytics. This is important since you want to know and define the influences on your operations to consider future undertakings; you want to know what happened, how it happened and why. This is the holistic formula of a successful business.
But let's dig deeper into other industries.
Procurement: Is it possible to outperform my supply delivery process?
Another example that we can show you to better see what is business intelligence and analytics, and then you can also explore additionally by yourself, is the procurement dashboard, expounding on the supplier delivery performance. As mentioned before, after you establish your indicators, in this case, procurement KPIs, you can dig deeper into the analysis of the business processes to establish a better performance and decide on future company aspects of success. Business intelligence analytics is often used together (even in the wording), which can help you to get a holistic overview, like in the dashboard presented above. It doesn’t mean it cannot be used separately, but to make better decisions, you need the best tools you can utilize in this competitive market. That being said, business intelligence vs analytics can show the mentioned correlations and causations that will provide an extensive value to the general business operations and future reasoning of important decisions.
With our last example, we will wrap up what business intelligence analytics can do for a company and how to use it. The advantages are clear, but what about the indispensable features a simple visual overview can provide you with? Using your raw data and assembling a visual representation of all your important performance, historical and present intelligence, you can create a powerful insight tool that will gather and connect the most significant acumen a business needs to manage their small, mid, and big-scale operations, while making balanced decisions and creating a sustainable process. Let’s see this through an example.
Sales: How to decrease the sales cycle lenght?
The sales dashboard visualized above reflects on the sales cycle needed to perform the complete process – from potential opportunity to a paid invoice. While compiling the historical data (calculating the average in a define time-preset), with the present insights and trends that are occurring in the sales process, we can dig deeper into the BI of the cycle. We can examine the sales funnel (which can, also, be customized by the particular needs of a business or department), what were the trends and patterns happening during the sales funnel stage, how it affected the complete sales cycle, and who were the top performing representatives from the team. By drilling down the productivity, outperforming processes, the ones that have the less amount of efficiency, a company can easily spot what is working and what is not. If you see the average sales cycle length of 18 days, but your benchmarks are telling you that it should be no more than 15 days, then tackling deeper into the BI angle of conducting research can give you the answer where those 3 days are underperforming. This will give an extra edge for the next sales cycle, as you can easily pinpoint what is the issue, and brainstorm solutions.
By detailing the factors that caused these insights (in plain language, why something happened), adding predictive analytics and examining the, already mentioned, why of these processes, a business can utilize the business analytics point of view – that will help to gather the interconnected data into a comprehensive data-story. If you tackle into the raw sets of data, and leverage the power of statistics to predict the future of your performance, then you have taken the advantage of the entire sequence of the business intelligence vs data analytics sphere.