Nowadays, companies generate a great volume of data through their ERP systems, supply chain networks, stakeholders, sales platforms, and many others. However, such data is only as valuable as the insights it provides. To effectively integrate this information into the decision-making heartbeat of an organization, owners often adopt business intelligence (BI) strategies.
What Is Business Intelligence (BI)?
Business intelligence is an umbrella term that encompasses a wide variety of methods for collecting, storing, and analyzing data from business operations. In an organization, BI allows managers to move from simply having raw information to reaching valuable business intelligence insights. The idea is to gain an accurate and deep understanding of a business. In this way, a company can apply the available knowledge and skills to predict and prescribe its performance.
Although there are various business intelligence strategies to choose from, we can broadly differentiate between qualitative and quantitative BI approaches.
Qualitative BI Analysis
A typical qualitative BI approach to business intelligence involves a thorough evaluation of a firm’s risks and opportunities. This is a diagnostic and descriptive analysis that examines the current environment and identifies various risks and opportunities for the business.
Suppose that European authorities decide to increase the tax on plastics. For a bottling company like Coca-Cola European Partners, this would be both – a risk and an opportunity. While higher plastic packaging tax does affect sales price, it may as well drive the company towards embracing sustainability. As soon as the Risk and Opportunity Report is ready, an organization can analyze the information relevant for them. In the end, all they want to do is mitigate risks and exploit opportunities.
Once you have generated a qualitative analysis, you would try to quantify the risks and opportunities you’ve identified. In Coca-Cola’s case, a higher tax will increase the selling price of the product, since it is typically the customer who pays added costs. In turn, this may result in lower sales for the company. That’s how Coca-Cola European Partners might quantify the magnitude of the lower sales.
On the other hand, the tax situation will inevitably stimulate companies to look for alternative, more environmentally friendly, packages. So, the risk of higher taxes may turn into an opportunity for improving recycling practices. In the quantitative analysis to follow, the company will then estimate the additional investments required and the impact of these investments on their future sales.
Quantitative BI Analysis
A quantitative BI analysis helps us obtain business intelligence insights by stepping on the underlying data to draw meaningful conclusions and recommendations.
Let’s consider the increased tax on plastics example once more. We’ll assume that Coca-Cola European Partners applies a simplified predictive analytical tool to translate this situation into financial metrics. Based on the predictive model, they expect that the sales of plastic products will decrease, so the company must be ready for implementing such a change.
The bottom line is that they need to avoid plastic – that’s what all this tax increase is all about. In any way, the management can’t adjust production patterns overnight. Besides, some customers still prefer plastic bottles – after all, they are lighter and easier to carry than glass containers, and we certainly don’t want to lose these buyers.
Based on the underlying information, what would Coca-Cola’s strategy be?
We can conclude that the company needs to come up with the appropriate package for the appropriate occasion. So, their strategy may consist of promoting glass package for home usage and plastic package for usage on the go. They can combine this with a strong message to customers to recycle but also publicize our commitment to using more recycled content. With this business intelligence strategy, our predictive model shows that the lower sales of plastics will be compensated by increased sales of glass packages.
Enterprise Performance Management (EPM)
In the example above, we used the underlying data to obtain business intelligence insights and came up with an intelligent strategy. More specifically, we used analytical tools to understand our business. This is known as Enterprise Performance Management (EPM).
Technically speaking, EPM is a set of software capabilities that enables companies to create, manage, and deliver financial processes and data without having to maintain lengthy spreadsheets. As the name suggests, it “manages” the performance of an enterprise.
EPM’s goal is to analyze the underlying effectiveness and efficiency of a firm’s operations. The insights you get from EPM help you build your business plan, strategy, and highlight the risks and opportunities for your company.
Many insurance, manufacturing, and retail companies around the globe adopt enterprise performance management systems to establish supreme efficiency and accessibility to data.
Business Intelligence: Next Steps
In practice, analytics tools like enterprise performance management give you better insights into a company’s performance. Unlike analysis, analytics looks beyond simply describing what has happened in your business – it examines end-to-end processes and gives you the business knowledge necessary to make better decisions. It is forward-looking, robust, and mostly cloud-based!
Do you want to learn more about business analytics techniques and processes?
