Business Intelligence vs. Business Analytics
Business Intelligence vs. business analytics: These terms just seem to flow together into the mess of technical ‘mumbo jumbo’ that we hear from software salespeople. It sounds impressive, but what is business intelligence and analytics? What does it all mean, and what impact can ‘business intelligence’ and ‘business analytics’ have on a real business?
That’s a valid question, and in this article, we’ll explain the basics (defining business intelligence and analytics in simple language) and then discuss how all this could be relevant to you and your business.
Let’s start with definitions…
Business Intelligence is the gathering of data about your business. It may come from internal sources (sales reports, WIP levels, incoming orders, forecasts, profitability, etc.). This is key, solid information about your business that you know and helps give you perspective on how to interpret new information. There may be outside data that you also use – perhaps you’ve seen a correlation between your business and certain stock indices, or tracking the price of oil since that relates directly to your business. All of this is helpful information, and makes immediate sense.
Business analytics is the application of mathematics and statistics to use your business intelligence data in a different way – extrapolating the data to be predictive. This typically means developing sophisticated algorithms that were reserved only for the largest and most deep-pocketed organizations 20 years ago. Today, with the advent of ‘big data’, and the power of computers, it’s much easier for smaller organizations to meaningfully benefit from analyzing huge amounts of information and drawing relevant predictions from it all.
What is business intelligence and analytics in ERP?
Ok, now that we know the definitions of business intelligence vs. business analytics, how do we set ourselves up to take advantage of this information?
Traditionally, business computer systems have ‘tables’ of data. Customer information is in a table; manufactured products are in another table, vendors are in a third table etc. These tables are connected so that information can be drawn from multiple places when it’s relevant to a single question. How profitable is my customer ‘x’. That question would be answered by linking the customer to the products they purchase, looking up the costing information, and then displaying it in a report or dashboard. (A dashboard is just a way of displaying information on a screen and being able to manipulate it in real-time, while a report is just output that may be on paper).
Either way you use it, the information is relevant and answers your question.
The next question might be much more strategic. For instance, if my top 10 customers contribute 90% of my profitability, and my bottom 100 customers I lose money on, then why do I have those bottom 100 customers? If I had all profitable customers, I’d be much better off….make sense? We all know it’s not quite that simple, but it’s worth evaluating. How do we profile those customers that are so much more profitable? There is information about them that isn’t in our database, and we’d also want to know what industries they are in and whether those industries are growing or shrinking. If our best customers are in a shrinking market, then we’re in trouble!!
We may decide to collect more information about our customers (more tables) and connect the information in ways we hadn’t thought of before. Of course, we’re not entirely sure whether this will work….and we wouldn’t want to permanently set up our data structure, not knowing if it makes sense. So, what do we do?
Now comes the idea of ‘data sets’ or ‘data cubes’. We export our data into a ‘cube’ that allows us to link it to other data in different ways. We do our analysis, and if it doesn’t work, we can erase it and start over. We haven’t damaged our source data.
We would want to apply statistics to our data in a cube to see trends, and sometimes there are unexpected trends that we stumble on that can yield critical strategic information. Now we’ve arrived at ‘business analytics’ – the ability to predict the future based on internal and external information from business intelligence.
ERP systems are organized to work efficiently inside the business and are largely transaction-based. Some, like Epicor Kinetic, have their approach to help companies bridge the gap between ‘business intelligence’ and ‘business analytics.’ Epicor calls it ‘Epicor Data Analytics’ or ‘EDA.’ Epicor houses the models for EDA within the database, so everything is correctly set up to be utilized and updated in real-time. Outside packages do this sort of work, but it’s much easier when it’s all contained within the same system.
Contact EpiCenter ERP Today to Learn How Business Intelligence and Analytics can work with your ERP System!
So, if you are in the market for a new ERP system, pay attention to the transactions and ask what they have for business analytics. Business analytics is emerging as a critical way for smaller organizations to use their information for strategic decision-making realistically. We hope you’ll look to EpiCenter as a resource to help you define your needs and provide trusted advice as you navigate your way through various choices. Contact us today to get in touch with our ERP and business intelligence experts!
written by Jeffrey W. Glaze, President