The Basics of Business Intelligence

The Basics of Business Intelligence

Team Polyrific

Whether from a colleague or from marketing material extolling the virtues of a software platform that can help you understand your enterprise's big data, you have most likely heard the term "Business Intelligence". But do you really know what business intelligence (also referred to as "BI") is? Let's take a closer look.

What is business intelligence?

As with other industry buzzwords such as "big data" and "machine learning", the term "business intelligence" can mean different things to different people. BI has been referred to as a set of methodologies for understanding data, an umbrella term that refers to applications, infrastructure and tools which enable analysis of information, and a set of concepts to improve business decisions. While all of these definitions hit the general area of the matter, we like to think of BI as the GPS of executive decision making.

These days we use our GPS units so often that we almost forget they are there but stop and think for just a moment how truly amazing these devices are. Our world contains hundreds of millions of possible destinations and multiple routes to each. It contains traffic patterns that may become better or worse depending on the time of day, whether there is ongoing construction along the route, or whether there has been an accident along the route. Today's GPS units can take all of this into consideration instantly and provide you with intelligent guidance to the nearest grocery store if you ask it to do so. We take it for granted, but today's GPS and navigation technology would be very hard to do without in our modern world.

You can think of business intelligence as the GPS of enterprise landscape, helping to guide executives and decision makers to important destinations (conclusions) while taking into account important factors--sales data, customer sentiment, geographical performance, stocked materials, etc.--when calculating the route. Business Intelligence can take the form of a simple data warehouse that combines enterprise data from various resources into a single source of truth for executive reporting, or it can be a collection of data visualizations that help to highlight data trends that might not be noticed when presented in tabular form. 

No matter how it surfaces in your company, the GPS metaphor holds true: business intelligence tools are like a GPS, they help guide you to a desired destination while considering all of the factors that will influence your path along the way.

Why is business intelligence important?

We can also use the GPS analogy to illustrate why business intelligence is important to today's enterprises. Picture what your hometown must have been like 100 years ago, before the time of GPS. If it existed at that time at all, your hometown probably didn't have too many destinations to navigate or routes to take them there. GPS technology was a long way off at the time but that was okay because there wasn't really necessary. If you needed to go to the general store to purchase necessities, there was probably only one place you'd visit located in the center of town. Easy. Likewise, business of that age didn't have very much data to manage. They may have kept a ledger to keep track of customer accounts, but there wasn't even a concept of tracking things like customer buying habits, supplier lead times, consumer sentiment and so forth. It would take only a matter of minutes for the owner of the general store to look at his ledger, determine who is behind on paying their tab, and then make a sound decision about whether to extend those customers credit in the future.

Today things are different. If you ask you GPS to guide you to a dry cleaner you may be presented with a dozen different choices which may change from one day to the next depending on traffic. Navigating enterprise decision making has become just as complex. Not only are enterprises swimming in an ocean of their own data, but they must also consider an ocean of external data and contributing factors such as seasonality, federal reserve rate changes, and a torrent of data beginning to pour in from the smart devices that comprise the Internet of Things. Without the guidance of a solid business intelligence program, executives and decision makers are likely to get lost in the forest thanks to the trees. 

Ready to get started with business intelligence?

A solid business intelligence program is more than any one tool, it is a combination of tools and integration points. There are a few great platforms out there, but you will need some help in choosing the ones that will work well for your enterprise and then tying them together in a meaningful way that offers the best possible guidance for your own particular environment. 

At Polyrific, we are experts in creating enterprise solutions that conform to the way you do business. If you'd like our help in helping you to design a successful business intelligence program, please contact us today!

Team Polyrific | Apr 10, 2017

Machine learning is a system of algorithms aimed at detecting patterns in big data and then learning from those patterns without being explicitly programmed by a human operator. These algorithms take a probabilistic, rather than a deterministic, approach to accomplishing goals. Let's take a quick look at what that means by way of example:

Deterministic Approach

A human programs software in no uncertain terms to remind him to bring his umbrella to work if the chance of precipitation in his area is greater than 40%:

//Get the weather forecast
var chanceOfRain = myWeatherForecast().precipitationChance;

//Send the message
if(chanceOfRain > 40%){
    sendEmail("Bring your umbrella to work!");

This approach is deterministic because the outcome is predetermined by the the author of the code in no uncertain terms.

Probabilistic (Machine Learning) Approach

An algorithm crawls big datasets such as tweets about the weather, weather news, forecasts, umbrella sales, and supervised feedback (we'll get to that later) such as, "did you bring an umbrella to work today?":

machine learning

As you can now see, Machine Learning is about statistics and probability. Whereas humans typically write algorithms with a predetermined output for a given set of a few inputs, machine learning algorithms consume a vast amount of inputs and surface the most likely output. Over time, machine learning algorithms get better by adjusting their own internal methods to predict outcomes based on a growing body of data or knowledge. Eventually, a machine learning algorithm can become better at predicting outcomes than can its human counterparts because human beings, and deterministic algorithms written by humans, can not factor the massive quantities of inputs than machine learning algorithms easily digest.

Machine learning is nothing new

Believe it or not, the early concepts of machine learning and statistical analysis were proposed by British mathematician Alan Turing more than sixty years ago. So why is machine learning just now becoming so popular? There are several factors for this but chiefly it is because processors are more powerful, data storage is incredibly cheap, and thanks to the Internet, everything is now connected. In other words, it is now common for the phone in your pocket to display to you a message such as "bring your umbrella" as it detects you are leaving your home for the day as a result of a server to which it is connected crunching potentially millions of connected data points. Even more impressive, this happens in less than a second and it is all thanks to machine learning.

You use it every day

You might not have realized it, but you already benefit from machine learning every day. Here are a few of the common areas in which machine learning is making our daily lives better:

Search Results

Think that Google and Bing get your search results simply by how many times your search term is displayed on a given web page? Think again. Such a deterministic approach would never help you wade through the universe of irrelevant information to get to what you actually need. Search engines use complicated machine learning algorithms that factor information such as your current location, the time of day, your recent searches, the page you last visited, your recent purchases, and so on. This probabilistic approach usually surfaces what you were probably after within the top ten results on your page. A deterministic approach would never succeed in doing the same.

Spam Filters

Every time you classify an email as "spam" you are training a machine learning algorithm to recognize similar emails as having a high-degree of probability of being spam.

Recommendation Engines

Netflix, Amazon, and even Spotify use your searches and feedback as fodder for machine learning engines that in turn offer you better recommendations.

News Feeds

You may thank that your Facebook feed is already full of posts that don't hold your interest, but this would be even worse without machine learning which is at least promoting to the top of your feed people and topics you seem to care about the most.


You may have already heard of Google Translate which helps you translate text from one language to another but it gets even better: Microsoft recently capitalized on machine learning algorithms to translate spoken Skype conversations in real time. This means you can literally talk to someone in english while they hear Mandarin and vice versa.

Fraud Detection

Ever had your credit card declined when you are travelling or making a large purchase? Machine learning algorithms help credit card companies detect events that seem out of character for you and decline the card to prevent fraudulent use.

Will machine learning replace humans in the workplace?

Probably, but but it should be considered as good news because it will promote productivity and creativity. In some cases, machine learning will augment workforces wherein there are currently shortages of skilled workers. 

Consider the example of a general practice physician. Currently, you will need to schedule an appointment with your GP when you have a medical issue and, once you get in to see the doctor, you have to carefully explain your symptoms and answer the doctor's questions. Drawing on her previous experience the doctor may be able to make a diagnosis, but if she happens to not have prior experience or knowledge about your particular situation, then she may refer you to a specialist or send you to a lab for tests. 

Now consider a digital doctor which is trained by vast quantities of shared data and experience. At any given time, this digital doctor may be processing--and learning from--millions of cases. This doctor becomes more experienced and better at making correct diagnoses by the second. So when this doctor interviews you and makes a diagnosis, it is drawing on millions upon millions of past cases, symptoms, and outcomes in order to let you know what is ailing you in a matter of seconds. There is not likely a need to go for further tests or a need to go see a specialist.

Human doctors can use such a digital doctor to augment, rather than replace, their care of you. Now they can receive a report and know immediately whether your appointment should be expedited, or whether they can simply write a prescription for you to come pick up.

Get started!

Machine learning is a productivity multiplier. You can think of it as a way to unload cognitive tasks to the computer so you can free your staff's minds to focus on higher value work. Machine learning is no longer the property of big tech companies alone, we can all now capitalize on the amazing things it can accomplish. Contact us today to learn more about how machine learning can benefit your business.