Business intelligence (BI) is evolving and is leaps and bounds ahead of where it was just a decade ago.
With the evolution comes the integration of machine learning.
What exactly is machine learning and how does it influence decision making in the modern business sphere?
Machine Learning Defined
Machine learning is basically a form of artificial intelligence capable of learning and implementing new input without being programmed to do so.
We actually see machine learning in our everyday lives; most people just don’t know it. When you see a recommendation list on your Amazon or Netflix account, for example, that is a form of machine learning.
Fraud detection in your online banking account is another example. Even autonomous, self-driving cars are a form of machine learning.
With machine learning, a system is able to self-change its behavior based on the data that comes in. This is different than a programmer simply writing code to get a system to act in a certain way.
Corporate giants like Google, IBM, and Microsoft all have its own projects devoted to machine learning. Countless other startups are also slowly entering the fray. In 2016, over $5 billion in venture investment has been devoted to this industry.
How It All Connects with BI
In the early days, BI software merely generated static reports based on raw data input.
These days, BI tools are far more sophisticated and come with interactive dashboards, real-time reporting, and various modes of complex visual storytelling.
Whereas decision makers previously had to spot the trends based on compiled reports, modern BI systems perform much of the predictive analytics.
Predictive analytics is the key term here with respects to machine learning’s influence on BI.
Predictive analytics is rapidly growing; one study estimated that this market will become a $3.6 billion industry by 2020 in the U.S. alone.
Predictive analytics is all about predicting the most probable behaviors and patterns by your consumers, or even your own staff if using a BI solution for HR use.
While the final decision is always up to human personnel, BI with predictive analytics and modeling can forecast the decision that would most likely lead to the best ROI, retention, etc.
Predictive Analytics at Work
How does predictive analytics work when put into actual practice? There are dozens of modes in which predictive analytics can be used.
In marketing and sales, for example, analytics can gauge various forms of customer data, both structured and unstructured, stored in data lakes and warehouses.
Decision makers in this instance can look at trends that go deeper than broad sales numbers.
This may include social media activity, the click-through rate of particular ads, sale numbers according to geolocation, etc.
Based on this data, staffers can make decisions accordingly. Some BI systems can even be set to automatically implement new ads or programs based on incoming data. It may, for instance, display multiple ads and swap them out according to customer click rate.
Fraud detection is another area where predictive analytics is heavily relied on.
Advanced BI systems can detect the likelihood of a stolen credit card, fraudulent tax return, or a false insurance claim based on past data from a particular consumer or group data that yields the factors of a high-risk offender.
Putting Machine Learning to Use
Machine learning indeed sounds promising, but how can companies make the concept work for them? The key is to make the latest BI software available to departments that may benefit. Support your talent with top-of-the-line productivity tools that automate much of the grunt and guess work.
Familiarity with BI tools is paramount; this can be achieved by making the system accessible through personal devices. BYOD work environments are already on the sharp incline, so now is the time to put this into motion if you’re not already an early adopter of the practice.
Another key for success is diversifying your data. For predictive analytics to be accurate and useful, it requires data mining from a diverse source of data. Companies will need to come up with new metrics. Ideas may include:
- Loyalty program retention rate based on geo-location AND income
- Click-through rate of ads based on ad type (e.g. type of longtail keywords, use of call-to-action, etc.)
- Trial offer sign up rate based on month, day of week, and time of day
- Site re-visit rate of consumers that reached a certain stage of the sales funnel
Notice that these metrics or very specific. Modern BI systems can derive data and predict trends based on well-honed metrics.
Machine learning is the way of modern BI. With it comes a mode of predictive analytics that is stunningly accurate. Best of all, machine learning is a concept that is available to SMBs and does not require a complex learning curve. The time for adoption is now.