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How to Leverage Predictive Analytics in Your Business Intelligence Solutions

 


By: Reuben Yonatan, CEO and Founder of GetVoIP

GetVoIP is a trusted VoIP comparison resource the helps companies understand and choose a business communication solution for their specific needs. Reuben helps SMBs align business strategy with culture and improve overall corporate infrastructure. Follow him on Twitter @ReubenYonatan

 

The term business intelligence, also commonly referred to as BI for short, describes all of the technologies, the processes and the procedures that go into integrating, analyzing, and presenting the massive volumes of data that your organization (and its customers) are creating on a daily basis.

Data on its own is largely meaningless. It's little more than a series of 1s and 0s on a hard drive somewhere. But with the right business intelligence solution at the heart of it all - One that allows you to analyze and take control of your data - there is suddenly a way to uncover the valuable story hidden underneath, just waiting to be told.

On their own, business intelligence solutions are geared towards providing you insight into what has already happened or is happening right now. With the addition of predictive analytics, however, you suddenly become an expert in what happens next.

Indeed, there is a wide range of different ways that organizations like yours can leverage predictive analytics in your business intelligence solutions that are worth exploring. Let’s explore what they are and see how you can use them to your advantage.

 

What Are Predictive Analytics?

Before you can learn how to better leverage predictive analytics in your business intelligence solutions, it's important to come to an understanding about what this concept is actually describing in the first place.

At its core, predictive analytics is the idea that you can use a combination of data, statistical algorithms and machine learning techniques to put yourself in a better position to identify the probability of future outcomes based on historical information.

Essentially, you're taking the lessons that you've learned in the past and are applying them to better predict the future.

Predictive analytics are used in a variety of different ways in nearly every industry that you can think of. For example, cybersecurity experts use them to help detect fraud or cyber-attacks while they are still in their early stages, thus giving them a better chance to do something about it before the problem grows too large. Predictive analytics are used to analyze all of the information going on across a network in real-time, aiding researchers in their effort to spot the types of suspicious activity that could lead to intrusions and other instances of fraud faster than ever.

Other companies use them to optimize their omni-channel marketing campaigns to reach their own audiences in a more effective way. Based on past customer purchases or responses to certain types of marketing collateral, predictive analytics can be a way to do everything, from creating better, more informed collateral in the future, to helping promote opportunities, to assisting in cross-selling or up-selling to existing customers.

What predictive analytics are NOT, however, is a silver bullet that is guaranteed to make an organization better, stronger or faster. As the old saying goes, "There is no fate but that which we make for ourselves." Predictive analytics are not a guaranteed way to confirm that something is going to happen. Instead, it's a way to give yourself access to better, more informed insights based on what has already happened to better prepare for certain things that are likely to occur down the road.

 

Putting Predictive Analytics to Work For You 

When it comes to the major nexus between predictive analytics and business intelligence, it's important to keep in mind that your goal is less about predicting the future in a literal sense and is more about putting yourself in a better position to achieve desired outcomes by leveraging past behaviors to your advantage.

Case in point: many companies use predictive analytics to gain insights into potential future trends. You see this happen in the electronics industry all the time. Based on market research data about what people are buying, what they're responding to, what they like and what they don't like, you begin to get a better idea of where an entire industry might be headed. This puts certain savvy companies in a position to get in on the ground floor of something poised to be the "next big thing."

In other cases, discovering a potential future trend is just a question of applying what we learned from our trend as it behaves across different seasons and mixing that with the most recent behavior of the trend. Together you can create a forecast that can take into account the seasonality of your business and your most recent performance so you can better plan ahead. Applying that technique in a Business Intelligence tool such as Dundas BI may look like this:

 

Figure 1 – Exponential Smoothing in Dundas BI

Another example commonly used in a variety of industries has to do with how companies leverage predictive analytics to recognize inefficient business practices. A warehouse, for example, might uncover new insights into how their resource allocation (think equipment) is affecting their ability to load or unload trucks on a regular basis. Or, predictive analytics might be used to show that when X, Y and Z conditions occur, trucks have to wait in a loading dock before they receive attention for far too long, thus severely harming the overall productivity level of the entire warehouse. With this knowledge, you could simply correct X, Y and Z conditions to achieve the desired outcome - turning over more trucks in a faster way, thus cutting costs and increasing efficiency at the same time. One common predictive analytics algorithm to be used in this scenario is a clustering algorithm that can help identify those conditions.

Software companies are another example of this type of idea in motion. Many use predictive analytics to help improve the original value proposition that their products are offering in the first place. This in turn provides users with advanced proof that they can do what they say they can, thus creating a unique competitive advantage (and likely decreasing that company's overall time-to-market as well). For example, Netflix improved its services through predictive analytics by having the software determine what movies and shows would fit a customer’s list based on what they’ve previously watched, how often they watch certain shows and movies, and how often movies are rated and recommended to others.

Regardless of how predictive analytics are leveraged in your current business intelligence solutions, the end result is almost always the same. Someone in a planning position can use the past to better predict what next week's schedule might look like, helping them mitigate risk from gaps in coverage or sudden capacity influxes. Resources in the field can be better allocated to help get employees where they need to be at exactly the right moment. Even representatives in a call center could use past appointments to predict future ones, thus reaching out to customers who may be on the fence and offering an appointment with a high probability of success.

The end result is the same: you're left with a better, faster and more efficient organization that offers better, faster and more enjoyable service to customers. At the end of the day, that truly is the most important goal of all.

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