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How to make the most of predictive analytics

We’ve been talking quite a bit in recent months about the topic of Internet of Things, and for good reason. It’s a technology on everyone’s radar right now, and is forecast to impact all lines of business, every major industry, and transform society as we know it. Advances in IoT and ubiquitous computing will spawn new IT models, change the way organizations interact with their customers, and continue to create new and innovative lines of business. This will generate great engines of economic growth throughout the world, but it will also create its share of challenges. Companies will be increasingly tasked with finding ways to capture, leverage, analyze, and visualize the vast amounts of data produced from the billions of new endpoints that come online in the next few years.




The significance of Internet of Things became apparent last summer when Gartner released its much anticipated annual Hype Cycle report, showing that IoT is now officially the most hyped technology – even more than Big Data. As huge as Big Data has been over the past 5 years, Internet of Things will be bigger – precisely because it will be this vast generator of all kinds of discrete data. As much as data has become an economic asset, and differentiator in industry, we haven’t really seen anything yet!

This is where predictive analytics comes into play. As one article declares, predictive analytics is really the science of taking the vast store of historical data – the ginormous amount of structured and unstructured data stored “out there” – and using data mining and statistics to make accurate models and predictions of future customer behavior and business scenarios in near real-time. PA is critical for providing a complete view of customer behaviors and trends. Businesses that learn to capture these insights will increase revenue, cut costs, and stay ahead of the competition.

Predictive analytics is complex and multi-faceted and it’s impossible to give a thorough overview in the confines of this article. But we want to pull together the highlights to capture the process and get you thinking about specific steps that will comprise your predictive analytics strategy going forward.

An integral factor to start thinking about now from a business perspective is how to start integrating IoT into your PA/Big Data strategy. We’ve said before, and it bears repeating here, that the Apple Watch is going to overhaul the smartwatch user experience and this will likely lead to commoditization of the smart watch and wearables in general. Moreover, Apple Watch (unlike other competitors) draws upon a whole ecosystem of supporting products, such as iPhone6, iOS 8, and apps like HeathKit, which will enable developers to create great health and fitness apps to work seamlessly together. Through these channels, Apple will create a host of new endpoint for the Internet of Things.

We digress, but the point here is that with all this data, organizations will need to be well-versed in the power of predictive analytics to draw conclusions about the trends, insights, and formulations of the massive sea of data. Read on for some steps to follow in organizing your PA data strategy.




Identify your business problem: What is the primary question you wish to solve? Look across all lines of your business to see where the painpoints are. What are your highest expenditures? What steps need to be taken to improve the bottom line? For instance, say you’re a heating and air conditioning business that wants to employ IoT to enhance your business, so you decide: “We want to collect and analyze data endpoints on our home furnaces so they sync with weather forecasts and allow home owners to adjust for changing temperatures in advance, and save on efficiency and costs.”

Understand your data: Start by asking some basic questions: where does the data reside, who owns it, how far back, and in what format? There are also growing numbers of companies on the market that specialize in helping businesses to analyze their existing data, or else to generate new data, in order to derive a 360 degree view of their customer interactions and behavior.

Clean your data: Disparate sets of data often contain duplicate, wrong and missing values, and other inconsistencies. Your data sets will need to be validated or “cleansed” to ensure optimal outcomes. Leverage available software tools to make this process less painstaking. Fortunately, there are many tools on the market that help automate the data preparation and cleansing process using graphical ETL (Extract, transform, and load) capabilities.

Build & evaluate your predictive model: This is a multi-faceted process that involves identifying the time frame of your study, choosing the dependent/independent variables, selecting the right methodology, and then building and testing the model. As a small business leader, who may be new to the world of predictive analytics and modeling, one of the most important points is to not get overwhelmed. The best advice especially for the novice is to make ready use of the growing numbers of predictive analytics tools on the market today.

Deploy the predictive model: The journey has been a long one, but don’t stop short of rolling out the predictive outcomes to the rest of the organization. Deploying your model means sending live data to your predictive model and then feeding the results to your favorite data visualization tool. The results will be worth it!





To keep competitive in today’s environment business leaders and stakeholders need to develop a clear predictive analytics strategy. This is especially incumbent now that Internet of Things is spinning up rapidly. PA is not just aligned with Big Data, but must embrace IoT as a major conduit for data endpoints. The path is wide open and resources are readily available for small business leaders to jumpstart their PA strategy today. The oft-quoted phrase, “A journey of a thousand miles begins with a single step,” is appropriate here because getting aligned with these latest technologies might seem daunting for some. But with clear planning and deliberate strategizing, predictive analytics can be a big win for your small business or startup in 2015, especially when integrated into a broader IoT and Big Data strategy.


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About Jeffrey Walker

Jeff is a business development consultant who specializes in helping businesses grow through technology innovations and solutions. He holds multiple master’s degrees from institutions such as Andrews University and Columbia University, and leverages this background towards empowering people in today’s digital world. He currently works as a research specialist for a Fortune 100 firm in Boston. When not writing on the latest technology trends, Jeff runs a robotics startup called virtupresence.com, along with oversight and leadership of startuplabs.co - an emerging market assistance company that helps businesses grow through innovation.