Taking Big Data to the Next Level With Predictive Analytics
Every day, we create 2.5 quintillion bytes of data. To put that in perspective, that’s over a thousand times more information than in the entire Library of Congress. It’s also over a million high definition videos. And that’s just in a single day.
We’re creating so much data that 90 percent of the data in the world today has been created in the last two years alone. And the pace of data creation is only accelerating. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records and cell phone GPS signals to name a few. This data is big data.
Problems with big data
The very ubiquity of big data brings its own problems.
First, big data is just too big. The very size of big data makes it difficult to process. There is already more data in the world than can be easily processed, and more is added every day.
Second, big data is messy. Just like the human beings that generate it, the data we deal with on a daily basis is inconsistent, biased, and unpredictable.
Third, big data is unstructured. Having terabytes of data doesn’t do you much good if you can’t tell the difference between an mp3 and a set of GeoCoordinates.
Why predictive is more than big data
There’s a lot of talk about predictive analytics, and often people wrongly conflate the two. What’s the correlation?
While big data refers to the mounting repository of information, predictive analytics is the process by which all that data can be turned into something useful. Predictive is often defined as the practice of extracting information from existing data sets and using it to build models and determine patterns to predict future outcomes and trends. In other words, predictive unleashes the potential of big data by building algorithms that can actually learn from themselves.
These robust learning engines can pull in enormous amounts of data and find the overlapping patterns that allow organizations to, for example, predict crime before it happens. Others have deployed predictive analytics to help combat human trafficking. Still others are identifying new subtypes of diabetes with these algorithms.
Wherever human life is generating data, in any domain, predictive analytics can be applied to identify and take advantage of patterns. Predictive analytics won’t clean the data for you, but it does provide the justification for cleaning it up. Data cleansing is not a cheap process, and you need to know you’ll be able to put it to good use once it’s cleaned.
Using big data for prediction and prescription
Predictive analytics has the potential to drive action in a way that big data and traditional methods don’t. With predictive, you have a much clearer idea of what is going to happen when. You can prepare for this by going one step beyond prediction and actually prescribing the recommended course of action.
XANT has built its platform around the potential of data science as a means of predicting and prescribing sales actions to improve results.
By collecting and analyzing billions of sales interactions collected over 12 years, our self-learning engine is able to not only predict which prospects are most likely to become buyers, but also, prescribe to sales reps the optimal times to reach these prospects.
Prescription also comes with another benefit: structured data. When you’re prescribing specific actions inside of an app, the data you receive back from the user will be structured in a way that automatically updates the data set and allows your predictive engine to learn on its own.
Big data comes with enormous potential to transform how business operates, but it also comes with serious limitations. Predictive analytics promises to offer an effective means to unlock the potential of big data with self-learning algorithms. Finally, pairing predictive with prescriptive applications allows the capture and analysis of structure data.
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Image credit: Luc Legay