Your company is deeply committed to better serving its customers. Because of this, you and your team built out a survey. This lets you quickly get customer feedback and respond accordingly. While building out the survey, you decided that one of the best ways to get customer feedback would be to use an open-ended question. This all sounds great. The problem? Open-ended questions have actually become annoying for you and your company to use. You certainly don’t feel like you’re getting the full benefit from them. Text analytics can help solve this problem.
Over the past two weeks we discussed two techniques that help to find patterns within data – clustering and association rules analysis. Clustering helps to split data into groups that are similar to each other. Association rules help to find items that are commonly grouped together. On their own, these techniques are powerful and could help any business to make better strategic decisions. While these techniques help you to mine your data – to understand the patterns within it – they fail to make any predictions about what will happen in the future. That’s where the last set of techniques come into play, aptly named predictive analysis.
Predictive analytics and data mining can be used for many different purposes. Last week we discussed cluster analyses, a group of techniques that can help businesses to better identify their customer segments (among many other things). This week we move on to a group of techniques that would be of interest for companies that sell many products, especially those in a retail environment: association rules.
Last week we discussed that there are three broad buckets of predictive analytics: clustering, prediction, and association. Using the techniques in each of these buckets allows for organizations to gain deep insights into the work that they do. Each bucket is a piece of the puzzle in building a model for a company. This week, we’ll discuss the first of those buckets: clustering.
A few weeks ago we discussed different types of questions that could be answered through predictive analytics. We included examples that covered everything from determining which customers might churn to figuring out what profits might look like in the future to what products might be bought together. For any organization, there's almost a never-ending number of questions that predictive analytics can answer.
Last week in our home state of Delaware there was an announcement - the New Castle County government launched an Open Checkbook. With the launch, New Castle County became the first local government in Delaware to embrace open data, joining the State of Delaware and their open data portal.
Everyday new data visualizations are built, highlighting information in a way that is interesting and meaningful for the people consuming it. As visualization libraries have improved, the breadth of visualizations has grown tremendously. Since the beginning of the year we've been amazed by some of the visualizations that have been built. Today, we're presenting 5 of our favorites (in no particular order).
The Google Analytics (GA) add-on for Google Sheets is an accessible introduction to the power of the GA API (Application Programming Interface), letting non-developers easily collect, manipulate, and share the data.
On October 17, 1991, Walt Mossberg published the first installment of his weekly column for the Wall Street Journal, Personal Technology. In his first words, he succinctly summarized popular sentiment at the time towards personal computers. Now, 30 years later, his sentiments apply just as well to artificial intelligence as they once did to personal computing.
Over the past seven years "mobile first" has taken over design philosophies across the country. This past week, Google launched a new product signaling their transition to a new philosophy: AI first.
“Predictive analytics”. If you've paid any attention to the business or tech world over the past several years, then you’ve heard the phrase. It's a phrase that has likely been accompanied by other, equally intimidating sounding words like “big data” or “artificial intelligence” or “machine learning”. Over the past decade, these phrases have only become more popular and even more important for businesses to understand. Don't believe us? Here's what the Google Trends for both "big data" and "machine learning" look like over the past 13 years.
At its heart, predictive analytics is about making better choices. If your company has the opportunity to make an informed decision, then it is likely to be more successful in the long run. There are plenty of ways that predictive analytics does this – from artificial intelligence to clustering to regression. One particularly important tool for predictive analytics is time series analysis.
Machines walking. Computers tutoring. Cars driving. Algorithms diagnosing. As we outlined in Part One of this series, each of these examples highlights some of the amazing progress that we have made using artificial intelligence. In Part Two, we discussed how these artificial intelligence systems work. Beyond understanding what artificial intelligence is and how it works, we must also understand its pros and cons.
Last week we discussed what makes artificial intelligence…artificial intelligence. Over the past several years AI has grown increasingly popular, helped along by the mystique of its name. AI is one technique of many in the world of predictive analytics, though. While last week we talked about what artificial intelligence is, we didn’t go into as much detail about how it works.
Each week at CompassRed, we take a few minutes to share what we've been reading with you. Some of it's technical, some of it's topical, all of it's interesting.
by Dr. Steve Poulin
Over the past several years, artificial intelligence (AI) has received an increasing amount of attention (and scrutiny) recently because of the exciting new ways it is being used. Many people are familiar with the technologies that AI has enabled, but how many people actually know how it works? In this post, we'll explore what makes artificial intelligence...artificial intelligence.
by Dr. Steve Poulin
Those who are working with Predictive Analytics are always trying to find a better more effective way. We have chosen a field that that can get better and better every day. And in the last few years, with the development of new technologies and approaches to acquiring data – we are finding the spotlight on us to do just that: find better ways. Traditionally, there are three primary ways to develop models and algorithms for predictive analytics: (1) the expensive SAS solution, (2) the cheaper but just as effective IBM SPSS, or (3) the open source “R”. We, at CompassRed, think there is a fourth: Leverage the best of all.
CompassRed Data Labs announces today that they have been approved to become a certified IBM Business Partner.