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.
by Steve Poulin and Patrick Callahan
As a Data and Analytics company, we at CompassRed have seen the full cycle of leveraging data for insight with each phase of development being just as important as any other. Deployment is the part of the predictive analytics process that puts the predictions to work. Predictive analytics requires a very time-consuming process of data preparation and a very complex process of finding the best algorithms (also known as “modeling”) for producing predictions. However, all of this effort is for naught if the predictions are not used by an organization to meet their objectives, which typically means increasing revenue and decreasing costs
By Patrick Callahan and Noah Baker
A major component to CompassRed’s predictive analytics capabilities - or any data analysis, is the method of delivery for the insights we unlock from a customer’s data. . Gathering insights without making them useful is a waste. Delivery can come in many flavors: via application programming interface (“API”) integration into your company’s software, custom triggers that alert via email/mobile notification/tweet, or in most situations, through dashboards delivered via “Business Intelligence (BI)”.
In our CompassRed Data Lab, on behalf of our clients, we are always looking for a better way to “Predict” with our algorithms, including Machine Learning (ML), and Artificial Intelligence (AI). As data becomes more ubiquitous and complicated, and as the systems that manage the data become more fluid, the process and methodology become ever more important (as long as they’re flexible). (read more)
How CompassRed’s Dr. Steve Poulin and his team leveraged Unstructured Data and External Data to uncover success and save $10 million dollars for every percentage point reduction in turnover.
When 92% of the agents hired by a life insurance company were leaving the company within their first year, this large Insurance Company realized their current approach to retention was not sustainable. This was causing significant expenses for continuous recruitment and hiring costs. Using unstructured data from the applicant’s resume and US Census data about the area in which they live, a predictive analytics process was developed to identify which applicants were most like to sell the amount of insurance required to become a successful agent with the company. (read more)
Dr. Poulin joins CompassRed as Principal Data Scientist & Manager of Predictive Analytics to continue CompassRed’s recent success providing data solutions to top companies in the county.
+ A great read and presentation by Azeem Azhar on the current state of technology (with a heavy focus on Machine Learning and what it means for our near term future)
+ McKinsey's most recent Analytics Study Defines the Future of Machine Learning
+ Do you know what is powerful realtime analytics?
"Once advertising becomes totally data-driven. advertisers are going to have to build their own data agencies or partner with data agencies. That's going to be a multibillion-dollar shake-up.”
When you’re standing at a podium and looking out over the crowd while delivering a presentation - are you able to understand the thoughts of the crowd - quickly? Or looking at any crowd for that matter - can you gather insight right from the stage? And not just from a Social Media measuring or monitoring exercise - but a quick true visual of the various conversations that are taking part around a topic, person, or concept?
We read a ton about "Big Data Tools & Techniques" successful CMOs need to know. Our offices are littered with all the new tech jargon that bubbles to the top of the industry. Rarely do we read about the business challenges and decisions that need to me made first.
Here’s an article by John Kelly via eConsultancy that does just that. The article discusses fundamentals and decisions to be made first, rather than just the “big data” stuff. In summary: lead with strategy and the business decisions, and then get the data to inform your decision making. “The big five” techniques from Sandeep Sacheti, executive VP at Wolters Kluwer discusses are: A/B Testing; Net Promoter Score; Customer Lifetime Value; Recency, Frequency, Monetary Analysis; and Customer Wallet Estimation.
CompassRed Data Labs (http://www.CompassRed.com) announced today that Robert “Bob” Dawes has joined the company to lead and enhance the growing Business Intelligence (BI) offerings for its regional and national accounts. Bob comes as a key leader with a wealth of experience and leadership within the Big Data and BI industry.
As the social data and analytics industry matures, new and innovative uses of the data continue to give us insight into industry and ourselves. A study published in Preventive Medicine in October, demonstrated that, combined with external data, Yelp could be used as a real-time surveillance tool to "provid[e] near real-time information on foodborne illnesses, implicated foods and locations."
Will you be in Washington DC on August 19th? We'll be assembling some great speakers at the WashingtonPost to discuss new ways organizations are visualizing social data with Twitter, MapBox, and NodeXL. Find out more here: http://www.meetup.com/Social-Media-Analytics-DC/
This past week - we attended the Big Boulder Initiative in Boulder Colorado. The meeting was the third in it’s history and comes at a pivotal time in the development of Social Data. We’ve been following the development of organizations using social data for the past three years and we are finally relieved and happy to see it mature in it’s ability to provide meaningful insight and affect the bottom line.
How Social Data is ultimately used within any organization is a question that most organizations are asking in order to move beyond the Facebook Fan or Twitter Follower count. At somepoint, someone is going to ask - "This is great - we have doubled our follower count, but we really need to leverage this somehow." Replying with a "it gives us better insight into our customers" is a step in the right direction - but not the final answer.
The think-tank The Altimeter Group, who has always been a leader in defining how social media is leveraged in companies, recently published a report on the state of social media data: "Social Data Intelligence: Integrating Social and Enterprise Data for Competitive Advantage." The study successfully drags the field towards specifics on how most companies are beginning to see a real change to the way they leverage social data and transform themselves from "a company that leverages Social Media" to a "Social Business." Read the full report here.
Businesses and organizations as a whole have yet to see the full impact of social data on the organization - but the wave is starting to be embraced. And as that happens, even more profound and impacting data enhancements are on the way as the scale and breadth of data starts to get even larger. With wearable devices (i.e. Google Glass, the Fit Bit, and Apple's rumored "iWatch") at the onset of creating even more data - organizations that have already started to build their data foundation to become a data driven organization gathering true insight.