Five Trends in Predictive Analytics

Predictive analytics, a technology that has been around for decades has gotten a lot of attention over the past few years, and for good reason.  Companies understand that looking in the rear-view mirror is not enough to remain competitive in the current economy.  Today, adoption of predictive analytics is increasing for a number of reasons including a better understanding of the value of the technology, the availability of compute power, and the expanding toolset to make it happen. In fact, in a recent TDWI survey at our Chicago World Conference earlier this month, more than 50% of the respondents said that they planned to use predictive analytics in their organization over the next three years. The techniques for predictive analytics are being used on both traditional data sets as well as on big data.

Here are five trends that I’m seeing in predictive analytics:

  • Ease of use.  Whereas in the past, statisticians used some sort of scripting language to build a predictive model, vendors are now making their software easier to use.  This includes hiding the complexity of the model building process and the data preparation process via the user interface.  This is not an entirely new trend but it is worth mentioning because it opens up predictive analytics to a wider audience such as marketing.  For example, vendors such as Pitney Bowes, Pegasystems, and KXEN provide solutions targeted to marketing professionals with ease of use as a primary feature.  The caveat here, of course, is that marketers still need the skills and judgment to make sure the software is used properly.
  • For more trends: http://tdwi.org/blogs/fern-halper/list/ferns-blog.aspx

Closing the loop in customer experience management: When it doesn’t work

Last week I had the unfortunate experience of trying to deal with American Airlines regarding some travel arrangements via its Advantage help desk.  I literally spent hours on the phone trying to get to the right person.  I won’t bore you with the details of my experience, however I did want to talk about how American used social media analytics in an active way – and where it came up short.

By now, many people are aware that companies are not only using social media analytics to understand what is being said about their brand; they are using it to actively engage with a customer when there is a problem as well.  This typically involves some sort of automatic classification of the problem, automatic routing to the right person, and suggested responses to the customer.

The good news was that when I tweeted about American Airlines I actually got a response back from them.  Here’s my first tweet and response:

First twitter round with aa

So far, not bad.  Here’s the next round of tweet/response:

conversation with aa round 2

Well, this was not what I wanted to hear, since it only partially addressed my issue.  If I just wanted an apology, I would not have bothered to tweet about a credit.  I would have preferred a follow up email (if they had a way to link my information together) or at least the contact information where I could get more help.  American Airlines wasn’t helping me they were whining.

So then I tweeted the following:

aa round 3

I gave up after this response. Frankly, it almost sounded sarcastic.  Should I have said, “Not on twitter, send me an email contact?”  I’m sending a letter to Craig Kreeger, instead, explaining my dissastisfaction. Maybe I’ll send it snail mail……….

My point is that if you’re going to engage your customers online via the channel that they used in the first place, make it count.  This exchange simply annoyed me.  Maybe twitter wasn’t the best channel for customer service, but it is the one that I used since no one was answering the phone and the American site couldn’t let me perform the function I wanted to do.  I’m not saying its’ easy to engage adequately via twitter.  To do this properly would have involved more finely tuned text analytics to understand what I was actually talking about as well as a way to integrate all of my data together to understand me as a customer (i.e. my loyalty information, recent trips, etc).  Maybe the customer service reps were tired after last month’s outage debacle at American when thousands of passengers were

Two Big Data Resources Worth Exploring

It’s a good day.  Our new book, Big Data for Dummies, is being released today and I’m busy working on a Big Data Analytics maturity model at TDWI with Krish Krishnan.  Krish, a faculty member at TDWI, is actually presenting some of the model at the TDWI World Conference:  Big Data Tipping Point taking place during the first week of May (see sidebar).  I would encourage people to attend, even if you aren’t that far along in your big data deployments.  TDWI has terrific courses in all aspects of information management and we understand that most companies will need to leverage their existing infrastructure to support big data initiatives.  In fact the title of this World conference is, “Preparing for the Practical Realities of Big Data.”   Check it out.

Back to the book.  Here’s a look at the Introduction!  Enjoy!

 

3 Takeaways from the IBM Big Data Event

Last week I attended the IBM Big Data at the Speed of Business event at IBM’s Research facility in Almaden.  At this analyst event IBM announced multiple capabilities around its big data initiative including its new BLU Acceleration and IBM PureData System for Hadoop.  Additionally, new versions of Infosphere Big Insights and Infosphere Streams (for data streams) were announced as enhancements to IBM’s Big Data Platform.  A new version of Informix that includes time series acceleration was also announced.

The overall goal of these products is to make big data more consumable –i.e. to make it simple to manage and analyze big data.  For example, IBM PureData System for Hadoop is basically Hadoop as an appliance, making it easier to stand up and deploy.  Executives at the event said that a recent customer had gotten its PureData System “loading and interrogating data 89 minutes.”  The solution comes packaged with analytics and visualization technology too.  BLU Acceleration combines a number of technologies including dynamic in-memory processing and active compression to make it 8-25x faster for reporting and analytics.

For me, some of the most interesting presentations focused on big data analytics.  These included emerging patterns for big data analytics deployments, dealing with time series data, and the notion of the contextual enterprise.

Big data analytics use cases.  IBM has identified five big data use cases from studying hundreds of engagements it has done across 15 different industries.   These high value use cases include:

  • 360 degree view of a customer- utilizing data from internal and external sources such as social chatter to understand behavior and “seminal psychometric markers” to gain insight into customer interactions.
  • Security/Intelligence- utilizing data from sources like GPS devices and RFID tags and consuming it at a rate to protect individual safety from fraud or cyber attack.

For more visit my tdwi blog

Two Weeks and Counting to Big Data for Dummies

I am excited to announce I’m a co-author of Big Data for Dummies which will be released in mid-April 2013.  Here’s the synopsis from Wiley:

Find the right big data solution for your business or organization

Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you’ll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You’ll learn what it is, why it matters, and how to choose and implement solutions that work.

  • Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals
  • Authors are experts in information management, big data, and a variety of solutions
  • Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more
  • Provides essential information in a no-nonsense, easy-to-understand style that is empowering

 

Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Five Best Practices for Text Analytics

It’s been a while since I updated my blog and a lot has changed.  In January, I made the move to TDWI as Research Director for Advanced Analytics.  I’m excited to be there, although I miss Hurwitz & Associates.   One of the last projects I worked on while at Hurwitz & Associates was the Victory Index for Text Analytics.  Click here for more information on the Victory Index.  

As part of my research for the Victory Index, I spent I a lot of time talking to companies about how they’re using text analytics.  By far, one of the biggest use cases for text analytics centers on understanding customer feedback and behavior.  Some companies are using internal data such as call center notes or emails or survey verbatim to gather feedback and understand behavior, others are using social media, and still others are using both.  

What are these end users saying about how to be successful with text analytics?  Aside from the important best practices around defining the right problem, getting the right people, and dealing with infrastructure issues, I’ve also heard the following:

Best Practice #1 - Managing expectations among senior leadership.   A number of the end-users I speak with say that their management often thinks that text analytics will work almost out of the box and this can establish unrealistic expectations. Some of these executives seem to envision a big funnel where reams of unstructured text enter and concepts, themes, entities, and insights pop out at the other end.  Managing expectations is a balancing act.  On the one hand, executive management may not want to hear the details about how long it is going to take you to build a taxonomy or integrate data.  On the other hand, it is important to get wins under your belt quickly to establish credibility in the technology because no one wants to wait years to see some results.  That said, it is still important to establish a reasonable set of goals and prioritize them and to communicate them to everyone.  End users find that getting senior management involved and keeping them informed with well-defined plans on a realistic first project can be very helpful in handling expectations. 

 

for more visit my tdwi blog

 

 

Five Challenges for Text Analytics

While text analytics is considered a “must have” technology by the majority of companies that use it, challenges abound.  So I’ve learned from the many companies I’ve talked to as I prepare Hurwitz & Associates’ Victory Index for Text Analytics,a tool that assesses not just the technical capability of the technology but its ability to provide tangible value to the business (look for the results of the Victory Index in about a month). Here are the top five: http://bit.ly/Tuk8DB.  Interestingly, most of them have nothing to do with the technology itself.

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