IBM launched on January 9, 2014 its first business unit in 19 years to bring Watson, the machine that beat two Jeopardy champions in 2011, to the rest of us. IBM posits that Watson is the start of a third era in computing that started with manual tabulation, progressed to programmable, and now has become cognitive. Cognitive computing listens, learns, converses, and makes recommendations based on evidence.
IBM is placing big bets and big money, $1 billion, on transforming computer interaction from tabulation and programming to deep engagement. If they succeed, our interaction with technology will truly be personal through interactions and natural conversations that are suggestive, supportive, and as Terry Jones of Kayak explained, "makes you feel good" about the experience.
There are still hurdles for IBM and organizations, such as expense, complexity, information access, coping with ambiguity and context, the supervision of learning, and the implications of suggestions that are unrecognized today. To work, the ecosystem has to be open and communal. Investment is needed beyond the platform for applications and devices to deliver on Watson value. IBM's commitment and leadership are in place. The question is if IBM and its partners can scale Watson to be something more than a complex custom solution to become a truly transformative approach to businesses and our way of life.
Forrester believes that cognitive computing has the potential to address important problems that are unmet with today’s advanced analytics solutions. Though the road ahead is unmapped, IBM has now elevated its commitment to bring cognitive computing to life through this new business unit and the help of one third of its research organization, an ecosystem of partners, and pioneer companies willing to teach their private Watsons.
December 26th at my house was probably a lot like it was at yours: We ate leftovers; we binge-watched shows we’d missed earlier this year; and we played with toys. Not kids’ toys—tech toys. The one we played with most is also the one I spent the most time researching before I bought it: the 3D printer.
Between printing demo pieces and whistles, I checked out my favorite sites to see if any new stories had been posted over the holiday. One of them appears to have implemented a cookie-based content targeting strategy, as both its tech and design sections were packed with headlines about 3D printing. I was pleased to see this attempt at relevance, but it failed in my case. Why? Because it was too one-dimensional.
By just looking at my recent cookies, an automated system could conclude that I’m interested in 3D printing in the abstract. But in fact, I was just trying to learn everything I could in order to make the most informed purchase. If the targeting strategy had taken into consideration the timing of those cookies (I only ever dug into the topic between Thanksgiving and the second week of Dec), my affinity data from Facebook and other social networks, and my long-standing content habits, I would probably have ended up with headlines related to smartphones, tablets, and wearables: things I’m more interested in now that my Christmas shopping is done. 3D printing headlines may have seemed more relevant, but they didn't get a single click from me.
But what are the trends, and what are the best practices?
We are hearing from all the pharma stakeholders four stories that are driving the questions that are being asked of the data:
Pharma needs to get away from its focus on molecules and pivot to a holistic view of disease. As per a senior IT manager at a major pharma in a meeting with me last week: "We have to deliver whole solutions, and not just pills."
Pharma needs to understand prescribing behavior in the formulary and in the physician's office better in order to influence it and thus drive sales. As per a senior marketing manager from a meeting recently: "In the old world, we just sprayed and prayed," meaning that the marketing campaigns aimed at the physician did not discriminate as to who that physician was.
Genomic-based drugs are driving changes though the amounts and types of data that the industry must manage.
I’m sitting on my sofa at home (Yes! Home!) on Sunday morning just before Christmas. I’m “shut down” for the holidays now, but of course, I’m watching Twitter and now listening to my brilliant friends Chris Dancy and Troy DuMoulin discussing CMDB (configuration management database) on the Practitioner Radio podcast. It’s a marvelous episode, covering the topic of CMDB in with impressive clarity! I highly recommend you listen to their conversation. It’s full of beautiful gems of wisdom from two people who have a lot of experience here – and it's pretty entertaining too!
I agree with everything these guys discussed. In particular, I love the part where they cover systems thinking and context as the key to linking everything conceptually. I only have one nit about this podcast, and the greater community discussion about CMDB, though. Let’s stop calling this “thing” a CMDB!
I coauthored a book with the great Carlos Casanova (his real name!) called The CMDB Imperative, but we both hate this CMDB term. This isn’t hypocritical. In fact, we make this point clear in the book. Like the vendors, we used CMDB to hit a nerve. We actually struggled with this decision, but we realized we needed to hit those exposed nerves if we were going to sell any books. Our goal is not to fund a new Aston Martin with book proceeds. If so, we failed miserably! We just wanted to get the word out to as many as possible. I hope we've been able to make even a small difference!
The majority of large organizations have either already shifted away from using BI as just another back-office process and toward competing on BI-enabled information or are in the process of doing so. Businesses can no longer compete just on the cost, margins, or quality of their products and services in an increasingly commoditized global economy. Two kinds of companies will ultimately be more successful, prosperous, and profitable: 1) those with richer, more accurate information about their customers and products than their competitors and 2) those that have the same quality of information as their competitors but get it sooner. Forrester's Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 (we are currently fielding a 2014 update, stay tuned for the results) survey showed that enterprises that invest more in BI have higher growth.
The software industry recognized this trend decades ago, resulting in a market swarming with startups that appeared and (very often) found success faster than large vendors could acquire them. The market is still jam-packed and includes multiple dynamics such as (see more details here):
All ERP and software stack vendors offer leading BI platforms
. . . but there's also plenty of room for independent BI vendors
Departmental desktop BI tools aimed at business users are scaling up
Enterprise BI platform vendors are going after self-service use cases.
Cloud offers options to organizations that would rather not deal with BI stack complexity.
Hadoop is breathing new life into open source BI.
The line between BI software and services is blurring
I regularly hear CIOs and IT suppliers discussing the “four pillars” of cloud, social, mobile, and big data as if they’re an end in themselves, creating plenty of buzz around all four. But really, they’re just a means to an end: Cloud, social, mobile, and big data are the tools we use to reach the ultimate goal of providing a great customer experience. Most CIOs in Australia do understand that digital disruption and customer obsession are the factors that are changing their world, and that the only way to succeed is to embrace this change.
My colleagues at Forrester and I have been puzzling over the discrepancy between the wealth of attractive new mobile, cloud, and smart computing technologies in the market, and the relatively weak record of actual growth in tech spending that our tech market forecasting numbers show. Certainly, the recessions in Europe and weak economies in the US, Japan, China, India, Brazil and other emerging markets explain part of the weakness in tech buying. In addition, cloud computing’s impact on the timing of tech spending (reducing initial upfront capital purchases of owned hardware and software while increasing future subscription payments for use of these resources) means that spending that in the past would have occurred in current years has now been pushed into the future. Lastly, as a recent Economist article pointed out, business investment in general has been low compared to GDP and to cash distributed to shareholders this decade, as CEOs with stock option compensation have focused on meeting quarterly earnings-per-share targets instead of investing for the longer term (see Buttonwood, “The Profits Prophet,” The Economist, October 5, 2013). Still, even taking these factors into account, tech investment has been growing more slowly relative to economic activity than in past cycles of tech innovation and growth.
Last year, my colleague Srividya Sridharan published The State Of Customer Analytics 2012 (subscription required). Using the results of her annual customer analytics adoption survey, she uncovered key trends of how customer analytics practitioners use and adopt various advanced analytics across the customer life cycle and highlighted challenges and drivers associated with customer analytics.
This year, I have the pleasure of teaming up with Sri on her yearly survey, to further explore the adoption of advanced analytics, measurement, and attribution. Please read her blog post to learn more about the survey. This survey will explore the adoption and usage of measurement techniques, including attribution, and the adoption of advanced analytics methodologies. With this expanded survey we want to understand how you use and apply measurement and analytics in your organization to optimize both cross-channel marketing campaigns and customer programs.
In particular, we’re fielding questions to understand the goals and challenges associated with measurement and analytics, the adoption and application of measurement and advanced analytics methods, the use of several marketing and customer metrics, the customer insights process and workflow, and the organizational aspects that support measurement and analytics. We encourage you to participate in this survey, as this information will help you benchmark your measurement and analytics adoption efforts.
In our recently completed Q3 2013 Global State Of Enterprise Architecture Online Survey, big data for real-time analytics moved from the No. 3 most revolutionary technology to the No. 2 position, according to the 116 enterprise architects who participated. This reflects the importance firms now place on turning vast amounts of data into immediate insight. And this trend is extremely important to telecommunication industry communication service providers (CSPs), who are sitting on a gold mine of data about what subscribers are doing on their mobile devices.
Let’s break this down a bit more -- according to the United Nations, there are about 2 billion mobile broadband subscriptions globally (that’s about 28% of the world’s 7.1 billion people). That’s a huge number of perpetually connected people, using bunches of apps for both work and personal. This is part of what we call the mobile mind shift, and it’s not about smartphones and tablets; rather, it’s about the changing expectations that pervasive mobile computing and broadband wireless have. According to a recent report, "The Mobile Mind Shift Index," we estimate 21% of the adult online US population now expects that any information is available on any appropriate device, in context, at their moment of need (see Josh Bernoff’s May 2013 blog Introducing The Mobile Mindshift Index). And this number is going to grow significantly over the next few years.
I remember my first day at high school. Yikes it was scary. The older kids were BIG! The teachers were BIG (the phys ed teacher was even a little mean), the school was BIG . . . Everything felt so BIG! But as the year ticked by, l became familiar and comfortable with my classmates, teachers, and the school -- the place shrunk to a more comforting size.
Today marketers feel about data as I did about my first day at big school -- it’s BIG. There is lots of it, and it’s coming at them from many directions and in many forms. But data does not feel so big and daunting to the marketer who recognizes their customers buried in the fog of big data. The fact is, customer recognition is the key for marketers to make sense of big data; and it is at the heart of all effective marketing activities. I write about this in my most recent report: “Customer Recognition: The CI Keystone.”
So what is customer recognition?
Recognition associates interactions with individuals or segments across time and interactions. The strength of recognition is gauged on its ability to associate interactions to anything from individuals to a broad segment; and to persist those associations across different touchpoints over time.
Keys are needed for recognition at touchpoints. There are many types of keys, ranging from IP addresses, to cookie-based TPIKs, to phone numbers and customer account numbers. At Forrester we call them touchpoint interaction keys (TPIKs)