The Olympic Games and US Presidential Elections come around every four years but SharePoint upgrades come on a three year cycle. There are still organizations using SharePoint 2007 and in the process of migrating to SharePoint 2010 and now we have SharePoint 2013 in all its glory.
Microsoft also seems to be hinting that in future there could be more frequent upgrades. Before long you will probably be able to major in Microsoft Upgrade and Migration Planning at most major universities.
My particular interest is in enterprise search and here I have to congratulate Microsoft on the progress it has made since the fairly terrible search functionality in SharePoint 2007. The company was also smart enough to go out and buy FAST Search and Transfer in 2008, but not quite smart enough about financial due diligence and building a sensible search technology strategy for itself and for SharePoint.
The Technology Story So Far
The immediate result was the arrival of FAST Search Server for SharePoint 2010, abbreviated to FS4SP. This took a lot of the components of FAST ESP 5.3, suitably modified to SharePoint 2010, and offered a substantial enhancement to SharePoint Search 2010 which itself was a significant leap forward from the search offering in SharePoint 2007.
Two issues immediately became obvious. First, many companies were convinced that they now had a licence for FAST ESP 5.3 and had no idea of the real state of affairs. Second, no one in the Microsoft partner community had any idea of how to get the best (or indeed anything at all!) out of FS4SP. My experience says that not that much has changed since launch unless the company has brought in external implementation expertise.
In 2010 Wrox published "Professional Microsoft Search" by Mark Bennett and his colleagues that covered all the Microsoft search products (including FAST ESP 5.3) in 450 pages. It is an excellent book because of the substantial amount of guidance it gives on the skills and management attention needed to get the best out of any search application.
It was not until earlier this year that Microsoft itself published "Working with Microsoft FAST Search Server 2010 for SharePoint," which runs to 450 pages just on FS4SP. There are so many omissions from this book that it would take me the rest of this column to list them, but the most notable are no references at all to using search logs to manage search performance and a total absence of any indications of the staffing requirements to support the product. It creates the impression that search implementation is a project, when at the minimum it is a program and in reality it is a journey without end.
The other significant problem is that (unlike the Wrox book) the book does not tell you what FS4SP does not do. A good example is document thumbnails, which out of the box are only supported for Word documents and Powerpoint files. Certainly there are some good third-party solutions (Documill comes to mind) but that misses the point.
Fortunately there are many excellent search implementation companies (Comperio, Findwise, Raytion and Search Technologies for example) that can really make FS4SP sing and dance but that inevitably adds to the implementation cost. Even then, any company running FS4SP probably needs a search support team of at least 3-4 people full time. A look at Sadie van Buren’s invaluable SharePoint benchmarking service shows search way down the list in maturity of implementation.
Having provided web content management solutions for major brands over ten years, Agility is now launching a new, cloud-based Magazine Publishing Suite that is optimized for online magazines and blogs.
CEO Michael Assad said in a statement that the Suite “follows the success we’ve had with our CMS platform in the media and publishing space,” and that it “saves months of setup time and tens of thousands of dollars over traditional web publishing systems.” He added that the Magazine Publishing Suite is intended to help publishers who are “struggling to find their footing online” make the transition to digital versions of their properties.
Media-Heavy Sites
The Toronto-based company said its Suite was developed over five years, and has been built on its experience of working with large media companies and serving millions of page views.
The system is oriented toward media-heavy sites that offer frequent updates of images and video, as well as text, and the company said it was the only online publishing suite specifically intended for media sites (we'd argue with that, pointing out Atex as another example). The company’s customers currently include Glow Magazine, Canadian Bride, Clean Eating, Oxygen, Oprah Winfrey Network and W Network.
The Suite is designed around nine areas that target website performance — layout and navigation, advertising and ad placement, search engine optimization, content merchandizing, social media integration, content syndication, mobile, community and performance.
Subscription Integration
The magazine-specific features include the ability to integrate the system with major magazine subscription systems, and a built-in module for showcasing magazine issues. The CMS is also oriented toward continuous content updates, has support for articles, blog posts, galleries and videos, and can import content from the publisher’s current Web CMS.
There are also such functions as Latest Articles, including an automatic update to the most recently published items, Popular Articles, Polls and Contributor Workflow to channel writers’ submission into the editing/publishing process.
Using the CMS, Agility said that a new site can be ready for business in as little as five weeks.
The delivery process, which is covered by a $1000 setup fee, involves consultation with Agility’s experts, design of a site skin, content deployment from a current site and training. The system is priced at $300/month, with additional charges for tech support by phone, support for external contributors and other add-ons.
The computing world won't change overnight with the launch of Windows 8, but in a few years time, the digital environment could be influenced heavily by what Microsoft shows today in New York.
The Big Day is Here
It is kind of odd watching a Microsoft software launch, we've seen the operating system in action since the first builds, developer betas, public trials and all the way up to RTM. So, there isn't a huge amount for Microsoft to amaze us with.
But, combined with all the new hardware that's coming, Microsoft can still pull off a few tricks. And it has to both amaze, inform and delight with the launch, as hundreds of millions of perfectly happy Windows 7, Vista and XP owners remain to be convinced.
You can watch the webcast here, and we'll update with product and feature news as it happens. With Microsoft having taken over a chunk of Times Square for the whole day, it could be quite a long running series of events.
After almost 80 years in print publication, weekly U.S news magazine, Newsweek is leaving the print world behind. Today, the magazine’s parent company, The Newsweek/Daily Beast Company LLC, announced that the long time magazine would cease the publication of their print version, in favor of a digital one in early 2013.
What About Print?
Tina Brown, Newsweek's editor and chief, and founder of the Newsweek Daily Beast Company, along with Baba Shetty, CEO, posted on the magazine’s website today. Due to the increasing demand of digital and online media, a decision was made regarding how the magazine should proceed.
Our business has been increasingly affected by the challenging print advertising environment, while Newsweek’s online and e-reader content has built a rapidly growing audience through the Apple, Kindle, Zinio and Nook stores as well as on The Daily Beast,” says Brown and Shetty in the post. “Tablet-use has grown rapidly among our readers and with it the opportunity to sustain editorial excellence through swift, easy digital distribution — a superb global platform for our award-winning journalism.”
The Future is Digital
The Daily Beast attracts over 15 million, up 70 percent from 2011, unique visitors per month with a lot of these visitors visiting articles by Newsweek reporters.
Exiting print is an extremely difficult moment for all of us who love the romance of print and the unique weekly camaraderie of those hectic hours before the close on Friday night,” said Brown and Shetty. “But as we head for the 80th anniversary of Newsweek next year we must sustain the journalism that gives the magazine its purpose — and embrace the all-digital future."
The post continues by saying that 39 per cent of Americans who were polled in a study from the Pew Research Center have said they read their news online. It’s because increase in digital readership, along with the previously mentioned factors that have lead The Daily Beast to make a change that they say will be profitable and beneficial to Newsweek’s readers.
We are transitioning Newsweek, not saying goodbye to it. We remain committed to Newsweek and to the journalism that it represents,” says Brown and Shetty. “This decision is not about the quality of the brand or the journalism—that is as powerful as ever. It is about the challenging economics of print publishing and distribution.”
The magazine’s last print issue will be released on December 31, 2012. The new digital only magazine will be renamed to Newsweek Global and will available by paid subscription to those with tablet and web e-readers, while selected content will be available on the The Daily Beast website.
LinkedIn use is exploding, and all those millions of new users added over the last two years have some updated features to play with.
LinkedIn has updated the homepage by adding a larger profile image and redesigning the whole feel of its pages with a more refined look. The power to find new contacts and network effectively is still there — it's just under a less utilitarian design.
Status: Updated
Starting today, LinkedIn members can update their profile via an opt-in sample/invitation. Everyone else will see the updates roll out to their profiles over the next few months, the company said in blog post. Larger profile images, new inline editing and richer insights into people's networks are all part of the update.
Larger images show off a profile's highlights right at the top of the page.
Additionally, the Activity Stream, People You May Know and Background sections all have a new look, with the people section having the most dramatic change. There's a new graphic stats display for network suggestions that adds some color and useful information on those you may want to connect with.
For updating background information on your profile, there is a new menu of options that pops up on the right-hand side.
Suggestions are still made about what updates to make, but now they are a bit more polished.
Simpler Editing
Once updates are made to the background section, each portion gets a new little icon instead of a solid horizontal line to separate them. For example, there's a little hand next to the Volunteer and Causes section, and a little compass above the Skills and Expertise section.
This is also where the inline editing feature comes in. Now, instead of changing those skills using a new edit page, the changes can be made right from the profile — no need to click over to the Edit Profile page.
Simpler editing makes an already popular activity even easier.
Linkedin CEO Jeff Weiner gave a brief presentation on the updates, and said the company now has over 175 million users, with 50 million of those added in just the last two years. He also pointed out how popular the recent Company Pages release has been and how millions of people are updating their profiles even though they aren't necessarily looking for jobs.
Tell us in the comments if you are a LinkedIn Pro user or if you are cross-posting things to LinkedIn and Facebook — and how you decide what goes where.
The Enterprise Governance, Risk and Compliance (EGRC) market has been evolving steadily since it emerged eight years ago. It has now matured to such a point that, according to Gartner’s recent Enterprise GRC Magic Quadrant, the key differentiators are the delivery of advanced risk management functionality. Running straightforward GRC components is no longer enough to make the cut.
Gartner’s GRC Magic Quadrant
This contrasts with earlier GRC platforms where differentiation was about the provision of basic core functions like audit management, compliance management, or risk and policy management.
MarketScope reports help users understand how the status of an emerging or mature market aligns with their own state of maturity and future plans, rather than providing comparisons between vendors and products.
The level of maturity in this market probably also explains why there are now nine different companies in the Leaders quadrant, six in the Visionaries quadrant, and handful of vendors across the Challengers and Niche players quadrants.
The Leaders Quadrant includes: EMC-RSA, IBM, MetricStream, Nasdaq-BWise, Oracle, SAP, SAS, Software AG and Thomson Reuters.
In this first look at Gartner's MQ we will look at what’s driving the market. Later in the week, we will look at the Leaders and what it is they are doing that is pushing them to the top of the pile.
The Evolution of EGRC
According to the report's authors French Caldwell and John Wheeler, the principal focus in the EGRC market is on enterprise risk management, with many vendors looking to the next phase in the market evolution. This next phase will include adding or integrating with business analytics, and scorecarding capabilities.
Generally speaking, the market can be divided into two separate functionality sets: GRC management products to oversee risk management and compliance programs, and, secondly, GRC products for the automation and monitoring of controls.
In both cases, some of that functionality is inherent in EGRC platforms. In the current market, most enterprises are investing in platforms that do a little of everything, instead of platforms that cover a single area like finance, IT or legal.
Where more sophisticated functionality is required, enterprises are integrating point solutions to satisfy GRC needs, rather than buying platforms that cover specific areas of business.
By investing in single platforms with integration when needed, users get a holistic view of the entire enterprise's risk and compliance exposure, as well as views of geographies, business entities and enterprise needs.
EGRC Risk Management
The principal purpose of the EGRC platform is to automate the work associated with the documentation and reporting of risk management and compliance activities. The key functions are:
Risk management: Offers enterprises documentation, workflow, assessment and analysis, reporting, visualization and remediation of risks.
Audit management: Manages audits related to work, time management and reporting.
Compliance and policy management: Documentation, workflow, reporting and visualization of controls objectives, controls and associated risks among others.
Regulatory change management: Enables business and risk analysis of changes to regulations as well as impact on business.
EGRC platforms are able to do this across the enterprise through integration with legacy systems like business intelligence, content management, controls automation, monitoring solutions and IT technical controls.
A few years ago, IBM’s new computer was a game-playing curiosity. Now Watson is poised to change the way human beings make decisions about medicine, finance, and work. Photo by Dan Winters
The woman was gravely ill. Her name was Ms. Yamato. thirty-seven years old, born in Osaka, Japan, she had never smoked, and yet there it was anyway: a spot on her lung.
A doctor had already performed a bronchoscopy and had made the diagnosis of cancer. Then he referred the patient to Mark Kris, an oncologist at Memorial Sloan-Kettering Cancer Center in New York. Seated alongside me in his office on the Upper East Side of Manhattan, Kris is showing me Ms. Yamato's electronic medical record on an iPad. "I'm preparing for the first visit," he explains, swiping the screen to show what that entails. He's interested in running at least two tests on the patient. The first is an MRI, to find out if the cancer has spread to her brain. The second involves a deeper diagnostic regimen. Lung cancer tumors are not all the same; there are thousands of variations. So a test that examines the mutations within a tumor will be crucial, he says. It so happens that cancer patients born in East Asia who have never smoked often have a particular mutation that responds well to a medication by the name of Erlotinib. That may be the case here. One can hope.
The woman is not real. She happens to be a character within an app that IBM has created for Watson, its new computer. Watson's special talent, its reason for being, is a singular ability to grasp the intricacies of human language and answer exceedingly difficult questions. You may have heard about Watson already. Back in 2007, a group of computer engineers at IBM's research labs in upstate New York began building the machine--named for IBM's founder, Thomas J. Watson--with the goal of creating a question-and-answer technology that would be more authoritative and powerful than anything on the planet. The initial objective of the Watson group was simple: to win in the game show Jeopardy!, something Watson famously achieved in February 2011. Yet the group had a far more important goal: to turn Watson into a business, hopefully one of some scale. So starting in late 2009, a business development team at IBM began holding meetings outside the company in an effort to understand the ultimate worth of this new technology. No doubt it could be a business one day. But what kind of business?
"The first thing that hit us about Watson," recalls John Kelly, IBM's chief of research, "was that this thing could be applied almost anywhere." Early on, IBM executives decided to focus on a field in which Watson could have a notable social impact while also proving its ability to master a complex body of knowledge. The team chose medicine. They believed Watson could help doctors make diagnoses and, even more important, select treatments. Specifically, they thought Watson could be the perfect tool to chart the complex decision trees that cancer specialists like Kris negotiate every day as they weigh treatment options that might involve radiation, surgery, and any of countless chemotherapy drugs. Watson can ingest more data in a day than any human could in a lifetime. It can read all of the world's medical journals in less time than it takes a physician to drink a cup of coffee. All at once, it can peruse patient histories; keep an eye on the latest drug trials; stay apprised of the potency of new therapies; and hew closely to state-of-the-art guidelines that help doctors choose the best treatments. Watson never goes on vacation. And it never forgets a fact. On the contrary, it keeps learning.
Manoj Saxena is in charge of commercializing Watson for IBM. He calls it the most meaningful endeavor of his life. | Photo by Dan Winters
This fall, after six months of teaching their treatment guidelines to Watson, the doctors at Sloan-Kettering will begin testing the IBM machine on real patients. The Ms. Yamato app shows how it will work. After Kris inputs the results of her medical tests, Watson begins deliberating. "It's going through its algorithms," Kris says as we stare at the iPad. "It's seeing where the data sends it today." On the screen, a colorful globe spins. In a few seconds, Watson offers three possible courses of chemotherapy, charted as bars with varying levels of confidence--one choice above 90% and two above 80%. "Watson doesn't give you the answer," Kris says. "It gives you a range of answers." Then it's up to Kris to make the call. He regards the options on the screen and wonders how they might change if Ms. Yamato happened to develop a common symptom: hemoptysis, or coughing up blood.
"Let's try that," he says. He inputs the information and shows me the result approvingly. Watson has dropped one drug from the top chemo regimen. That's just what Kris would have done.
To make sense of all this--that is, to gauge both the value of Watson to a hospital like Sloan-Kettering and its potential to change forever the worlds of medicine and business--you could follow two different paths. You might consider Watson's evolutionary promise. Watson can almost certainly generate huge administrative benefits. Already, one large health insurer--Indiana-based Wellpoint--has begun using a Watson computer in its Virginia data center to speed along the authorization for medical procedures. Usually, authorizations are evaluated by a team of trained nurses and can sometimes take weeks to come through. Watsonizing the process would speed it up--a boon for a doctor like Kris, who now must wait while assistants exchange faxes with insurers before he can get clearance for any expensive tests.
Kris shows me what happens when Watson's treatment plan calls for an MRI. A button pops up on his screen to ask for preauthorization. "I just click that," he says, and it's done instantly.
I ask him what if Watson's request is denied.
Kris seems amused by the question. Watson has already consulted the latest medical literature, and it's been trained by the best cancer doctors in the world. "Who is the authority that is going to trump that?" he asks. Insurers balk at paying for unnecessary procedures; Watson's expert opinion essentially guarantees the necessity.
But the more intriguing path is the second one--a consideration of Watson's potential to do something revolutionary. This is the trail that captivates Kris. Eventually, he thinks, Watson could provide any doctor anywhere with the world's best second opinion. A physician in a community hospital in the Midwest, or at a remote medical center in China, could have instant access to everything that the medical field's best oncologists--people like Kris and his colleagues at Sloan-Kettering--have taught Watson. What is more, Watson will be able to excavate facts beyond the ken of Sloan-Kettering's current lineup of specialists. As Kris says, "We could ask Watson: What is the best treatment for this rare condition based on all of Sloan-Kettering's records?" It could then go through several years of cancer cases looking for the most successful outcomes. In time, it could even look at hospital records from around the world. As Manoj Saxena, the IBM executive now in charge of commercializing Watson, tells me: "It's like being able to take a knowledge worker--cancer specialist, nurse, bond trader, portfolio manager, whatever--and equip that person with the best knowledge, and have it available at their fingertips." As Watson evolves, Saxena believes, these knowledge banks will significantly alter how, and how well, humans make decisions.
Within a few years, for instance, Watson may be reaching well beyond oncology to assist patients suffering from any chronic disease and help general practitioners make diagnoses in their offices. Ultimately, Saxena believes, Watson could play an essential role in the diagnosis and treatment of mental health; in the financial services industry, where Citibank is testing it now; and in education. It could become the world's smartest dietitian.
Saxena now commands a team of about 200 people who are working to adapt Watson's skills for various IBM clients. He and I are discussing his progress over lunch one day near IBM's upstate New York headquarters when he leans back and tells me that after creating two successful tech startups, both of which he sold (the second to IBM), his current job is far and away the most meaningful endeavor of his life. Those startups, he confides, were exciting, important. "But this," he says of the Watson rollout, "this is stuff that is going to change the course of history."
Over the past year, IBM executives have come to believe that Watson represents the first machine of the third computer age, a category now referred to within the company as cognitive computing. As Kelly describes it, the first generation of computers were tabulating machines that added up figures. "The second generation," he says, "were the programmable systems--the mainframe, the first IBM 360, PCs, all the computers we have today." Now, Kelly believes, we've arrived at the cognitive moment--a moment of true artificial intelligence. These computers, such as Watson, can recognize important content within language, both written and spoken. They do not ask us to communicate with them in their coded language; they speak ours. And perhaps most important, they can learn, so they improve without constant human instruction. Siri, on the iPhone, might be considered an elementary example. Watson is industrial strength. "Computers do numerical calculations, they move data around, and they've been doing that forever," David Ferrucci, the IBM researcher who commanded the team that built the first Watson computer, tells me one day at IBM's research labs. "When I think about Watson, it's interpreting the information in human terms. It's saying: What does this mean to me? And that's a big deal." Also significant is how Watson renders an answer. Unlike its responses in Jeopardy!, in the real world it will perform as it did for Kris at Sloan-Kettering--by giving not a single solution but a range of probable solutions, each backed up by Watson's evidence and ranked by its level of confidence. In the lingo of computer science, that makes the machine probabilistic rather than deterministic. One might say this trait gives Watson a humanizing glow of humility and diminishes concerns that it marks a stride toward a computer-led dystopia. Watson, in IBM's marketing schema, is here to help with our questions, rather than solve them. In the case of medicine, it--for Watson is not really a he--is here to support doctors, not replace them.
The Watson of today is not precisely the same machine that won in Jeopardy! IBM has fine-tuned its software and algorithms for medical applications (or, in the case of Citibank, financial services applications). Watson has shrunk, too, from a row of about a dozen server racks that would have filled a small bedroom to an assemblage about the size of a double-door refrigerator. But for all the concentrated power, it doesn't look like anything special. Its sleek black servers are standard IBM Power 750s. You could wander around Watson and regard its blinking lights, as I did on a quiet midsummer afternoon at IBM's research labs, and not think something unusual is happening inside it. But there is. The way Watson solves problems--or, rather, the way it looks for answers, simultaneously sending out thousands of inquiries in all directions and then scoring the evidence it collects--is different from how other computers work. One person at IBM likens Watson's process to (1) gathering hundreds or thousands of possible solutions from a vast data bank, (2) pouring them into a giant funnel, (3) stirring with a dash of algorithms, and (4) letting only the best drip out of the bottom.
At the moment, a half-dozen Watsons are scattered around the country. Some are on the premises of IBM clients, as with the insurer Wellpoint, while others are cloud based, which is how hospitals such as Sloan-Kettering will access Watson. "Effectively, there's no limit to how many Watsons there can be," Bernie Meyerson, IBM's VP of innovation, tells me. Watson is a creation of software, not hardware. "That's the beauty of it," he says.
Watson is different from big servers and mainframes in other ways, too. The best computers of today have the extraordinary processing power needed to create, say, complex supply chains for building a new automobile or planning a satellite launch. These machines are good at manipulating the vast amounts of clearly defined data--numbers and facts--known as structured information. But most of the world's information is more ambiguous and less precise and lies beyond their reckoning. "We now have this proliferation of what we call Big Data," Saxena, Watson's business manager, tells me, referring to the flood of information created by our computers, our electronic sensors, and ourselves. "Ninety percent of the world's information was created in the last two years," he says. "But 80% of that 90% is unstructured or semistructured information, like doctor's notes or product reviews on Amazon." This near infinitude also includes tweets, blogs, emails--all the noise and scribble of modern life. So any company that aspired to manage the data of all the world's businesses would today be able to analyze only a small part of it. Watson, though, is a genius at reading unstructured information. And it's precisely this facility that explains why IBM sees such a rich business opportunity here.
It likewise explains why medicine is a logical first choice. While some health information is indeed structured--think of blood-pressure readings or cholesterol counts--the vast majority is unstructured. This cache includes textbooks, medical journals, patient records, and nurse and doctor evaluations. In fact, medicine embodies so much unstructured information that its proliferation has, by the account of many medical professionals, far outstripped the ability of doctors to keep up. Neither better training nor continuing education could ever wholly remedy this problem. When I meet with Herbert Chase, a professor of clinical medicine at Columbia University who consulted with IBM during the early stages of the Watson project, he says it is "not humanly possible" for a busy doctor to keep abreast of the current literature.
One result of information overload is a high rate of misdiagnosis and consequently incorrect treatment. By some estimates, Saxena tells me, 20% of initial diagnoses of cancer are eventually altered. "Imagine the implications of cancer care if there is a one in five chance that for the next six months whatever therapy they're giving you is wrong," he says.
Deciding on a course of treatment is even tougher than making a diagnosis. "It's still possible for a doctor to know the ways that people get sick," says Chase, who is also a kidney specialist. "But what is unmanageable, and what has been for decades, is knowing what the best option is today." Some applications now available to doctors are meant to alleviate this problem; one popular web-based tool is named Isabel. But Watson, in Chase's view, reaches a different level of sophistication. "I'll give you an example of a test we thought up for Watson," he tells me one day in his Manhattan office. "A patient was pregnant, had Lyme disease, and was also allergic to penicillin. And Watson came up with a drug. The first thing I thought was, Watson made a mistake. That drug can't be given to someone allergic to penicillin." But Chase was wrong, not Watson. "My knowledge was about five years old," he says. "And in the past couple of years, all the muckety-mucks had reviewed all the studies and had concluded yes, you can give that drug to someone who's allergic to penicillin."
To Chase, this proves a point: If you're a patient, you don't want to believe your doctor doesn't know everything. But he or she doesn't, and can't. At its best, the dispensation of treatment is inefficient today. "At its worst," Chase says, "it's subpar, incorrect, wrong therapy," and doesn't reach the standard of care to which his profession aspires. "As you can imagine," he adds, "this is not something we like talking about."
Last year, IBM turned 100 years old, which sets it apart from West Coast counterparts like Amazon, Apple, Google, HP, and Microsoft--all younger and ostensibly the tech world's leading innovators. To delve into IBM's recent research, though, is to wonder if our perception of technological leadership sometimes suffers from the distortions of branding and familiarity. We use iPhones and search engines and laser printers every day. But IBM's technologies are lodged deeper within the infrastructure of daily life; you're tapping into them whenever you send an email, for instance, or log on to a website. IBM has been granted more patents than any other company in the world for 19 years in a row. Yet since getting out of the laptop business in 2004, it has not produced a single product that it sells directly to the consumer.
To understand how Watson figures into the company's culture of ideas, or to see how it represents the kind of large-scale innovation that arguably lies beyond the capabilities of any startup, it helps to understand what the company actually does these days. IBM has operations in 172 countries and an organizational chart that resembles a vast Soviet bureaucracy. It employs about 433,000 men and women. Though IBM still sells hardware--big mainframe computers, silicon chips, and supercomputers--mainly it makes money selling software and consulting services to businesses and governments. The company's strategy has been validated of late by its performance: IBM's stock price has been on an upward trek for the past five years, and its winning streak has attracted the likes of Warren Buffett, who last year decided the company merited an investment of $10.7 billion. Meanwhile, as one of the few global titans to invest staggering sums on R&D ($6 billion to $7 billion a year), IBM maintains one of the world's last great industrial laboratories. At its main research center in Yorktown Heights, New York, a jet-age dream of glass curtain walls and rusticated stone designed by the Finnish-American architect Eero Saarinen, IBM employs the bulk of what is likely the world's largest mathematics department, with 300 members. If you're looking for a new PC design, you're out of luck here. But if you're shopping around for a new or better algorithm, IBM can build you one. Not everyone is impressed by the direction of IBM's management. A relentless focus on earnings and cost cutting has led to a significant offshoring of domestic jobs, and a vocal corps of disillusioned or laid-off IBMers regularly take to the web to lament that the company's best days are behind it. IBM has also had its share of technological stumbles, apparently bungling several high-profile government contracts in recent years (in Texas and Indiana, for example) that left the company embroiled in disagreements with unhappy clients. And though these flare-ups may be uncommon, the company otherwise rarely quickens the pulse, with a long-standing reputation for being slow, steady, reliable, and maybe a little dull. IBM doesn't have big growth spikes or ballyhooed product launches; rather, it has plodding, long-term client contracts built around its ability to help optimize, say, a company's global IT services or a public utility's electrical grid. The corporation moves along like a supertanker. "IBM's annual revenue base is huge--$100 billion," says Toni Sacconaghi, a technology analyst for Sanford C. Bernstein. "So to move the needle is tough. It's hard to find big new products."
The managers and engineers keep looking anyway. One way IBM tries to infuse the troops with a sense of mission is through its periodic attempts to create for itself a Grand Challenge, such as the construction of Deep Blue, a chess-playing computer, or, more recently, Watson. The Grand Challenges are focused and expensive efforts--IBM will not verify Watson's cost, but estimates put the sum between $100 million and $1 billion--to push the company beyond the competition.
Watson's origins can arguably be traced back some years to a more modest annual initiative IBM calls the Global Technology Outlook, or GTO. Anyone at IBM can contribute to the outlook, and most of the results are eventually made public. The GTO tries to identify future business opportunities by putting a spotlight on various technology trends. A while ago, the IBM outlook pointed to analytics as a potentially huge field. Not long after, then-CEO (and current chairman) Sam Palmisano green-lighted IBM's acquisition of about $16 billion in smaller companies that had computer technologies to do this kind of work--essentially, to comb through vast stores of data, both structured and unstructured, and help extract nuggets from the global corporate babel.
Like Big Data or cloud computing, analytics is one of those contemporary catchphrases that everyone talks about but no one pauses to define. Bernie Meyerson, IBM's VP of innovation, argues that the great promise of analytics is not just to spot trends or glean information for boosting sales but to use computers and software to change the future. "Analytics is the capability to see what no human can," he says. Recently, at a public event, Meyerson was asked if IBM missed out by not building a tablet to compete with the iPad. He responded that as part of its Smarter Cities Initiative, IBM had just spent several years gathering all of the data on car transportation in Singapore; it then fed the data into a model it had built to predict the time and location of traffic jams. "We know from history what happens in Singapore if you slow the lights down in one direction by three seconds, and how to tweak the model so the jam never happens," he told his questioner. "And so there will be a traffic jam that never occurs because we can predict what happens 20 minutes from now, because we can take enough Big Data and crunch it, and do analytics on it. So we're predicting the future, and changing it. And you're asking me if I'm worried about a tablet?"
Watson, too, fits into Meyerson's conception of analytics, though it aims to change not the future of a traffic jam but of illness and investing. And by all indications, that tantalizing promise is not lost on the business community. "I have my shoulder against the door," Saxena tells me. He means he is turning clients away--something I heard from several other sources, too--until IBM executives feel confident Watson has proved its credibility at places like Wellpoint and Sloan-Kettering. Saxena seems certain that Watson will be a multibillion-dollar business, though he will only go so far as to say that by 2015, IBM will have annual revenues of about $16 billion from its analytics portfolio, of which Watson will be a part. When I put the question of Watson's potential to John Kelly, IBM's chief of research, he says: "It's like asking, at the very beginning, How big will the PC industry be?"
Kelly notes that the business model for Watson is still to be determined. He isn't sure whether selling Watson as a computer or marketing it as a service will make the most sense. But he feels he has time to decide. None of IBM's competitors, more than a year after the Jeopardy! victory, has announced a Q&A technology like Watson. "I think we have a huge lead," Kelly tells me. "When people realize this is not a one-off game machine but a new era of computing, then you'll see other companies tripling down to catch up."
I asked a number of people, both within IBM and outside of it, whether other organizations could have built this machine first. The consensus was probably not. The reasons did not precisely connect to IBM's technological capabilities--Google and Microsoft have plenty of computer prodigies in their ranks too. Rather, it was the combination of assets at IBM that made the difference. The company had its vast corporate lab, huge sums it was ready to invest, a profound expertise in hardware as well as software, and a collaborative culture that brought in lots of help from academia. And crucially, it had its business clients. In this respect, being a company that doesn't cater to consumers has advantages. Watson is only as bright as its teachers. Without the staff at Sloan-Kettering, where doctors like Mark Kris teach it oncology, Watson would not be nearly so smart. In fact, it might be kinda dumb. Or it might get all sorts of things wrong, like Siri does, except you'll be looking not for a pizza parlor but for a tumor.
From the start, the team that originally built Watson under David Ferrucci has worked out of a big room on the second floor of IBM's Hawthorne Labs in Westchester County, New York. Hawthorne is a large glass cube of a building situated about 30 miles north of New York City. Inside the Watson work space are five fake wood-grained tables, each home to a group of computer engineers who sit around and alternately immerse themselves in their screens or break to discuss coding with a neighbor. The mood here is sober. The staffers bring water bottles, not junk food. These aren't the unlined faces you'll see at a startup. Indeed, Ferrucci, who sits off to the side, is a suburban dad who looks like he'd be just as comfortable standing in front of a grill with a basting brush as he is overseeing his team. The walls here are covered with huge whiteboards crammed with the hieroglyphics of computer science. Overhead lights cast the room in gloomy fluorescence. The place has the neglected feel of a finished basement in a 1970s-era subdivision.
In early fall, the Watson team, now about 45 strong, began moving its work to a gleaming new space in IBM's main Yorktown Heights research laboratory--a promotion that reflects their importance as they support Saxena's much larger business development group while simultaneously working on the next iteration of Watson, known as Watson 2.0. One of the team's goals is to make Watson adaptable enough so that it doesn't require several dozen people spending a year to get it ready for every new application, such as medicine or financial services. But a more immediate project is to help Watson through the U.S. Medical Licensing Examination, the complex test all med-school graduates must take before practicing. If it passes, says Ferrucci, "that doesn't mean I can have a computer be a doctor." But IBM would gain what he calls "a crisp metric" that proves Watson has a real proficiency in medicine. The credential would no doubt help Watson's standing with health insurers, doctors, and patients, too. Passing the licensing exam is a difficult task--far harder than winning at Jeopardy!--but in early September, Ferrucci seemed pleased by the results. The computer is doing "interestingly well," he said. He sounded confident that Dr. Watson will ace the test by year's end.
Harder to intuit is how soon afterward Watson will infiltrate society. When I ask Jaime Carbonell, a computer science professor at Carnegie Mellon, he says he has no doubt the impact of Watson will be significant. "But I don't think there will be one moment of, 'Now we have it and yesterday we didn't,'" Carbonell remarks. "It will take time to permeate. Like cell phones, which were big, clumsy things you could barely carry at first." Was there a year, or month, or day, he asks, when cell phones began to change the world? "I can't think of when that was," he says. "But now we can't do without them."
Such is the course of technology: Electronic tools initially available only to the elite grow ever faster, smaller, cheaper. Kelly tells me he believes that eventually Watson will shrink to the size of a handheld device. Randy Katz, a computer science professor at UC Berkeley, sees a more approachable Watson, too. "Can the person in the street ask Watson a question now? No, he can't," says Katz. "But in five or 10 years, will there be systems like that--like Siri, but much better? I think the answer is yes."
In many of my conversations at IBM, the talk often drifts to applications of Watson. All sorts of intriguing scenarios are presented to me--for instance, that Watson will soon analyze not just words but images, such as MRIs and EKGs. Or it will diagnose a spider bite on a child's arm in a crop field in Africa, transmitted via smartphone by his worried father to a U.S. hospital. One afternoon, Saxena suggests this one: When you think you're coming down with the flu, Watson will be able to discern, before you even arrive at the doctor's office, that it might be a ragweed allergy, based on your medical record (you've had the same symptoms twice before at this time of year); your symptoms (gleaned from the insurance claim and diagnostic information in journals); and recent news (it just read an article in the Austin-American Statesman on a ragweed outbreak near your hometown).
It all sounds amazing. It's also speculative. Watson has not yet saved a life or a dollar of medical costs, or added anything, really, to IBM's bottom line. It has not yet faced its resistors--doctors who may find the technology objectionable and slow its adoption. It has not yet, as Saxena believes it will, changed the course of history. It has only won a television game show.
In the seven years since Google Analytics launched, the realm of analytics has changed hands from the purview of the IT department to the marketing department and beyond. But just because we can readily access the tools does not mean that we are necessarily putting them to good use. To gain some insight into this we interviewed Brian Clifton, author, consultant and trainer, who specializes in performance optimization using Google Analytics.
Brian acted as head of Web Analytics for Google Europe from 2005-2008 and recently released the third edition of his "Advanced Web Metrics with Google Analytics," used by students and professionals worldwide.
CMSWire: Do people collect data that is useless or unnecessary?
Brian Clifton: No, and let me say that I don't think collecting *any* kind of data is useless. However, it can be distracting and generate a higher level of "noise." The mistake I see most often is people not focusing i.e., they simply look at all of the data all of the time. Beyond the high level reporting of metrics such as total visitor counts, total revenue etc., looking at all of the data all of the time has very little analytical value. Therefore, what you need to do, is chop up the data into meaningful groups of similar data points i.e., segment.
Example segments to enhance the signals from the noise:
Customers
A segment that includes only visits from existing customers — even if they do not purchase from your site when they visit. This is why this segment is so valuable — these visitor have bought from you before!
Engagers
After customers, Engagers are your next most valuable group of visitors. They have shown engagement, i.e., made an effort, perhaps even given you their contact details. Examples include registrations, contact requests (form submissions), file downloads, ranking products and content, comments and feedback, video views, clicks on social love buttons etc.
Mobilers
A segment that includes only visits from mobile devices. Mobile is a very different experience to browsing on a bigger screen. Lots of implication for your web site structure/design if this segment is significant to you.
Non-Bouncers
A segment that excludes all bounced visits i.e., removes single page visits that have no engagement. The theory is that these visitors are clearly not interested in your website, so remove them from your analysis.
Of course these segments are not mutually exclusive and you can be very creative by combining them.
My point is that data "noise" is not something to be discarded. It's only noise relative to a specific question you are trying to answer at a particular time. When you look at answering a different question, and that noise can be a rich data stream indeed (people have won nobel prizes for analyzing noise!). Therefore, always collect the full spectrum of web data and user segmentation as your tool for focus and analysis.
Editor's Note: To get more insights from Brian, follow him on Twitter @BrianClifton
CMSWire: How can a company decide which data to collect? Are there common metrics/strategies that work across the board or does it change on a case by case basis?
BC: My approach when I first engage with a client is not to show or look at any analytic reports. I usually get an odd response to that when I have been hired as the web analytics expert! However, the first meeting is a workshop where I sit down with the marketing team to understand their needs and abilities. By needs, I mean understanding what tasks they perform as a group and therefore how they can measure the success (or not) of that. A metric that measures success is known as a Key Performance Indicator (KPI).
The average consumer in 2012 is very different than the average consumer 10 years ago. For that matter, consumers today are a lot different than they were 10 months ago. Empowered by technology and with instant access to vast quantities of information, people have new rules for shopping, communicating and even relaxing. The bottom line is that consumers are now data-driven, which means marketers have no choice but to dive even deeper than their customers and competitors into the information goldmine.
Tweets, in-store purchases, QR codes and other tags, online searches, Facebook posts: all of these (and many, many other interactions) leave digital information trails that tell us how consumers interact, what they consider important and more. These sources add up and create what’s called “big data,” a repository that last year added up to 1.8 zettabytes of information in the U.S. alone.
But how do you harvest this information and analyze it to make marketing decisions?
Real Time Responses to Complex Data
The answer is Integrated Marketing Management (IMM). Since customer behavior is cross-channel, cross-business-unit and cross-device, the IMM approach uses software solutions to combine all traditional and digital channels — even departments beyond marketing (sales, IT, etc.) — into a single, cohesive effort.
IMM provides a path to increased revenues, customer responses and relevancy by aggregating and analyzing all available data to nurture the customer journey with personalized offers and real-time responsiveness.
A great example of these analytics in action comes from International Speedway Corporation (ISC), the world leader in motorsports entertainment. This company used IMM to transition from old-school mass marketing to targeted, innovative and compelling fan experiences both on and off the racetrack.
While many marketing analysts regard NASCAR enthusiasts as the most brand-loyal fans in all of professional sports, ISC had not been able to leverage that devotion. The company had transactional information from its 12 racetracks, but the data was not analyzed effectively or translated into marketing strategy. As a result, ISC was losing out on valuable opportunities to create customer touch points and drive purchasing decisions.
Racing Towards Customer Insights
All that changed when ISC revamped its marketing analytics by replacing outdated collection methodologies and built a program with companywide buy-in. ISC started out by spending an entire year analyzing customer trends. This new data empowered the company to create thousands of unique customer segments, each with its own behavior pattern. Using this analysis, ISC developed all-new dialogue strategies, ranging from drip marketing and target up-sells to new loyalty programs and prospect nurturing.
The results of this data-driven, customer-centric approach were extraordinary. The first year after implementing its new program, ISC achieved a 153 percent of its prospect collection and purchase conversion goal.
According to Jim Cavedo, the company’s senior director of consumer marketing,
The more data you can get into your CRM system and the more data you can use to help guide your decisions, the smarter you’re going to be, the more effective your marketing spin is going to be, and frankly, the more valuable your communications are with your customers. That lets them say, ‘Alright, I’m going to engage with you because clearly you know who I am, and you’re communicating with me the way I want you to communicate with me.’”
There is only one certainty about the future of the marketing industry: marketers can no longer be grounded in selling to large, mostly amorphous demographic groups. While marketers have no crystal ball, we know that the customer is now in control more than ever before, and the old way of doing things won’t cut it any longer.
It's a busy time in the document management world. The Microsoft Exchange Conference returns after 10 years as Microsoft gears up for the new Office 2013 release, HP uses Autonomy’s IDOL for information governance, AvePoint offers governance in SharePoint, FileTrail extends SharePoint’s records management, and Alfresco releases a new synching tool for enterprises.
Microsoft’s Exchange 2013 Features
There are a number of conferences on, but one of the more interesting ones is the Microsoft Exchange Conference, which makes its return after a 10-year absence.
It’s not really surprising that it should reappear now, given the developments around Office 2013 and Office 365, but this is specifically about Exchange. So far, with only one day of talks over, the whole emphasis has been on the new features that are due to come in Exchange Server 2013, as well as those that will appear in Exchange Online and Office 365.
In an Office blog post published this week Harv Bhela, General Manager for Exchange Program Management with Microsoft outlines some of his thoughts on it, and talks about some of the features that will appear in both online and on-premises versions of Exchange (even if these are well known already).
Little in relation to this will be much of a surprise, as the public beta was released in July and the private beta earlier in the year.
The first day of the conference focused on the security and compliance features that will come with Exchange.
According to Bhela, the data protection and archiving features look to take Exchange into the Big Data world. Some of the features that aim to do this include:
Cloud-based email security with Exchange Online Protection
Monitoring, protection and identification of sensitive data with Data Loss Prevention technology
Email archiving, hold and native data governance
New e-Discovery tools to locate information
Integration with SharePoint and collaboration with site mailboxes
Meanwhile, Microsoft (for some reason) still won’t confirm release dates for Office 2013 or Exchange Server 2013, but a good guess is Spring 2013. If you’re interested in more on the Exchange conference in Orlando, check it out here.
HP’s Information Governance with IDOL
One of the more interesting announcements this week is the one from HP about the use of Autonomy’s IDOL in the management of structured and unstructured data.
It’s just over a year since HP bought Autonomy for US$ 10.2 billion. While HP has been adding Autonomy’s IDOL to some of its software, it never really gave the impression that it had a fully developed IDOL integration plan.
This release, though, looks very much like an information management strategy that sees HP really pushing the boat out as it looks to try and build on that acquisition. Interested in more?
AvePoint Compliance For SharePoint
But it is not only HP that has been issuing releases around information governance this week. AvePoint has also been busy and announced the availability of its DocAve 6 Service Pack 1 (SP) that provides governance for SharePoint.
In recent years, an increasing number of startups and big-name companies have looked to celebrity backers to boost their brands and street cred. Here, four questions to ask your celebrity investor before taking the plunge.
Lady Gaga, Justin Bieber, 50 Cent, Ashton Kutcher: A-list celebs, sure. But they're also members of a Who's Who of new-age venture capitalists and product developers. That is, at least according to public perception.
In recent years, an increasing number of startups and big-name companies have looked to celebrity backers to boost their brands and street cred: investors in Airbnb and Spotify include Hollywood stars and chart-topping artists; Beats Electronics has created headphones for Diddy, David Guetta, and Lebron James; and Justin Bieber recently graced the cover of Forbes under the the headline, "Venture Capitalist." But what they get depends on how much these celebrities are actually involved in their investments. What makes celebrity investment better than a traditional celeb endorsement of, say, McDonald's or Pepsi?
For a number of reasons--the generally sensitive nature of investments, sharp-elbowed celebrity publicists, and so forth--rarely does the public get insight into this area. But certainly the perception, at least in the press, is that certain celebrities have an innate business sense. Speaking with a range of entrepreneurs in the space, we look to see whether this reputation is warranted, or whether it's just Hollywood marketing 2.0. Below, a cheat sheet for you to tell the difference.
For most entrepreneurs, it all comes to what celebrities represent the right fit for their startups, and whether they are authentically interested in the products themselves. Sure, popular celebrities can bring much attention to a startup, but they have to engage with a startup's products in order for the relationship to be effective. For one entrepreneur, with backing from several A-list celebrities, it's incredibly important that a celebrity actually believe in a product before investing in it. For example, the entrepreneur says, the celebrities interacting with the product, whether on TV or elsewhere in the media, brings an attention that is unrivaled so long as it's genuine. "There's kind of an intangible value to that," the entrepreneur says. "That's something you almost can't put a price on."
Robert Brunner of design firm Ammunition LLC, which has notably developed the Beats by Dre headphones, agrees that authentic involvement is crucial, though acknowledges it varies from celebrity to celebrity. "It always works best when the celebrity is involved for reasons other than economics--where there's an actual passion there. With Dr. Dre, he's been fairly involved with the physical design, and really gets involved in the tuning and the sound. This is personal to Dre; it's a reflection of him," Brunner says. "Some other [celebrities] though, well, I've never met, and only get feedback through three levels of channels."
One startup founder, who has myriad celebrity investors, argues that a celebrity's involvement must be mutually beneficial to both brands. "You don't want someone making an investment just like they're selling some new bottled water," the founder says. "It's not like signing a deal with Pepsi where you have to do three events and a commercial. Are they really emotionally invested--and not just financially invested--in the product?"
In that sense, it doesn't matter how many followers a celebrity has on Twitter or how much engagement they could potentially create. If they're not the right fit for your startup--and your product--then it won't matter. In other words, microloan platform Kiva might not want Charlie Sheen as an investor. Inversely, Lindsay Lohan might not have been the right fit to have invested in enterprise social network Yammer before it was acquired. "[Celebrities] probably shouldn't go investing in productivity tools," the founder jokes.
Are you starstruck?
Talk to entrepreneurs about celebrity backers and you'll inevitably soon be talking about press attention. "If you look across TechCrunch once a day, you're likely to see at least five companies with big-name angel investors," the startup founder says. "But I don't feel it's all that useful to have a name--to just have a vanity investor. It's not just a matter of getting any celebrity X, Y, Z."
Brunner too contends that it works much better when "it's more than just a name play," when it's not just "hollow celebrity branding." He cites doing design reviews with Pharrell and Lady Gaga, who get involved with the product development to provide feedback and inspiration.
"Name recognition hasn't meant anything to us," says the entrepreneur with A-list backers--a celebrity endorsement can't mask a poor product. The product has to be able to stand on its own.
Having celebrity investors is beneficial beyond what headlines their names can generate, most argue. "They're involved in the product itself, the decisions we're making, giving us input on what they think we should be doing across the board," the entrepreneur says. The startup founder agrees, explaining that celebrities are also very "helpful with introductions, with campaign and promotional ideas."
Is Kim Kardashian really the New Reid Hoffman?
Still, as much as celebrities might boost engagement among fans, provide feedback and marketing prowess, as well as generate media buzz, the fact remains that celebrities are not exactly venture capitalists or product gurus, at least in the traditional sense. Sure, Bieber might be slated a new-age VC, but as the startup founder told me, "It's like he understands liquidation preferences or how to structure a term sheet."
So be warned: Don't expect Kim Kardashian to lead your Series B round. But also remember that celebrities entourage--for every Vincent Chase, you're likely going to get an Ari Gold. "They have smart managers, smart lawyers, and smart agents--they're looking to bet on companies and categories that are going to be winners," says the startup founder.
Have you considered the downsides?
One source deeply involved with the celebrity investment community describes the downsides of working with artists and actors: "The problem is most of the higher-level celebrities do not know or understand good design. They kind of know what they think is cool or looks cool, but it doesn't necessarily translate into good products. Another thing dealing with musicians is that in the music industry, you can change a recording until the last second it's published. You really can't do that on a product. So the problem I constantly run into is people wanting to go fuck around with this stuff late in the process which is just catastrophic on a development schedule."
If your office is a meat locker in the summer and a sauna in the winter, your employees' productivity and collaboration suffer--probably more than you think.
Some years back, the Campbell Soup Company stumbled upon a marketing insight worthy of Don Draper.
If you want to predict when people will buy soup, the reasoning goes, you have to look beyond the product. It’s not about the depth of the soup’s flavor, the color of its packaging, or even its price. In fact, it’s hardly about Campbell’s at all.
It’s about the weather.
Consumers buy more soup when conditions are cold, damp, or windy. The question facing Campbell’s was this: How do you leverage this information into sales?
So they did something brilliant. They linked the frequency of their radio buys to the weather of each station. To determine when ads would be purchased, they developed an algorithm called the “Misery Index,” which uses meteorological data to track weather patterns. To this day, if you’re hearing an ad for soup on the radio, there’s a good chance you’re either carrying an umbrella or wearing a coat.
The rationale behind Campbell’s Misery Index is simultaneously clever and obvious, a hallmark of game-changing ideas. But it also raises an interesting question.
If a drop in temperature changes what we buy, what does it do to the way we think?
Typing With Gloves
If you sit near a vent, share legroom with a space heater, or use your desk to store outerwear, the question warrants serious consideration. One of the painful ironies of office life is that we can never quite get the temperature right. We spend our summers shivering in meat lockers and our winters sweating in saunas.
Central air hasn’t made us comfortable, so much as made us uncomfortable in a different way.
The experience isn’t simply unpleasant. It comes with a real financial cost.
To find out just how much, Cornell University researchers conducted a study that involved tinkering with the thermostat of an insurance office. When temperatures were low (68 degrees, to be precise), employees committed 44% more errors and were less than half as productive as when temperatures were warm (a cozy 77 degrees).
Cold employees weren’t just uncomfortable, they were distracted. The drop in performance was costing employers 10% more per hour, per employee. Which makes sense. When our body’s temperature drops, we expend energy keeping ourselves warm, making less energy available for concentration, inspiration, and insight.
Feeling Cold? You Might Just Be Lonely
And it’s not just performance that dips. It’s our impression of the people around us. In a fascinating study reported in the prestigious journal Science, psychologists uncovered a link between physical and interpersonal warmth. When people feel cold physically, they’re also more likely to perceive others as less generous and caring.
In a word, they view them as cold.
When we’re warm, on the other hand, we let our guard down and view ourselves as more similar to those around us. A forthcoming paper from researchers at UCLA even shows that brief exposure to warmer temperatures leads people to report higher job satisfaction.
Why the link between physical and mental warmth?
Psychologists argue it has to do with the way we’re built. The same area of the brain that lights up when we sense temperature--the insular cortex--is also active when we feel trust and empathy toward another person. When we experience warmth, we experience trust. And vice versa.
Neurologically, it seems we have our wires crossed. Except it’s not a coincidence.
There’s a reason we associate warmth with trust, and it’s because doing so promotes our survival, especially early on. As infants, keeping close to our caretaker is vital to staying alive, which is one reason we’re programmed to seek out warmth. Throughout our lives, we associate warmth (a hug) with affection (this person loves me). It’s a connection that grows stronger with every intimate embrace.
Why Lonely People Take More Showers
Because our minds unconsciously link warmth with affection, we’re more sensitive to cold temperatures than we think.
Research shows that when we experience cold temperatures, we’re especially likely to feel isolated. In fact, countering the experience of isolation is one reason people spend more time in the shower when they’re feeling down.
The unconscious desire for physical warmth is thought to be the reason lonely people bathe longer, more frequently, and use higher temperatures.
The Warmth-Productivity Link
We know that cold temperatures worsen productivity. What new research is showing is that it can also corrode the quality of our relationships.
And this, ultimately, is why office temperature matters.
Great workplaces aren’t simply the product of good organizational policies. They emerge when employees connect with one another and form meaningful relationships that engender trust. What’s often overlooked is that connections don’t operate in a vacuum.
It seems obvious that the temperature of a restaurant or theater can alter our experience. So why do we continue to neglect it in the workplace?
It’s been thirty years since John Naisbitt published his landmark book, Megatrends, exploring a number of major changes in society, ten to be exact, likely to impact the way we live, work and govern ourselves. Looking back across those three decades, it’s clear that Naisbitt got a lot right and a few things wrong about the information society whose arrival he recognized.
What may be useful to us, however, are the trends he saw in the early 1980s, the variables he expected to shape and balance the resulting changes and the reasons some of those variables didn’t behave as he predicted.
Industrial to Information
First, to his credit, Naisbitt saw and made his first chapter about the rise of an information society in place of the industrial society we had been living with since the end of WWII.
Although the personal computer had only just made its appearance in the late 1970s and while many commentators were still grappling with the end of the industrial age, seeing nothing coherent on the horizon, Naisbitt recognized that information would become the critical resource and wealth in a new “information society.” He reasoned that society would need a new knowledge theory of value to replace earlier labor-based theories because, he also reckoned, we would “mass produce information” the way we used to mass produce hard goods.
He was right of course, and we are mass producing information, or at least data, in volumes that even he couldn’t have conceived. However, the fact that so many information utilities, social media and search engines most notable among them, still rely on advertising for their revenue suggests that we haven’t quite yet solidified that new theory of information value.
Searching Isn’t Always Finding
Use a modern search engine and you will see that the creation side of the information equation is charging ahead full steam. But try finding a complex or subtle item of information on the Internet and you will also see a location side still struggling. As the glut of content mounts, the need for a comprehensive and universally understood way of identifying information so that it is easily available and not buried in a gazillion search engine hits, grows with it.
The brick and mortar library world addressed this problem as early as 1876 when Melville Dewey came up with his decimal classification system and in 1908 when the Library of Congress adapted Cutter’s dictionary cataloging scheme. The process of assigning classifications to content was further organized with the 1967 publication of the Anglo-American Cataloging Rules or AACR.
But with the rise of automation in the 1960s, the library world panicked at the thought of those new computer people invading their sandbox and much of the momentum and progress was lost, sending the content world essentially back to square one trying to figure out what an effective cataloging scheme should look like — working for Xerox Education Division back then, I witnessed some of this happen and it wasn’t pretty.
Complicating the process were multiple schemes, developed by different players with different perspectives, different funding, even from different parts of the world, but all convinced that they were right and none much interested in consensus.
The search engine world hasn’t helped either: after all, effective content cataloging reduces the need for their systems by providing pre-configured paths to information and if you don’t need to search the entire Internet to find information, you likely won’t see or respond to the ads that provide the lion’s share of Google’s (et al) revenue. So we spend most of our time searching through everything for tidbits of information that often exist only in a few places.
Walking the halls at ILTA it’s very clear how far the legal tech community has come. Wasn’t it just two years ago that vendors in the e-Discovery space were talking about how lawyers should get empowered to adapt new technologies into their workflow? And now, not only have they begun to adapt, they’re reshaping the landscape of legal tech.
This week, I’ve sat in on sessions that tackled practical issues of iPads v. laptops, wireless expectations and best practices for creating a mobile legal workforce. Even the conference itself launched a mobile app that helped attendees create their own agenda, submit session evaluations and network with each other.
By actively embracing and integrating cloud, mobile and social into legal technology applications, it’s hard to remember that it’s still about e-Discovery.
kCura and the Evolving State of Relativity
We had an opportunity to sit down with Nick Robertson, vice president, sales and marketing at kCura to talk about how the shift in the legal tech landscape is affecting how they approach case review.
Earlier this week, The Recorder placed kCura on its list of The Best Predictive Coding Solution Providers of 2012 (PDF) for its Relativity Assisted Review software. During ILTA, kCura offered demos of a few key components of Relativity — Assisted Review, Ecosystem, Fact Manager and Review Manager, which offer a variety robust application and workflow functionality.
According to Robertson, the legal community has become more educated about e-Discovery and its role in assisted review. But that doesn’t mean the challenge of managing big data has ceased to be. In fact, it’s only getting bigger as the volume of data collected increases, which means that there’s often more to review. But the increased data sets do give kCura the opportunity to make review smarter, faster and more efficient, by using text analytics search technology to identify similar documents to your case.
As well, kCura lets users create applications, without much programming experience necessary. Users can build applications designed to enhance the review, analysis, and production capabilities in Relativity. Furthermore, Relativity Applications can leverage Relativity’s APIs, allowing third-party software companies to integrate their technology with Relativity.
In fact, these advanced review capabilities are no longer only relevant to eDiscovery — they can also serve a broad range of IT trends, like records management, compliance and information management.
The Best Information Governance Framework is a Balanced One
Speaking of information governance, this week Iron Mountain released a new report, titled “A Proposed Law Firm Information Governance Framework.” The report, a product of a three-day working symposium convened in May, identifies best practices for information governance within law firms. Key themes of the report were presented during a panel discussion today called "Effective Information Governance Programs: Why Balance Matters."
How do you define Information governance?
It’s only recently that the legal industry has begun to embrace the merits of information governance (though none of the session attendees admitted to having a program already in place). Most firms generally consider information governance to be a way to manage information faster so as to stay out of trouble.
Despite the reluctance of many companies to seriously consider Bring-Your-Own-Device (BOYD) polices, Gartner says that in the coming years the development of such polices will create as big a shift in enterprise computing as PCs did when they first entered the workplace.
While that seems like a fairly dramatic claim, it needs to be understood here that Gartner is not saying that every company will allow employees to bring their own device, but that in terms of enterprise computing companies are going to have to make strategic allowances for it.
Enterprise BOYD Programs
To be clear, BOYD refers to company policies that allow employees, business partners and others use personally selected and purchased client devices, and to access enterprise applications using those devices.
The security implications and threats are clear. So much so that earlier this year we saw IBM decide against allowing workers to bring their own devices despite having started off with a BOYD policy that enabled them do this. IBM stated at the time that it was afraid that sensitive data would find its way outside the workplace and into the hands of competitors.
The result, in this instance, is that device use in IBM enterprises is strictly controlled, and smartphones and other devices that have not been vetted are off limits — including unapproved PCs.
Developing BOYD Policies
While this may appear to go against Gartner’s claim, which is contained in a research paper "Bring Your Own Device: New Opportunities, New Challenges" by David Willis, it actually falls in nicely with what Gartner is saying — that all enterprises in the future will have to develop BOYD policies.
Factors that are necessitating the development of such a policy include mobile innovation that makes even the most powerful smart devices affordable for most employees, as well as making upgrades easier and less expensive.
Rather than entire enterprises trying to keep up with the pace of change that this is encouraging, it will be easier for enterprises to develop a policy that enables their employees do this, while at the same time keeping data and networks safe from outside interference.
A typical BOYD approach would involve permitting users access rights to certain enterprise applications and information on personal devices, subject to enterprise security arrangements, demands and management policies.
Enterprises, for example, may provide a list of acceptable devices that the user can choose from. The IT department may then be able to offer partial, or even full IT support to users, along with full, partial, or no reimbursement to users that sign up to it.
Theoretically, everyone here is happy. The employees gets technology they want and are used to working with and get support for the work applications that they want to use. For enterprises that reimburse there will often be initial economic advantages as employees take up special offers from vendors that are only available to individuals, and enterprise security is guaranteed.
BOYD Drawbacks?
Except that according to Gartner it won’t evolve like this:
Just as we saw with home broadband in the past decade, the expectation that the company will supply full reimbursement for equipment and services will decline over time, and we will see the typical employer favor reimbursing only a portion of the monthly bill. We also expect that as adoption grows and prices decline employers will reduce the amount they reimburse,” Willis says.
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