Accenture Technology Labs Blog
Bold thinking, commentary and application of new technologies that address many of the key business challenges facing organizations today.
By:
Colin Puri A curdling scream and gasps can be heard from within the halls of the workspace. Hearing this, you jump into the nearest broom closet only to emerge dressed in a suit made of spandex, a bright red cape, and the letters DQ, short for “Data Quality,” emblazoned on your chest. Running down the hall, you find the commotion, quickly administer prompt justice, and vanquish the evil dirty data that is causing people to collapse and ball up into the fetal position. Taking a few glances at the data, you run it through the Accenture Data Quality Rule Accelerator and POOF! Once again, you are the hero!
Okay, so maybe that’s a bit dramatic. In all likelihood, you wouldn’t be wearing a cape; too often, those things get caught in pesky and annoying doors or cause a face plant into the ground. I do encourage you to wear a facemask though!
Data Quality initiatives aren’t quite as glamorous. In reality, you won’t be leaping tall buildings in a single stride, but rather copious amounts of data and information. You won’t be emerging from a phone booth with a gleaming suit on, but you may run to the broom closet in fear though. What I can tell you is that data is often dirty and often the amount of information that organizations have can be so overwhelming that tackling data quality issues can be difficult, costly, headache inducing, and may leave you with the desire to jump to other projects.
A recent survey of organizations found that most have yet to calculate the ramifications of poor data quality. So what does this mean? This means that most organizations don’t know what to do about the data that they have. When approaching a data quality engagement, those on the ground know how tedious, time consuming, and frustrating it can be to sort through the mounds of information. This only compounds the problem as it causes headaches and the desire of some to avoid all together for fear of spending even more money on an issue that some might see as a money pit.
Furthermore, many of the problems of data quality are no longer focused on names and addresses anymore. Dirty data is frequently encountered on engagements, it is pervasive, and data can be of such poor quality that 30%-80% effort spent in a data integration initiative is spent on data cleanup and understanding. The process of clean up and understanding involves interviews with subject matter experts if they are still around, trying to find documentation, reading all materials, and the manual discovery of and creation of data quality rules that can guide the process to scoring the cleanliness of data.
For this reason, I want to introduce the Accenture Data Quality Rules Accelerator (ADQRA). The ADQRA tool, currently in beta, is a part of the larger R&D initiative surrounding data quality and was created in our Technology Labs with the support of AIMS. The accelerator can take what would normally comprise of a significant amount of time and seed data quality efforts. The accelerator takes a data set and automatically returns with a set of data quality rules that can be used to pin point what data is dirty, which data isn’t, how dirty the data is, and can be help to determine how much effort is needed. The current version of the tool essentially detects inconsistencies in a given dataset, one of the six dimensions of data quality. The discovered data quality rules can then be used to enforce proper data entry or even discover interesting patterns. The ADQRA can do this with in a short amount of time because it is always checking for the stability of a rule as it encounters data. This means that it doesn’t need to look at the entire amount of data to determine a data quality rule. Results are returned relatively quickly and it is tolerant to dirty data.
Using the Data Accelerator is a three-step process: load your data, discover data quality rules, and then browse the rules.
In the current version of the Data Accelerator, data is selected and uploaded.
Once the data is uploaded, the user can tweak the results by manipulating several parameters:
- Maximum number of rules – specifies that the ADQRA should return no more than the specified number of rules and that once it discovers that number of rules to stop and return the results
- Maximum number of conditions – specifies the total number of conditions in a data quality rule where a rule is composed of conditional values on the left side of an “if-then” rule.
- Maximum number of seeds – specifies the of condition combinations
- Coverage – specifies the minimum amount of data a rule should cover for it to be considered interesting
- Error rate – specifies the expected error rate in the data
- Frequency – specifies how frequent the ADQRA checks for rule stability
- Window size – specifies the number of tuples to consider at a given time
Once the rules are discovered, they can be browsed, edited, deleted, approved, and then exported to a format suitable for use with Informatica.
If any of this is remotely interesting to you, then I encourage you to contact Accenture and Accenture Technology Labs about the Accenture Data Quality Rules Accelerator. Take it for a test drive and see how it can help you. With the ADQRA you too might become a data quality super hero (but please, leave the spandex at home).
By:
Alex Kass and Manish Mehta
What if work could be more like our favorite games? This question has been inspiring growing interest in bringing “gamification” to the workplace, which means leveraging games and game mechanics to help shape workplace behavior.
The idea that businesses might leverage the enjoyable – even addictive – power of games to engage and influence both consumers and employees has a powerful appeal. You can see this in the growing number of blogs (e.g., Gamification.co and Zdnet), books, and conferences demonstrate. This appeal has been further enhanced by the emergence of gamification software vendors, such as Badgeville,and Bunchball, who make it relatively easy for an enterprise to get started with gamification. Technologies now exist to help “gamify” existing applications, processes, and interfaces by weaving in game mechanics such as reward points, leaderboards, badges, and the ability to ‘level up’. Enticed in part by the onramp these vendors have created, many companies are exploring gamification, and some, such as LiveOps, have made gamification of work a core part of their service delivery model.
The same techniques that keep Farmville players working their plot seem to have real application in the workplace as well, but how broad and deep are the potential impacts? Is it limited to providing employees with small nudges to perform chores like turning in surveys, filling out expense reports on time, perhaps even moving customers through a check-out line a bit faster? Or is gamification something that can address deep-seated and complex behavior change? Can it be used to help change beliefs and priorities? Can it be used to promote behaviors which will take intensive effort to learn, or which employees don’t even realize they need? And can games be used to create change that lasts even after the game is out of the picture?
The answer to all these questions may well be yes, but it will take an expanded repertoire of gamification techniques, specifically designed to address a broader swath of the behavior-change lifecycle: One key insight from the literature on persuasion and behavior modification on a range of topics from cancer to computing is that behavior change often follows a stereotyped pattern of stages and that each stage presents different challenges. The stages in this behavior-change lifecycle each pose different kinds of behavior-change challenges that must be understood if we’re to create game-based techniques to all the stages.
Here’s a 5-stage version of the behavior-change lifecycle which we have adopted from the literature:
- Raising awareness: Understanding exactly what the as-is behavior patterns are, and recognizing that there is opportunity for improvement.
- Building buy-in: Committing to the commitment of time, energy, and resources, needed to execute the change.
- Learning how: Understanding the mechanisms and techniques that underlie the target behaviors
- Initial adoption: Trying out the target behaviors, getting used to actually executing them.
- Maintaining and refining: Perfecting the new behaviors through extended practice so that they become they eventually become self-sustaining.
Most gamification we have seen focuses on Stage 4 and, to a lesser extent, stage 5: promoting the initial adoption, and then the maintaining of target behavior patterns. Stages 1-3 are often all but ignored. This is fine in situations where raising awareness, building buy-in, and understanding the basic mechanism of the target behaviors are not crucial issues. In some cases, merely motivating initial adoption of behaviors is sufficient to generate buy in because the initial effort is not too great and the advantages become self-evident once the behavior is adopted. For example, if you can motivate someone to exercise for several weeks, he might start feeling good about himself, start enjoying the activity, and thereby start exercising on a regular basis.
But in many more challenging behavior-change scenarios, stages 1-3 play key roles in an effective behavior-change program. Consider some examples: An employee who uses a condescending tone with customers may not even realize they are doing so, which would mean that raising awareness of the problem is a first, crucial step toward sustainable behavior change; an employee who is too blunt or cursory with colleagues may not buy into the need to provide more tender-loving care, because there is no explicit connection made between that behavior and the morale or retention problems that it causes; an employee who does not understand the mechanisms for carrying out a new business process will be unable to respond to incentives to execute the new process – regardless of how well game mechanics are used to provide that incentive.
One game mechanic that can be effectively used for achieving buy-in is cause-and-effect game simulation. These simulations can help raise awareness of the impact of the user’s existing behavior patterns and the need for change. A simple example of such a simulation is Stone City game commissioned by Cold Stone Creamery, in which employees learn to scoop the right portion-size ice cream. An aspect that we see as critical to achieving buy-in – and thus to sustaining motivation beyond the confines of the game – is the game illustrates the long term repercussions of incorrect portioning behavior on the profitability of the company. Simulations can make long-term consequences, which motivate change, visible in a compressed timeframe. Outside the enterprise, games such as World of Warcraft motivate hours of detailed work, planning, and skill building, by making clear connections between that work and a big mission that players find meaningful.
The concept of gamification is currently enjoying a successful stint as a kind of ‘child star’ but now it is time to see whether it can transition to equally-successful work in adult roles. The true potential is not fully known, but we expect that as more organizations recognize the need for a more extensive behavior-change toolkit, exploration of more advanced gamification will produce a range of effective –and affordable – techniques to produce complex and and sustained behavior change.
By:
Alex Kass and Jordan Buller
Enterprise Social Collaboration (SoCo) technologies are rapidly maturing from their origins as internal corporate versions of the public, consumer social-network services, like Facebook, Twitter, Blogger, and others. What’s emerging is a new kind of collaboration platform, which we see transforming the way enterprise knowledge work gets done.
Unfortunately, while the technology has been evolving, the thinking about how to use the technology has not always kept pace, so let's review what's been changing and why: Enterprise SoCo began as a new channel for sharing thoughts within the enterprise: The initial focus was on supporting blogging / micro-blogging, and discussion groups, and using the social graph to channel shared thoughts to relevant/interested participants throughout the enterprise. This conception of enterprise social proved useful, but not as earthshaking as the impact of social networks has been outside the enterprise.
To understand why an evolution was needed to drive more impact, let’s look at the two premises of public social networking services:
1. Most sharing in consumer social networks happens by someone taking time away from their day-to-day activities to go onto a collaboration platform and write a blog entry or status update.
2. Content on consumer social networks is consumed by someone take time away from their day-to-day work, to go onto the collaboration platform and read what others have written.
While these assumptions led to incredibly rapid adoption of consumer social networking, they didn't work nearly as well within the workplace: Experience has shown that while a few dedicated blogger-types will actually take time from their day-to-day activities to produce content on these networks, most employees can’t, won’t, and perhaps shouldn't. Most enterprise knowledge workers are very goal/task-oriented while at work. As a result, within the enterprise, social technology is unlikely to see heavy, sustained use until going onto the social collaboration platform leads directly to getting day-to-day work done more effectively and efficiently. This is exactly the evolution we’re now seeing in leading SoCo platforms: By combining social technologies with key knowledge management and groupware functionality, they’re becoming comprehensive cloud-based social collaboration platforms. In particular, we’ve started seeing enterprise social networks augmented with functionality like task and project management functions; collaborative document authoring; customizable team pages that serve as collaboration cockpits; wikis the can be used to share (and even crowd-source) everything from meeting notes to group mission statements; and integration with the desktop, enterprise, and cloud- based applications knowledge-workers already use. Instead of places knowledge workers go when they can take time away from your day-to-day work, they’re becoming places that they go to do their day-to-day work, and the means through which important information about that work is systematically shared, with very little effort on the part of the sharer. The point of adopting SoCo in the enterprise is not to create an enterprise of bloggers, it’s to leverage the social and interest graphs to driveactionable awareness of what others are actually doing.
The potential benefits are far-reaching:
· Providing distributed teams with fine-grained awareness they need for effective coordination of day-to-day activities;
· Helping members of adjacent teams to maintain the systematic peripheral awareness of what related teams are doing they need to ensure alignment;
· Allowing colleagues across the enterprise - or even the extended enterprise, including customers, suppliers, and contractors – who may not even know each other, to avoid duplication of effort, and seize opportunities to join forces which might otherwise have been missed; and
· Giving management the birds-eye view they need of the collaborative activity patterns within their organizations.
We’ve begun using one of the leading SoCo offerings to carry out our own R&D work. So let us give just a couple of small examples of the impact we’ve felt on a day-to-day basis.
· When one of us needed to assign a task to another team-mate, we used to send an email. Now we use the social collaboration platform’s ‘create task’ feature instead. When tasks are completed, that’s logged on the platform as well. In addition to adding a bit of structure, making it easier to track, and making it less likely that the task will simply get lost in the email stream, this approach has the benefit of making the work we’re doing much more transparent: Team-mates can immediately see what’s on each other’s queues and management can get continuous visibility on status. And most excitingly, the social features of the platform mean that people we don’t talk with much, but who either follow one of us, our group or track a content tag attached to the task see it as well: Maybe they’ve already done something similar, and add a comment about lesson learned, or maybe they know someone else who has a need for what we’re working on.
· When we co-author documents we store them on the social collaboration platform, and use MS Office extensions that integrate Office with the platform. This means that we are using the tools we’re used to, but our work in progress is visible from an early stage. Tags we attach to the document can bring it to the attention of some relevant colleagues, and the platform’s content-based recommendation engine brings it to the attention of other. We can benefit from the enhanced awareness of what we’re doing as well as the early feedback we sometimes get from colleagues we may not even know have expertise related to our content.
The examples above are representative of two key modes through which these platforms share our day-to-day work. The first is SoCo Platform as Web-Based Workspace, where features like wiki’s and web-based task management supported directly by the platform are used to do work (which is then shared as a matter of course). The second way to achieve frictionless sharing is SoCo Platform as Collaborative Data Hub, where work is done in other desktop or enterprise applications, which have connectors to the SoCo platform, allowing activity performed in those other applications to be shared through the platform’s social network.
The evolution of SoCo is far from complete. These platforms are becoming very multifaceted beasts, and no single vendor’s offering leads every facet. The platforms don’t easily interoperate, so companies adopting the technology still need to set priorities carefully and make tough choices. At a deeper level, the entire SoCo community is still learning about how to make the most from combining social technologies with collaboration and knowledge management at scale. As knowledge work increasingly moves onto these platforms, a number of questions will become increasingly important. For instance: When the content is spreadsheets, marketing plans, product designs, and actions related to those, as opposed to the clever thoughts, happy-birthday wishes and cute photos that are shared on consumer networks, how should that content be routed, filtered, presented to support various forms of collaboration while avoiding information overload? What mechanisms can be used to shape the way users interact with these systems to provide maximum benefit? How can the flow of knowledge work through these platforms be used to provide management with useful insight about what the collaborative activity patterns look like? As SoCo vendors, their customers, and R&D labs like ours gain more experience with these platforms, and develop increasingly sophisticated answers to these kinds of questions, we expect the power of these systems will continue to grow dramatically.
The success of the modern enterprise depends, as much as anything, on the ability to bring the best thinking to bear on every project and problem. Geographic distribution and organizational silos can make this especially challenging. We use the term, ‘Fully-Networked Enterprise’ to describe companies that have the ingredients to overcome those challenges. Of course, technology is only part of the puzzle: it’s most crucial to have the right people and culture, but it’s also critical to make it convenient for those people to co-create, coordinate team activity, stay abreast of developments in related parts of the organization, and to learn from each other. The newly-emerging social-collaboration platforms promise to provide the technology needed to make the fully-networked enterprise a reality.
 We all know exercising is good for us and we all had our new year resolution, but few of us are able to carry it through. Can we inject a better motivation to help us get in shape? To answer that question, the Labs started the Steptacular pilot, in collaboration with Stanford University and Live Well at Accenture, which applies game concepts as a motivator for people to get healthy. The pilot includes several gaming concepts:
- Clear goals: We set up a goal target for our participants, which is to achieve 10k steps a day, a recommendation from many fitness experts as a minimum required daily exercise. In addition, we set smaller goals for those that are less active, i.e., Silver, Gold and Platinum levels depending on how much you walk each week.
- Instant feedback: Clear goals do not make a difference if there is no way knowing how far you are away from the goal. We ask our participants to carry an Omron HJ-720 pedometer, which has a digital display to instantly show you what you have achieved. In addition, the Steptacular website shows participants' step history and how they stack up against other participants.
- Social: Social games have been a run-away success. Steptacular also leverage social features to encourage peer pressure. In Steptacular, you can connect with friends, and then you can watch (and more importantly push for) each other's progress.
- Engaging user interface: Steptacular uses a very engaging game allowing user to redeem random rewards for their achievement. We got many love emails expressing how they are motivated to walk more in order to play the game more.
The Steptacular pilot has just concluded. Although we will be publishing the research results shortly, I thought I will share some high-level statistics. 5,105 people signed up for the pilot. Collectively, the participants walked more than 1.8 Billion steps, that is more than three times the distance to the moon. Along the way, we got many fan emails expressing how motivating it is for them, and how they are able to lose weight and lower their cholesterol level. I will post a link to the research paper when we publish it, so that you can see in details what game mechanisms worked and how effective are the game mechanisms.
From a technical stand point, launching such a pilot is not an easy task. We operated under a very tight constraint. First, we had a very short window to launch the pilot. The pilot must end by a deadline due to other HR constraints. To maximize the pilot duration, we had to act fast. Through the hard work of several Stanford students, we are able to launch in 3.5 weeks in the end. Second, the pilot is only scheduled to run for a few months. Hence, we do not want to waste money and time to procure hardware to power our application. Third, we have to manage a large number of participants, in particular, the distribution of pedometers presents a major challenge for our small team.
Fortunately, Cloud comes to the rescue. Our application is a prime candidate to use Cloud. Our application not only is temporary (only needed for few months), but we also require capacity scale up quickly (have to launch quick). A quick TCO analysis clearly shows that choosing cloud is more economical. In the end, we chose Amazon as the technology platform, and we ended up using several services, including
- Amazon EC2: We are able to get our server quickly. With few weeks to launch, we have no time to wait for server procurement. In addition to spinning up servers quickly, we also leveraged the free Cloud Watch service to enable us closely monitor our system's performance.
- Amazon SES: We have to send thousands of emails to our participants, for example, email verification during sign up or sending out announcements. We could not get an internal email account set up quickly (takes time to build a business case and takes time for provisioning), and we do not have access to an external email service allowing us to send a massive number of messages. It took us only a couple of hours to setup Amazon's Simple Email Service, allowing us to focus on application design.
- Amazon retail: I have been doing Cloud research for the past several years, so spinning up server instances is easy. Unfortunately, running a supply chain to get pedometers into our participants' hand is no easy task. We looked into being our own dealer (buy bulk from Omron, then send), or using Amazon Fulfillment services, but in the end, we choose to just use Amazon retail. It turns out to be cheaper than what we could achieve ourselves. Within a couple of weeks of launching, we helped to sell 3,000+ pedometer through the Amazon retail site.
It is the ultimate dream of Cloud that you can provision any service you need by yourself, and pay only for what you use. The Steptacular pilot is definitely a beneficiary of that grand vision.
One of the reasons that we have a Labs location in Silicon Valley is that we want to stay close to the center of innovation and keep abreast of the cool startups coming up. Towards that goal, Accenture sponsored the demo day event by StartX (formerly SSE Labs). I was one of the few Labs folks who were fortunate enough to see the demo day live.
I have to say that I am pretty impressed with the event. There are a lot of people, and the hallway is so packed during break that it was a fight to get through the crowd. On top of that, I am pretty impressed with the startups they are showcasing. All of them are really cool, and a few really stuck in my mind:
WifiSlam: An indoor positioning tool that can detect your location based on signatures of wifi signals (from wifi basestations). I dream of the day when I will never get lost in shopping malls again.
Lark: They produce a device that you can wear on your wrist, which can function as a silent alarm clock. But more importantly, the device also tracks your sleeping pattern, so that you can know what you do not know.
GameClosure: They produce a game platform that can run on multiple devices. It could run on an Android phone or inside a browser. Game developers only need to write their games once, in javascript, and the games will run everywhere. It is even more cool when you can play a multi-player on your phone with your friend who is sitting in front of a computer.
As a benefit of sponsoring the event, we have a little table to showcase some research projects from the Labs. I showed Steptacular, a wellness incentive project we are working on, which is as cool as those startups :-).
By:
Kelly Dempski
Being the lead of an R&D group called "multichannel interactions" means that people often ask me for my thoughts on mobility. The honest answer is that I have nothing useful to say about "mobility". There, I said it. That feels good.
Let me explain...
People label things based on what features they feel are notable. "Personal Computers" were interesting back at a time when the idea of a computer for a single person was very novel. Ask a teenager today what "PC" means, and I bet they won't know. Likewise, I grew up talking about "mobile phones" or "cordless phones", and now it's hard to find a phone that isn't mobile, or has a cord. On a related note, I feel like "Wired" magazine is an anachronism, but I won't dwell on that.
We started talking about "mobility" at a time when most computers and their networks were highly immobile. PDAs were as close as we got, which wasn't very satisfying at all. I watched people use phones with WAP, but I was never masochistic enough to use it myself. For years, we talked about "mobility" because it was new, it was challenging, and it was the kind of thing that thought leaders talked about in Wired magazine.
The world has moved on. I haven't had an "immobile" computer for a decade (although my time with a high end gaming laptop almost made me immobile). In fact, my laptop is far more "mobile" than my iPad, which rarely leaves the living room. I use my laptop on the plane, my iPad from the couch, and my phone from the street corner. Of course, computing is far more mobile than a few years ago. I just don't think it's interesting to talk about, or to somewhat arbitrarily tag some devices as mobile and others not. (I would argue that the iPad is closer to a laptop than a traditional "mobile" device, yet others call it "mobile", presumably because it looks like a bigger, fatter version of a familiar mobile device)
I'm sure that by now, you completely agree with my argument that "mobility" (as a label) isn't as interesting as it used to be. If not, let me give another example. At the moment, I love using the Starbucks iPhone app that lets me pay by holding my phone in front of the scanner. It makes me smile every time (perhaps as a Pavlovian response to impending caffeine). The fact that it's a "mobile" app doesn't thrill me - my wallet and credit cards have been "mobile" for as long as I can remember. What does thrill me is the experience of scanning the phone rather than watch the barista repeatedly swipe the battered magnetic strip on a sheet of plastic. I like getting rewards and other incentives. In short, the experience has improved. Or, more precisely, my experience has improved.
The fact that I have an internet connection in my pocket, that it easy to use, highly personalized, and readily available is a product of "mobility", but we need to rethink how we talk about it. The advent of apps that deliver gaming mechanisms like badges or other incentives, or apps that tie into my social network, is what we should be focusing on. My experience is what matters, whether it's the experience afforded by mobility, usability, community, affinity, or any words ending with ITY. "Mobility" is a piece of the experience puzzle, but it's only one piece.
The highly personal experience of the personal computer has advanced to the point where we have dropped "personal" from the conversation. It's time we do the same for "mobile", retiring it to a life of bingo and early dinners, while we talk about how to enrich the experiences of customers using new types of contextual information, social connections, rich UIs, incentives, and more. I have points of view on all these facets of experience, but I have little to say about "mobility".
It is no question that Cloud is making a big impact in the industry, but how would it impact consulting firms specifically? If you were to believe some analysis on cloud impacts (see reason #3), we, Accenture, will be out of busy very soon, because we cannot adjust to "the smaller projects, agile approach, and lower margins". But if you were to believe me, I think cloud is the best thing that happened to consultants.
One reason is that I believe faster innovation and the myriad of technology choices require deep technology skills and experience. It used to be simple to determine the infrastructure choice. Simply buy a hardware from HP or IBM, install Oracle software, and most of your stack is in place. Unfortunately, in the cloud, there are many options to choose from. On the server options alone, there are many infrastructure cloud providers and each provides many different virtual machine configurations. Even a simple apple-to-apple cloud cost comparison is non-trivial. Once you choose the hardware, you then have to choose the software stack. Again, there are many options. For example, if you were to use a NoSQL data store, you are faced with many NoSQL platforms. There are many dimensions to compare NoSQL stores, and understanding your application requirement and choosing the right one is a daunting task.
To make the matter worse, the innovation rate is much higher in the cloud era. As an example, every month, Amazon is pushing out new services or new features. Even for a person like me who is focusing 100% on cloud, it is difficult to keep up, let alone someone who is focusing on his business application and just want to choose the right platform. For those who do not want to be bothered with the intricacy of cloud (that is most of us), it is better to contact a consultant.
Indeed, with cloud, the infrastructure cost will be greatly reduced, which could eat into the deal size. But in large-scale projects, infrastructure cost is typically a small fraction, often less than 20%. The majority of the cost is in application development. As the recent Accenture technology vision points out, the value (hence the margin) is in the high-layer of the stack. Consultants' value is in solving clients' business problem, not in saving infrastructure cost.
One thing that is accurate from the cloud impact analysis is that I would not golf with my clients much. But, sadly, it has been true all along :-(.
By:
Kelly Dempski
10 years ago, I was researching some of the new capabilities of the Xbox and PS2, which seemed poised to reinvigorate interest in console games, and generally change the way people behaved in their living rooms. At the time, I asked several of our TV-related clients if they were thinking about developing video games. They gave me a look like I was slightly batty (a look I’m familiar with) and said no. My logic was this: if we assume the business of a cable company or TV network was to entertain you in the living room, the mode of that entertainment was about to shift and it would be logical to shift with it. Perhaps first person shooters are not the core competency of a cable operator, but evidence suggests that their core competency is getting fewer and fewer eyeballs every day.
Fast forward to today. The change in the TV space has been slow, but I still hold to the questions I was asking at the time. Also, I now can make similar observations for other industries. The other day, I was asked to say something about how devices like the iPad would affect large textbook publishers. As a fanatical Kindle owner, I remarked that books, and especially textbooks would certainly move to the digital format very quickly. When the choices are a heavy backpack or a slim device, the choice seems obvious. This led me into all sorts of theories about how the business model would change when books no longer wore out, or when the books themselves were not limited by physical factors. One day, I might say more about that, but in the meantime, I want to focus on another aspect of the shift to digital. I started to ask “once textbooks are digital, should they even be books at all?”
The idea of a book is very old. Printing data on a sheet of rock, papyrus, or paper is where it started. Soon those sheets were put into stacks, and then those stacks were bound into books. Knowledge or any importance was put into a book format because, well, what other distributable format would they be in? However, once I put that book on an iPad, why do I need pages? Why do I need chapters and “books”? Physics “textbooks” should be interactive sets of experiments and observations. Biology “textbooks” should allow me to virtually dissect a frog or watch a plant grow. Of course these books will have text, but they don’t need to be a book. Also, “Pride and Prejudice” will still be in book form for an English Lit class, but it’s the exception that proves the rule. Even history books are arguably better done as multimedia experiences. Omar Bradley’s “A Soldier’s Story” is one of the many good books that show that war is as much a matter of logistics as about fighting. A good WWII book would include interactive graphics showing troop movements, images from the battlefront, audio from propaganda broadcasts, and much more than text. If you’re going to talk about the Battle of the Bulge, show the Bulge.
This leads me to ask: If I’m a textbook publisher, how quickly should I be shifting to become a software company? Probably pretty quickly. It might take the world some time to catch up with the technology, but in the meantime, I need to figure out how to write software for iPad, Kindle, and more. I have to figure out how to design experiences and rich UIs for devices like the iPad. I need to select a business model (subscriptions instead of sales?), and a DRM scheme that I’m comfortable with. I need to hire interactive designers and 3D artists. There’s a lot of work to do, and it needs to start now.
By:
Kelly Dempski
I have a tongue-in-cheek observation that I refer to as “Dempski’s Law”, which states that “utility is inversely proportional to hype”. Of the two dimensions, “utility” is fairly straightforward, it is how useful something actually is. “Hype”, by my definition, is the kind of breathless hysteria that goes along with certain products, leading to bold announcements on the covers of magazines that embarrass the editors 18 months later. Certain coworkers of mine are quick to point out flaws in Dempski’s Law, but I think they lack vision, and I’ve told them that. Sure, I could point out all the problems with Kepler’s Law, but I’ll leave that to other blogs and focus on the relationship between utility and hype.
To understand the beauty of Dempski’s Law, consider the common toaster. Here is a product that has a high utility (who doesn’t like toast?), and no hype at all. On the other hand, consider the Segway. Consider the news articles, interviews, and books written about the Segway and the way it was going to change the world. Now, consider the sales of the Segway, or the fact that you can replicate Segway functionality with a 3 wheel scooter like the ones seen in the Frankfurt airport. The toaster and the Segway occupy two ends of the graph representing Dempski’s Law.
Next, consider Java. Some have pointed out that it had a high degree of hype, and is highly useful. I agree, although I contend that the most hyped aspect of Java was the idea that it could run in any browser, which turned out to be either untrue or uninteresting, and that the useful aspects of Java, such as the server side uses, were largely unhyped. On the other hand, Flash, which never really had the wild-eyed press coverage that Java had, was arguably a huge factor in the advent of Rich Internet Applications and sites such as YouTube. I could make similar arguments for Twitter vs. SMS. The latter has orders of magnitude more traffic, the former has orders of magnitude more press coverage. At CES next month, the floor will be covered by hundreds of 3DTVs. Considering the amount of available content and general response to 3D, I’d say 3DTV also adheres to Dempski’s Law.
Yes, you can think of exceptions (I’ll get to that in a bit), but let me defend the law just a bit more. If you step back, you’ll see that the idea isn’t that controversial. Washing machines, while very useful, don’t help sell issues of Wired. Bloggers are drawn to speculate about something new, different, and cool, more than they are to talk about “utility”. Trends in advertising and media in general have shifted toward emphasizing form over function. Just as engineers toiled to support Moore’s Law, the media is working to help me prove Dempski’s Law.
Keep the form over function point in mind when we talk about exceptions. Dempski’s Law is not meant to be a true law any more than Moore’s Law is, but occasionally, people will feel the need to point out exceptions like the iPhone (or iAnything, for that matter). The iPhone was both heavily hyped and heavily useful, but one observation I would make is that in many of these cases, “hype” is an intrinsic part of the product or the brand. Go to the Apple store, and you’ll see a level of fervor that is unmatched in almost any other retail store. Hype is part of Apple’s brand. The same could be said for Linux, which is useful, and is greatly hyped in its own way, but that zeal and passion is somehow part of the Linux brand. How does this happen? A full discussion of Apple’s brand is a long discussion, but I’ll focus on one facet that I call the “Good Enough Observation”. The GEO basically says that once the underlying technology of a given product segment is “good enough”, differentiation in form becomes more important than differentiation in raw technology. Intel-based notebooks are largely technically undifferentiated, but Apple has managed to capture a market with sleek lines, metal bodies, and glowing logos. This is especially true for the many people who buy Apple notebooks and then run Bootcamp. In segments where the GEO holds, many products appear to break Dempski’s Law, but only because the hype is around the non-utilitarian aspects of the product. Apple’s hype is supported by their great design. Linux benefits from a near-religious attitude of many users. So, utility is inversely proportional to hype, unless “hype” is an integral feature of the product.
Have I made a convincing argument for the reality of Dempski’s Law? I don’t know if it matters because I’m only half-serious, but hopefully I’ve planted a seed in your head that will bloom into skepticism every time you see a wide eyed magazine article, or read a sentence that ends with “…,the valley’s hottest new start-up”. Who knows, being aware of Dempski’s Law might one day save you from making a very bad investment.
Although MapReduce has found wide spread usage within the startups and SMBs community, its enterprise adoption is just beginning. We are seeing increasingly more enterprises evaluating the technology and developing PoC (Proof of Concept) to see where the technology may fit in the enterprise landscape. Also, there are several pioneering clients who have already implemented Hadoop in production. For example, I am informed recently that one of our government clients has already deployed 12 instances of Hadoop clusters.
For enterprises looking to deploy the technology, they now have one more choice to choose from. Accenture recently has partnered up with Appistry -- an internal cloud platform provider -- to release the next version of Cloud MapReduce (CMR). Building on a different architecture, the CMR implementation achieves many advantages over other implementations, such as Hadoop, including better scalability and higher performance. The Appistry integration allows an enterprise to run CMR inside its firewall instead of or in addition to running in the Amazon Cloud environment. This is officially announced by Appistry, which is also covered by GigaOM and New York Times. You can read about technical details on Cloud MapReduce on Appistry.
For an enterprise choosing which platform to deploy, they have several dimensions to consider. In addition to scalability and performance, there are several key reasons to consider the Appistry's version of CMR.
- No single point of failure. If you cannot tolerate downtime and potential data loss, then you should look into a fully distributed architecture as used by CMR instead of a master/slave architecture as used by Hadoop.
- Streaming support and incremental batch. If you constantly have new data coming in and need to continuously run your batch analysis to including the new data, then think about deploying a framework that supports streaming. The latest CMR implements streaming support, conceptually similar to what is provided by HOP, but it is implemented on a commercial grade.
- Support. If you are looking for a product company (instead of a consulting company) to support your deployment, Appistry is there for you.
Obviously, there are lots of reasons to choose Hadoop as well. For one, there is a huge community around the Hadoop platform. It is our intention to continue to develop CMR to make it interface compatible with Hadoop, so that CMR can be an integral part of the community. Stay tuned for the next version.
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