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Renovation

2010/02/21 in Focus | No comments

I’ve done a bit of spring cleaning of the blog on a quiet Sunday afternoon (plus the kids are monopolising the Wii, so I can’t play New Super Mario Bros).

There’s more to do, but the big change is to gather some of the article threads into categories. A couple of posts seem to have taken a life of their own, and the resultant ping-pong between this blog and others has generated some interesting narratives on a couple of topics. Rather than leave them hidden in the threads, I’ve created a Focus category, and started to collect each thread in a sub-category.

So far:

  • The Value of Information. Starting with a simple observation that when we get information has as much impact as what we get, this thread generated some nice thoughts on how we might use information to create a more dynamic enterprise.
  • The Art of Random. Triggered by an invitation to present at InnoFuture — which unfortunately didn’t eventuate — I used the content create a series of posts (the outline for the preso was around six pages, so it would have been to much for one post). It covers the idea that innovation seems random due to the simple fact that your are not aware of the intervening steps from interesting problem through to novel solution.

There’s also a placeholder for Knowledge Worker of the Future, but more on that later.

Oh — and my favorite flying car is now in the header. Next I need to sort out the CSS colours to match.

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Managing personalisation is more important than managing change

2010/02/06 in Enterprise 2.0, Knowledge Worker of the Future, Posterous | 7 comments

Death, taxes, and now, change, are the eternal verities. As I said in another post:

The pace of change has accelerated to the point that everyone’s challenge, from Pre-Boomers and Baby Boomers through Generation Y to Generation Z, is how to cope with significant change over the next ten years. If we are, as some predict, moving to an innovation economy, then it is the ability to adapt that is most important. Those betting their organisation on a generational change will be sadly disappointed as no generation has a monopoly on coping with change.

While the youngest generation (whichever that is at a particular point in time) might have the advantage of coming unencumbered to the new ways of working, every generation has a unfortunate habit of treating what they learnt in their formative years (~24) as dogma once they hit their late 20s. Social research has shown that most people’s interest in novel ideas or experiences peaks around the mid to late 20s. (Tell me your favourite band and cuisine, and I’ll tell you what decade you grew up in.) Or, put another way, 24–28 might have the advantage in a rapidly changing world, but once you grow out the top of that age bracket you’ll find yourself at the disadvantage.

However, as with all gross generalisations, and the exceptions are more interesting than the rule; in this case the commonalities between groups are usually stronger than the differences between them. Research like Forrest’s Groundswell show that its more productive to think in terms of personality types.

I prefer to focus on getting stuff done, and ensuring that each and every stakeholder has the tools and support they need to get their job done. This is not a static thing either, something we do once for each stakeholder, as someone’s needs and preferences can change month-by-month, week-by-week, day-by-day or even minute-by-minute.

And this is probably the most important mega-trend we’re seeing emerge at the moment: the drive to continually personalise communication/products/services/tools for each and every individual, rather than trying to divide people into coarse-grained, and increasingly unproductive, demographic groups with predefined needs. If you’re managing change, then you’re still thinking in terms of a static work/home environment that needs to be transformed (however regularly). If you’re managing personalisation, then you’re focused on creating a continually optimised environment for all your stakeholders, ensuring that they have the information and tools they need at that moment. Change isn’t an enemy that should be managed—its a tool to help you achieve, and sustain, peak performance.

Posted via web from PEG @ Posterous

Why scanning more data will not (necessarily) help BI

2009/12/22 in Business Intelligence, Posterous, The Value of Information | 1 comment

I pointed out the other day, that we seem to be at a tipping point for BI. The quest for more seems to be loosing its head of steam, with most decision makers drowning in a sea of massaged and smoothed data. There are some good moves to look beyond our traditional stomping ground of transactional data, but the real challenge is not in considering more data, but to consider the right data.

Most interesting business decisions seem to be a synthesis process. We take a handful of data and fuse them to create an insight. The invention of breath strips is a case in point. We can rarely break our problem down to a single (computed) metric, the world just doesn’t work that way.

Most business decisions rest on small number of data points. It’s just one of our cognitive limits: our working memory is only large enough to hold (approximately) four things (concepts and/or data points) in our head at once. This is one reason that I think Andrew McAfee’s cut-down business case works so well; it works with our human limitations rather than against them.

I was watching an interesting talk the other day — Peter Norvig was providing some gentle suggestions on what features should be beneficial in a language to support scientific computing. Somewhere in the middle of the talk he mentioned the Curse of dimensionality, which is something I hadn’t thought of for a while. This is the problem caused by the exponential increase in volume associated with each additional dimension of (mathematical) space.

In terms of the problem we’re considering, this means that if you are looking for n insights to a problem in a field of data (the n best data points to drive our decision), then finding them becomes exponentially harder for each data set (dimension) we add. More isn’t necessarily better. While the addition of new data sets (such as sourcing data from social networks) enables us to create new correlations, we’re also forced to search an exponentially larger area to find them. It’s the law of diminishing returns.

Our inbuilt cognitive limit only complicates this. When we hit our cognitive limit — when n becomes as large as we can usefully use — any additional correlations can become a burden rather than a benefit. In today’s rich and varied information environment, the problem isn’t to consider more data, or to find more correlations, its to find the best three or features in the data which will drive our decision in the right direction.

How do we navigate from the outside in? From the decision we need, to the data that will drive it. This is the problem I hope the Value of Information discussion addresses.

Posted via web from PEG @ Posterous

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Innovation and the art of random

2009/12/21 in Innovation, Mailing List, The Art of Random | 1 comment

A little while ago I was invited to speak at an event, InnoFuture, which, for a mixture of reasons, didn’t end up happening. The theme for the event was Ahead of the trends — the random effect. My take on it was that innovation is not random, it’s just happening faster than you can process, and that ideas are commoditized making synthesis, the creation of new solutions to old problems, what drives innovation. I was pretty happy with the outline I put together for my talk, that I ended up reusing the content and breaking it into three blog posts, rather than letting it go to waste.

Innovation seems to be the topic of the day. Everyone seems to want some, thinking that it’s the secret sauce which will help them (or their company) bubble to the top of the heap. The self help and consulting communities have responded in force, trying to bottle lightening or package the silver bullet (whichever metaphor you prefer).

It was in this environment that I was quite taken by the topic of a recent InnoFuture event when I was asked to speak.

Ahead of trends — the random effect.
When a concept becomes a trend, you are a not the leader. How to tap into valuable ideas for products, services and communication before they are seen as trends, when they are just … random? Albert Einstein said that imagination is more important than knowledge. Let’s open the doors and let the imagination in for it seems that in the current crisis, the right brain is winning and we may be rationalized to death before things get better.

I’ve never seen the random effect, though I have been delightfully surprised when something unexpected pops up. Having been involved in a bunch of companies and projects that, I’m told, where innovative, I’ve always thought innovation was not so much random, as the result of obliquity. What makes it seem random is the simple fact that your are not aware of the intervening steps from interesting problem through to novel solution.

I figured I’d mash together a few ideas that capture this thought, and provide some (hopefully) sage advice based on what I do to deal with random. I ended up selecting:

  • John Boyd on why rapidly changing environments are confusing,
  • Peter Drucker‘s insight that insight (the tacit application of knowledge) is not a transferable good,
  • the struggle for fluency that we all go through as we learn to read,
  • John Boyd (again, but then he had a lot of good ideas) on the need for synthesis,
  • KK Pang (and old lecturer of mine) on the need to view problems from multiple contexts,
  • the need to follow a consistent theme of interest as the only tractable way of finding interesting problems to solve, and
  • my own experiences in leveraging a network of like and dissimilar minds as a way of effectivly out-sourcing analysis.

The result was called Of snow mobiles and childhood readers: why random isn’t, and how to make it work for you. I ended up having far to much content to fill my twenty minute slot, so it’s probably for the better that the event didn’t go ahead, as it would have taken a lot of time to cut it down.

Given that I had a fairly well developed outline, I decided to make it into a series of blog posts (plus my slides these days don’t have a lot of text on them, so if I just dropped the slides online they wouldn’t make any sense). The blog posts ended up breaking down this way:

  1. Innovation should not be the race for the new-new thing.
    Points out that innovation only seems random, unexpected, as you don’t see the intervening steps between a problem and new solution, and that innovation is the result of many small commoditized steps. This ties into one of my earlier posts of dealing with the speed of change.
  2. The role of snowmobiles in innovation.
    Argues that ideas are a common commodity, and that the real challenge with innovation is synthesis rather than ideation.
  3. Childhood readers and the art of random.
    Argues that the key to innovation is to find interesting problems to solve, and suggests that the best approach is to be fluent in a range of domains (sectors, geographies, activities, …) to provide a broader perspective, focus on a line of inquiry to provide some structure, and build a network of people with complimentary interests, providing you with the time, space and opportunity to focus on synthesis.

I expect that these are more productive if taken as a whole, rather than individual posts.

If you look at the path I’ve charted over my career then this is the approach I’ve taken, and my topic of choice is how people communicate and decide as a group, leading me to John Boyd, Cicero, human-computer interaction, agent technology, biology (my thesis was mathematically modelling nerves in a cat), and so on.

I still have the slides, so feel free to contact me it you’re interested in my presenting all or part of this topic.

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Is BI really the next big thing?

2009/12/16 in Business Intelligence, The Value of Information | 8 comments

I think we’re at a tipping point with BI. Yes, it makes sense that BI should be the next big thing in the new year, as many pundits are predicting, driven by the need to make sense of the massive volume of data we’re accumulated. However, I doubt that BI in its current form is up to the task.

As one of the CEOs Andy Mulholland spoke to mentioned “I want to know … when I need to focus in.” The CEO’s problem is not more data, but the right data. As Andy rightfully points out in an earlier blog post, we’ve been focused on harvesting the value from our internal, manufactured data, ignoring the latent potential in our unstructured data (let alone the unstructured data we can find outside the enterprise). The challenge is not to find more data, but the right data to drive the CEO’s decision on where to focus.

It’s amazing how little data you need to make an effective decision—if you have the right data. Andrew McAfee wrote a nice blog post a few years ago (The case against the business case is the closest I can find to it), pointing out that the mass of data we pile into a conventional business case just clouds the issues, creating long cause-and-effect chains that make it hard to come to an effective decision. His solution was the one page business case: capability delivered, (rough) business requirements, solution footprint, and (rough) costing. It might be one page, but there is enough information, the right information, to make an effective decision. I’ve used his approach ever since.

Current BI seems to be approaching the horse from the wrong direction, much like Andrew’s business case problem. We focus on sifting through all the information we have, trying to glean any trends and correlations which might be useful. This works as small to moderate scales, but once we reach the huge end of the scale it starts to groan under its own weight. It’s the law of diminishing returns—adding more information to the mix will only have a moderate benefit compared to the effort required to integrate and process it.

A more productive method might be to use a hypothesis-driven approach. Rather than look for anything that might be interesting, why not go spelunking for specific features which we know will be interesting?  The features we’re looking for in the information are (almost always) to support a decision. Why not map out that decision, similar to how we map out the requires for a feedback loop in a control system, and identify the types of features that we need to support the decision we want to make? We can segment our data sets based on the features’ gross characteristics (inside vs. outside, predictive vs. historical …) and then search in the appropriate segments for the features we need. We’ve broken one large problem—find correlations in one massive data set—into a series of much more manageable tasks.

The information arms race, the race to search through more information for that golden ticket, is just a relic of the lack of information we’ve lived with in the past. In today’s land of plenty, more is not necessarily better. Finding the right features is our real challenge.

Posted via email from PEG @ Posterous

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Childhood readers and the art of random

2009/12/09 in Innovation, Mailing List, The Art of Random | No comments

Note: This post is part of larger series on innovation, going under the collective name of Innovation and Art of Random.

Innovation can seem random. We’re dealing with so much change in our daily lives that we miss the long and tortuous journey an innovation takes from it’s first conception through to the delivered solution, causing the innovation to seemingly appear from nowhere. We’re distracted as we’re trying to cope with the huge volume of work our changing environment creates, adjusting to the new normal, while trying to find time to sift through the idea fire hose for that one good idea. However ideas are common, commoditized even, and our real challenge is to make connections.

As Peter Drucker pointed out: insight, the tacit application of knowledge is not a transferable good. The value we derive from innovation comes from synthesis, the tacit application of knowledge to create a new solution. The challenge is to find time to pull apart the tools available to us, recombining them to synthesis new (and hopefully innovative) solutions to the problems we’re confronting today.

While ideas may be cheap, the time and space needed to create insight are not. We need to understand our problem from multiple contexts, teasing out the important elements, bringing together ideas to address each element in the synthesis of an original solution. This process takes time, often more time than we can spare, and so we need to invest our time wisely. Which steps in this processes are the most valuable (or the least transferable), the steps we need to own? Which can we outsource, passing responsibility to partners, or even our social network? And is it possible to create time? Using technology to take some of the load and create the breathing room we need.

Dr. Khee Pang

Dr. Khee Pang

One of the best pieces of advice I picked up at university was from Dr. K. K. Pang, who unfortunately passed away in March 2009. Dr Pang taught circuit theory, which can be quite a frustrating subject. It’s common to encounter a problem in circuit theory which you just can’t find a way into, making it seemingly impossible to solve. Dr. Pang’s brilliant, yet simple, advice was “If you don’t like the problem, then change it to one you do like.”. Just start messing with the problem, transforming bits of the circuit at random until you find a problem that you can solve.

Fast forward to my current work, far removed from circuit theory, and I still find myself using this piece of advice at least once a week. It’s not uncommon to come across a problem, a problem with little direct connection to technology, that needs to be approached from a very different angle. When stuck, take a different angle, make it a different problem, and you might find this new problem more to you liking.

You often bump into the same problem in different contexts as you work across industries and geographies. Different contexts can necessitate a different point of view, making the problem look slightly different. This highlights other aspects of the problem that you might not have been aware of before, highlighting previously hidden assumptions or connections to other problems. However, while this cross industry and geography insight is a valuable tool, the time required to go spelunking for insight is prohibitive. We find ourselves spend too much decoding the new context, and too little teasing out the important elements.

Learning to read, something I expect we all did in our childhood, is a struggle for fluency. We work from the identification of letters and words, through struggling to decode the text, to a level of fluency that enables us to focus on the meaning behind the text. Being fluent means being good enough at identification and decoding that we have the time and space for comprehension.

The ability to change the problem in front of you is really a question of being fluent in a range of environments; understanding a number of doctrines. These might be different industries (finance, public sector, utilities …) domains (logistics, risk management, military tactics, rhetoric …) or even geographies (APAC, EU, US …) as each has its own approach. We need enough experience in an environment to be able to decode it easily. Generally this means in the trenches experience, focused on applying knowledge, allowing us to weed out the common place and find the interesting and new. But building fluency takes time though; we can’t afford to immerse ourselves in every possible environment that might be of interest.

For quite a few years (from back in the day when my email address had a .oz at the end) I’ve been collecting a network of colleagues. Each is inquisitive in our own way, each with our own area of interest or theme, covering a huge, overlapping range of doctrines, while always looking for another idea too add to our toolbox. With the world being small, or even flat, this network of like minds has often been the source of a different point of view, one which solves the problem I’m working on. More recently this network has been migrating to Twitter, making the shared conversation more dynamic and immediate. It’s small networks of like-minds like this which can provide us the ability to effectively outsource the majority of our analysis, spreading the effort amongst out peers and creating the time and space to focus on synthesis.

Which brings us to the crux of the problem: innovation relies on the synthesis, and the key to synthesis is in finding interesting problems to solve. An idea, no matter how brilliant, will not go far unless it results in a product or service the people want. Innovation exists out at the surface of our organisations, or at the production coal face. Just as with the breath strips example, interesting problems pop up in the most unexpected places. Our challenge is prepare ourselves so that we can capitalise on the the opportunity a problem represents. As a famous golfer once said:

Gary Player

Gary Player

The more I practice, the luckier I get.
Gary Player

The world around us changes so rapidly that innovation can seem random. The snowmobile was obvious to the people who invented it, as they worked via trial-and-error from the original problem they wanted to solve through to the completed solution; it didn’t leap from their brow as a fully formed concept. Develop your interests, become fluent in a wide range of relevant topics and environments, use your network to extend your reach even further, and look for interesting problems to solve. In a world awash with good ideas, when innovation relies on your ability synthesis new solutions by finding an new angle from which to approach old problems (possibly problems so old that people forgot that they had them), the key to success is to find our own focus and then use your own own interests to drive yourself forward while effectively leveraging your network and resources around you to take as much of the load as possible. Innovation is rarely the result of a brilliant idea, but a patient process of finding problems to solve and then solving them, and sometimes we’re surprised by how innovative our solutions can be.

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A nice visual argument for the value of mash-ups

2009/12/08 in Business-Technology, Enterprise 2.0, Knowledge Worker of the Future, Posterous | No comments

As I’ve mentioned before, I would like a nice, clear, crisp definition for mash-up. A definition which captures the benefits that mash-ups can bring, rather than detailing a collection of tools, technologies and standards that we happen to find interesting at the time. For me, this is the TQM argument of fusing data and process to eliminate unnecessary decisions—make-work or swivel chair integration—to create a more efficient and effective work environment.

It’s Just a Bunch of Stuff That Happens has done a brilliant job of capturing this visually (included below). I like the usability aspect this highlights. A mash-up’s focus is cross-application usability—removing the annoyances of dealing with separate information sources. We could simply take these sources and squish them up against the glass, delivering the content into iGoogle or NetVibes gadgets. But what those original push-pins on a map mash-ups did was improve the usability of these information sources by eliminating the decisions required to navigate across them. Just as Apple did with the iPod and iPhone, eliminating or fusing functions to eliminate the (unnecessary) decisions required to navigate the overly complex and confusing interfaces of the mobile phones that came before them.

iGoogle and NetVibes are the Symbian to a mash-up’s iPhone.

Symplicity

Posted via web from PEG @ Posterous

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The rise of task worker 2.0

2009/11/25 in Business-Technology, Enterprise 2.0, Knowledge Worker of the Future, Posterous | No comments

Companies are delayering (again) and pushing decisions to the surface of the organisation, where there is direct contact with customers and partners, in order to be more responsive. Some companies, Zara for example, are making this into a science as they re-engineer their organisations to maximise agility. To do this companies are empowering the people working at the customer and partner interface to solve the problems in front of them, without intervention from head office or middle management.

One interesting effect of this is a shift in the coalface of Enterprise 2.0 adoption. We’ve been focused on the white collar, office bound knowledge worker as the adopter of Web 2.0 tools in the enterprise, with mobility limited to the ability to work from a local coffee shop or an executive tweeting from the airport lounge. However, with decisions devolving to the customer and partner interface we are finding that the middle layers of our organisations are being trimmed, and their responsibilities transferred to the people with direct customer or operational contact. Knowledge workers are being superseded by task workers: people focused on consuming information in the field to solve operational or customer problems.

Think about how Toyota structures production lines—the whole LEAN story—empowering the people on the shop floor (traditional task workers) to solve problems. Or the utility field worker on maintenance, who used to work under instruction from the depot but is now mobile, working remotely. Or the transactional shop assistant who’s focus is shifting from the financial transaction to customer management. And so on.

To a certain extent, Web 2.0 and Enterprise 2.0′s traditional target, the white collar knowledge worker, is being eliminated by the very technology that is intended to empower them. And their replacement, the situated task workers, has been ignored by the Enterprise 2.0 rollout. Or, even worse, we’ve deliberately locked down their computing environment to prevent them going off task.

This creates an interesting challenge. How do we move from our early adopters and use our new collaboration tools and technique to support (and not distract) these task workers, situated in a challenging operational environment?

Posted via email from PEG @ Posterous

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We need a better definition for “mash-up”

2009/11/24 in Business-Technology, Enterprise 2.0, Knowledge Worker of the Future, Posterous | 5 comments

Mash-up no longer seems to mean was we thought it meant. The term has been claimed by the analysts and platform vendors as short hand for the current collection of hot product features, and no longer represents the goals and benefits of those original mash-ups that drew our interest. If we want to avoid the hype, firmly tying mash-up to the benefits we saw in those first solutions, then we need to reclaim the term, basing its definition on the outcomes those first mash-up solutions delivered, rather than the (fairly) conventional means used to deliver them.

Definitions are a good thing, as they help keep us all on the same page and make conversations easier. However, what often starts our as a powerful concept—with a clear value proposition—is rapidly diluted as the original definition gets pulled in different directions.

Over time, the foundation of a term’s definition moves from the outcome it represents (and the benefits this outcome provides), taking rest on the means which the original outcome was delivered, driven by everyones’ desire to define what they are doing in relation to the current hot topic. Next, the people who consider it to be just a means, often start redefining the meaning to make it more inclusive, while continuing to claim the original benefits. We end up selling the new hype as either means or goals or any half-hearted solution in between – and missing the original outcome nearly completely. 

The original mash-ups were simple things. Pulling together data from two or more sources to create a new consolidated view. Think push-pins on a map. Previously I would have had to access these data sources separately—find, select, remember, find, select correlation, click. With the mash-up this multi-step, and multi-decision workflow is reduced to a single look, select, click. Many decisions became one, and I was no longer forced to remember intermediate steps or data. 

It was this elimination of unnecessary decisions that first attracted many of us to the idea of a mash-up. As TQM, LEAN, et al tell us, unnecessary decisions are a source of errors. If we want to deliver high quality at a low cost (i.e. efficient and effective knowledge workers) then we need to eliminate these decisions. This helps us become more productive by spending a greater proportion of our time on the decisions that really matter, rather than on messy busy work. Fewer decisions also means fewer chances for mistakes.

Since those original mash-up solutions, our definition of mash-up evolved. Todays definitions are founded on the tools and techniques used to deliver a modern web-based GUI. These definitions focus on the technology standards, where the data is processed (client vs. server), standards and APIs, and even mention application architectures used. Rarely do they talk about the outcome delivered, or the benefits this brings.

There’s little difference, for example, between some mashups and a modern portal. We can debate the differences between aggregating data on the client vs. the server, but does it really matter if it doesn’t change the outcome, and the difference is invisible to the user? The same can be said for the use of standards, APIs used, user configuration options, differing solution architectures and so on.

The shift to a feature-function base definition has allowed the product vendors and analysts of seize control of our definition, and apply it to the next generation of products they would like us to buy. This has diluted the term to the point that it seems to cover much of what we’ve been doing for the last decade, and many of the benefits ascribed to the original mash-ups don’t apply to solutions which fit under this new, broader church.

Modern consumer home pages, such as iGoogle and NetVibes for example, do allow us to use desk and screen real estate more effectively–providing a small productivity boost–but they don’t address the root of the problem. Putting two gadgets on a page does little to fuse the data. The user is still required to scan the CRM and order management gadgets separately, fusing the data in their head.  Find, select, remember, find, select correlation, click rather than a single look, select, click.

The gadgets might be visually proximate, but we could do that with two browser windows. Or two green screens side-by-side. The user is still required to look at both, and establish the correlation themselves. The chair might not swivel as much as with old school portlets, but eyeballs still do, and we are still forcing the user to make unnecessary decisions about data correlation. They don’t deliver that eliminate unnecessary decisions outcome that first attracted us to mash-ups.

The gold standard we need to measure potential mash-ups against is the melding of data used to eliminate unnecessary decisions. This might something visual, like push-pins on a map or markup on an x-ray. Or it might cover tabular data, where different cells in the table are sourced from different back-end systems. (Single customer view generated at the user interface.) If we fuse the data, building new gadgets which pull data attributes and function into one consistent view, then we eliminate these decisions. We can even extend this to function, allowing the user to trigger a workflow or process that make sense in the view they are presented, but with no knowledge of what or where implements the workflow.

We need a definition for mash-ups is that captures this outcome. Something like:

A mash-up is a user interface, or user interface element, that melds data and function from multiple sources to create one single, seamless view of a topic, eliminating unnecessary decisions and actions.

This v0.1 definition provides a nice, terse, strong definition for mash-up which we can hang a number of concrete benefits from.

  • More productive knowledge workers. Our knowledge workers only spend time on the decisions that really matter, rather than on messy busy work, making them more productive.
  • More effective knowledge workers. Fewer decisions mean fewer chances for mistakes, reducing the cost of error recovery and rework resulting in more effective knowledge workers.

Posted via email from PEG @ Posterous

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What are the benefits of a mash-up?

2009/11/19 in Business-Technology, Enterprise 2.0, Knowledge Worker of the Future, Posterous | 1 comment

The original mash-ups were simple things. Solutions like the Chicago Crime and AlertMap pulled together data from two or more sources (maps and crime databases, in the case of Chicago Crime) to create one single view. Previously I would have had to access these data sources separately–find, select, remember, find, correlate, click. With the mash-up this multi-step and multi-decision workflow is reduced to a single look, select, click. Many decisions became one, and I was no longer forced to remember intermediate data.

TQM, LEAN, et al tell us that unnecessary decisions are a source of errors. If we want to deliver high quality at a low cost (i.e. efficient and effective knowledge workers) then we need to eliminate these decisions. This brings a few immediate benefits:

  • More productive knowledge workers. Our knowledge workers only spend time on the decisions that really matter, rather than on messy busy work.
  • More effective knowledge workers. Fewer decisions mean fewer chances for mistakes.
If we were to use mash-ups in this way to simplify key, call centre processes (for example) then we can can translate these two points direct into business benefits:
  • Reduced staff on-boarding costs, cutting training time, and reducing time to competency by providing a simply and more direct workflow, one which leads the call centre operator through the workflow.
  • Reduce call servicing costs, including reduced escalations and improved first call resolution by avoiding mistakes and and ensuing that the operator has all the information required to solve the customer’s problem on hand.
  • Improved staff retention, by allowing them to focus on the customer engagement, rather than soul destroying swivel chair integration.

With a typical call centre agent using six applications per call, this represents a drastic simplification of the call centre work environment.

A third benefit is the decoupling a mash-up creates between presentation and back-end applications. As all user interaction is mediated by the mash-up, there is not direct connection between the data and function provided by a single application, and the work surface the knowledge worker interacts with. This enables us to evolve the UI and back-end separately, allowing us to keep the user interface in sync with business demands while continuing to pursue a separate, and longer cycle consolidation effort to consolidate backend systems to reduce operational costs.

It’s easy to extrapolate these (potential) benefits to other solutions. My favourite is human services, where providing a case worker with the right information at the right time, and removing unnecessary distractions, will result in a material difference in the quality of life for the people under their care. However, these benefits can easily be applied to any high value knowledge work processes, such as logistics exception manager, utility field worker, sales personnel, and so on.

Posted via email from PEG @ Posterous

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