Friday, 5 February 2016

Optimising for the internal search engine

Internal company search seldom works as well as Google, because so few people optimise the findability of their content.

Image from Wikimedia commons
People often cite Google as the gold-standard in search, but partly Google works so well because of the prevalence of search-engine optimisation in the World Wide Web. Anyone who uses the web as a market place or a means of making publications available, knows about the issues of Search Engine Optimisation. And if you own a website, you probably receive 10 emails a day from people offering to improve your SEO ranking.

As far as internal sites on the Intranet are concerned, there probably is little thought given to optimising search. Metatags may be missing, document titles may be poor or unhelpful, documents may be filed in unhelpful ways (see my post on "a haystack is no place to store your needles"), and the whole issue of findability is often ignored.

I remember in the mid 90s when BP introduced their first Intranet, and the manager of BP Norway (my boss at the time) set up a BP Norway home page. It went live, and the next day we went onto the Intranet, typed in the search term "BP Norway", and .......... got no results!

It turned out that the words "BP Norway" only ever appeared in banner graphics, which had no Alt tags (I don't know if this was even possible at the time) and were therefore invisible to the search engine. Therefore this whole site had become unfindable, or search-engine-impaired.

Part of the role of the Knowledge Management team, in any organisation that makes extensive use of codified knowledge combined with search, is to educate the owners of the knowledge stores about how to ensure findability and about the need for optimising codified knowledge for the internal search engine.

Maybe in this way we will get a little closer to Google. 

Thursday, 4 February 2016

Why KM "minimum standards" are vital

What are the minimum conditions of satisfaction in your company for KM?  If you have not set any, then the long term future of your KM activity may be at risk.

Knowledge Management becomes embedded in an organisation when everyone who needs to be is involved and engaged.  However not everyone wants to be engaged, and knowledge management is always in conflict with other less important but more urgent activity.

As a result, people may begin to "skimp" on KM - doing the bare minimum that they can get away with. Although this sounds bad, it's a fact of life at work. There is so much to do, there are so many competing pressures, that people will often focus on any one aspect (KM, quality, risk management) to the extent that they have to. They will follow the rules and the expectations, and very few people can go above and beyond the expectation in every aspect of their work.

If you have set minimum conditions of satisfaction for KM, at a level that still adds value to the organisation, then its no problem if people do the bare minimum that they can get away with. If you have not set any minimum conditions, then the bare minimum is effectively zero. People can refuse to engage with KM, and still get away with it.

If there are no minimum conditions of satisfaction, then effectively KM is optional; you can do as little of it as you want. If someone wants to do zero KM, that's up to them. Nobody cares, nobody minds. And if KM is optional, then generally it won't get done. Nobody has time for optional activity.

If there are minimum conditions of satisfaction, then people are clear about the acceptable standard. They know what is expected of them (at a minimum level, anyway). If they fall below the minimum, then people do mind and people do care. That's the point of minimum conditions of satisfaction - if you don't meet them, then your performance is not satisfactory.

So you need to ask yourself, what is the minimum standard that individuals and teams and projects need to do in tour organisation, to deliver a satisfactory level of KM?

Do they need to conduct lessons capture for each project? Do they need to ensure communities of practice are active for each key area of knowledge? Do they need to consult the company knowledge base at the start of each piece of work?  So they have a role in contributing to the knowledge base, and what is the minimum expected contribution?

Define these minimum standards, make sure they are not too onerous but still add value, and ask your management to help you embed these into policies, procedures and expectations. Make it part of a "rights and responsibilities" charter, perhaps, like Oxfam has. But define these minimum standards, otherwise the effective minimum is zero. 

Wednesday, 3 February 2016

How vancouver airport shared Olympic knowledge with London Heathrow

I have blogged in the past about Olympic Knowledge Management, and the video below shows another great example. This is what we call a "knowledge handover" - a process where one group of people who have recently completed a task shares their knowledge with others who are facing the same challenge.

In this case the challenge is a huge one - passing all the departing Olympic athletes quickly and successfully through a busy airport, when each of them is carrying about 6 pieces of large, oddly shaped luggage.

The video describes some of the lessons that Vancouver Airport learned from their experience with the 2010 winter Olympics, and how they shared this knowledge with London Heathrow airport, in time for the 2012 summer games.

Tuesday, 2 February 2016

The 100th Quantified value story for KM - $100 million for Xerox - 20 years of value from Eureka

To mark our 100th quantified KM benefits story, here's a $100 million success. 

The Xerox Eureka story has already made our Hot 100, coming in at number 29, where we gave three examples of it's benefits - a $40k cost reduction in Brazil, a 10% reduction in labour costs and 10% productivity improvement in France, and savings of $15 million in one year.

Now we have another value figure from this interesting blog post entitled "Xerox’s Eureka: A 20-Year-Old Knowledge Management Platform That Still Performs."

Eureka is a simple KM model, built from the bottom-up by Xerox field agents over 20 years ago, based around an established and informal knowledge sharing culture (see the seminal 1996 study by Julian Orr called "Talking about Machines"). The initial conversations that drive knowledge sharing were supported at first by the use of annotated manuals, but then by a shared online system of field problems and their resolution, known as Eureka.

Nowadays the customer service engineers use mobile handsets and tablets to access the Eureka web platform, meaning the knowledge travels to the job with them.

According to the reference:

 Used in tandem with mobile devices, the Eureka tool provides a searchable database for quick problem-specific results at the point of need.  Though mobility may be constantly evolving, one of Eureka’s biggest benefits remains unchanged: It offers help with issues for which no standard solution seems to apply. One crucial factor in establishing trust for Eureka information was having expert technicians vet all tips submitted to the community knowledge base. Updates and details regarding product fixes have since been included from all over the globe, translated into English, and made accessible to every technician’s device.Used in combination with the mobile platform and the overall Xerox KM system, Eureka enables technicians to know immediately how to resolve issues on 80 percent of calls.
Xerox estimates that since its implementation, Eureka has saved the company more than $100 million in service costs. 

Eureka, and the roles, processes and governance that work with it, provides an excellent example of the knowledge supply chain, providing the knowledge workers with the knowledge they need, at the point and time of need, thus enhancing their productivity  and saving big bucks. The great thing about xerox is that the knowledge supply chain is short, simple and constrained, which results in its long term success.  As the article concludes - "Eureka’s longevity proves that effective knowledge management never grows old".

Monday, 1 February 2016

Revolutionising the productivity of the Knowledge Worker 4 - eliminating the waste

Last week I blogged about the challenge of revolutionising the productivity of the knowledge worker, which Peter Drucker set for us. We looked at the division of knowledge labour, the automation/augmentation of knowledge work, and the knowledge supply chain. Now we look at the lean knowledge working environment.

The lean working environment for the manual
worker (image from
Does the working environment for the knowledge
worker look like this?
We have been looking at how the productivity of the manual workers has been revolutionised, and certainly lead production, lean working and the lean supply chain have all played their part. The Manufacturing Advisory Service (quoted here) claims a 25% increase in productivity through lean principles - a small increment compared to the difference made by division of labour, automation/augmentation and an effective supply chain, but still a significant factor in the continuous improvement of productivity. Lean is also a mindset - a relentless focus on adding value on behalf of the customer and removing waste effort and stock.

Many organisations are now beginning to realise the importance of the correct knowledge reaches each knowledge worker, at the time and place they need it, to the required standard and quality, in a deliberate and systematic manner.  However our track record of delivering that knowledge in a lean and efficient way is poor, and there is little or no sign of a relentless focus on removing waste and adding value.

Knowledge bases are full and clumsy to use, poorly structured and indexed, with duplicate, outdated or irrelevant material. Knowledge workers are often required to use multiple search engines or to visit multiple sites, social media streams are unfiltered and full of noise, knowledge is unsynthesised, often unfindable, and usually is poorly tagged and labelled.

All of this makes knowledge seeking a massive chore, which it is easier to skip than undertake.

A lean approach to Knowledge Management would involve eliminating the 7 wastes, such as

  • Over-production of knowledge, which then becomes noise in the system
  • Waiting for knowledge, and a slow turnover speed of knowledge
  • Unnecessary hand-off of knowledge, with unnecessary steps in the chain between knowledge supplier and knowledge user  
  • Non-value added processing—doing more work than is necessary. We often see this in lesson-learning systems, where the work of sifting, sorting and synthesising multiple lessons or multiple search-hits has to be done by the knowledge user. 
  • Unnecessary "motion" - the need to visit multiple databases, multiple knowledge bases, a separate CoP system etc 
  • Excess knowledge inventory— frequently resulting from overproduction.
  • Defective knowledge.
Lean KM is the last of the four components to drive knowledge worker productivity. Together they can be revolutionary.

If we can have a lean and efficient knowledge supply chain, using automation and augmentation to deliver high quality knowledge to knowledge workers in a divided system of knowledge work, then we will approach Peter Drucker's initial vision of a 50-fold increase in productivity of the knowledge workers.

Friday, 29 January 2016

Revolutionising the productivity of the Knowledge Worker 3 - the Knowledge supply chain

Over the last two days I have blogged about the challenge of revolutionising the productivity of the knowledge worker, which Peter Drucker set for us. We have looked at the division of knowledge labour, and the automation/augmentation of knowledge work. Today we look at the knowledge supply chain. 

The productivity of the manual worker was revolutionised through the transformation from craftsman production to factory production. Work was divided and automated, and individuals took their part within a work chain, or production line. Partly finished work came to them automatically, together with the parts and tools they needed, they did their own tasks, added their own value, and passed the updated work on to the next person.

That's how it works for manual workers, who make things.  Knowledge workers, on the other hand, make decisions rather than things. 

The raw material for knowledge workers is knowledge. Therefore in a world where knowledge work is divided (where we do not rely on experts who carry all the knowledge in their head) the knowledge worker needs partly finished knowledge to come to them automatically, together with the knowledge tools and additional knowledge they need, and when they have made their decisions and added their own value (often this is the innovation piece), then the updated work needs to be passed on to the next knowledge worker.

This is the vision of the organisation as a knowledge factory, or a knowledge assembly line, and for this to work, we need the knowledge supply chain.

I have already blogged several times about the knowledge supply chain (here, here and here). The knowledge supply chain is a new way of looking at an organisation of knowledge workers (predicted 20 years ago by Lord Browne of BP), and for ensuring that the correct knowledge reaches each knowledge worker, at the time and place they need it, to the required standard and quality, in a deliberate and systematic manner. Knowledge Management then becomes the supply chain for the knowledge worker.

Few organisations have got this right. Perhaps the only sector where KM approaches this model is the service-desk sector, where providing correct knowledge (answers to customer questions) to the front line staff is a vital KM service.

This vision of "Knowledge Management as a supply chain" requires a complete Knowledge Management Framework to be in place, with roles, processes, technologies and governance, with the sole purpose of supplying knowledge to the knowledge workers, to enable them to make the correct decisions.

In the next and final post of this series we look at the nature of this supply chain, and what it needs to become Lean

Thursday, 28 January 2016

Revolutionising the productivity of the knowledge worker 2 - knowledge automation

Yesterday I blogged about the challenge to revolutionise the productivity of the knowledge worker, and how the first step was the division of knowledge. The second step is the automation of knowledge work. 

Image from wikipedia commons
Automation was one of the factors that helped revolutionise the productivity of the manual worker.  The blacksmiths hammer was replaced by the hydraulic press, the auger by the electric drill, the wheelbarrow by the forklift truck. Machine power augmented muscle power. Automation, combined with the division of labour, gave us the modern assembly line.

Automation is one area where KM has done well. Perhaps we should say augmentation rather than automation. As Tom Davenport points out, smart technology is not yet replacing humans, but augmenting their power. Much as a hydraulic press augments the metal worker rather than replaces him, smart KM technology augments the knowledge worker, rather than replacing her. 

  • Knowledge bases augment the knowledge worker's memory. Rather than keeping our own notebooks, or trying to remember details of a process or procedure, we can consign it to a knowledge base. And if this is a shared knowledge base, then we have access to the knowledge of many people, rather than just our own. 
  • Search engines augment our power of recall. without significant mental effort, we can retrieve a fact or an image or a process from deep within a knowledge base, thanks to the power of search, be it keyword search or semantic search. 
  • Smart technology can also take the role of a mentor, and suggest knowledge to use when we need it (based on where we are in a process or a task), even before we ask for it.
  • Technology can also augment our personal networks, allowing us to ask not just our immediate colleagues for help and advice, but hundreds or thousands of co-practitioners we may never have met.
  • Technology can augment our ability to communicate, allowing us to hold virtual meetings across continents and timezones.
  • Technology augments our ability to see patterns in vast quantities of data and information, and to apply our knowledge to make sense of these patterns, and use them to support decision making. 
Automation/augmentation is an area where already great strides have been made in helping increase the knowledge workers' productivity. However this is not the only area that needs to be addressed, and tomorrow we will look at the third component, the Knowledge Supply Chain. 

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