openqabal

Name:

Technology Radar

 

Intent:

              Identify technological developments - which may present either a threat to the enterprise, or a groundbreaking new opportunity - as early as possible. 

 

Description:

New technologies are being developed at a dizzying pace.  Worldwide, private enterprises, academic researchers, and open-source hackers are all constantly pushing the envelope, developing new approaches and tools.   Some of these advancements may represent a huge threat to your organization, perhaps by enabling a competitor to cannibalize your existing business model with a much less expensive alternative.  Others may represent an opportunity to break new ground with products, product features, or services that can represent sizable new revenue streams. It is advantageous to identify these advances as soon as possible, in order to out maneuver the competition and take maximum advantage of new developments.

 

As Downes and Mui point out in their bookUnleashing the Killer App, this kind of awareness requires a technology radar consisting of a fat pipeline, a sensitive radar screen and sophisticated intelligence.

 

Solution Story:

              At MegaCorp, developers of the market leading Flozzit product, leaders are constantly jousting with rival HyperCorp, each striving to release the most advanced product in order to steal customers from the other.  Recently, HyperCorp has released a multiple new versions with features that no one at MegaCorp had considered, or believed possible at the time.  After the most recent release, MegaCorp leaders dug in and discovered that HyperCorp had integrated advanced technology developed by researchers at Miskatonic University.  “Why,†asked MegaCorp CEO Howard Phillips, “did we not know about this sooner? This is actually a better fit for our product..  if we had done this first, we could have taken a huge chunk of HyperCorp’s market share, instead of letting them jump further in front of usâ€Â  No one had an answer.

              In order to address this lack of awareness of emerging technologies, MegaCorp decide to implement a Technology Radar.  An “emerging technologies†channel is created, where every member of the organization can submit links to documents, articles and news-feeds that touch on technologies related to MegaCorp’s industry.  Users throughout the organization vote, tag and comment on each submission, allowing the collective intelligence of the organization to filter the less important items, while pushing the key ones to the top.  Product Managers and executives begin to make browsing the latest ‘top items’ on the channel a routine habit... and some users configure the system to send them a dynamic alert via instant messaging when an entry reaches a certain score.

              A few months later, the Flozzit Product Manager receives such an instant message... the link is to an article published by Arkham University, announcing the development of a  new algorithm which solves a problem that MegaCorp engineers have been struggling with.   AU has released the source code under a permissive open-source license, and MegaCorp begin integrating the new approach, and also recruit two of the students from AU who worked on the project.

              Using the new technology, MegaCorp are able to deliver a new release of Flozzit with several features which they believe that HyperCorp cannot match.  Amazingly, the new HyperCorp release comes out six weekslater, and is almost totally equivalent to the new Flozzit.

CEO Phillips talks to his mangers and explains why he’s happy with developments; “We weren’t able to leap-frog them this time... but if we hadn’t rolled that new stuff out when we did, their new release would have been a dagger into our heart.    Now we’ve shown them, and the market, that they aren’t always the ones on the forefront of technical advancements.  And the two new guys we hired from Arkham are already hard at work on some stuff that’s going to blow everybody away.â€

             

Vintage:

Mature Commercialization             

 

Challeges:

Corporate culture which fosters a “Not Invented Here†syndrome. 

Lack of incentives for participation in the system.

Lack of belief in the utility of the system.

Lack of participation in the system by executives and other decision makers.

 

Forces:

              TBD

 

Business Results:

              Better awareness of technological advances which are significant to the organization.                            Ability to gain early mover advantage over competitors by incorporating advances              

sooner.

Lower risk of being one-upped by the competition with a significant technical             

advancement.             

 

Capabilities:

              Share links to web-sites, documents and other items of interest

              Categorize links by topics using channels

              Tag links with specific keyword

              Rank items by voting them “up†or “downâ€

              Search and filter by topic, keyword, and/or score

              Sort view by various statistical measures, such as “all-time scoreâ€, “hotnessâ€, and              

“controversiality.â€

Dynamic alerting, via email, instant messaging, etc.,  when items reach certain thresholds.

 

Typical Use Scenarios and Guidance:

              A technology radar is established to pull in information from many disparate sources:

              RSS feeds, Twitter streams, email lists, and user submitted links to websites, documents               and articles.  Collaborative filtering through collective intelligence is used to filter the                             lower value submissions, while ensuring the relevant information gains visibility.               

Employees through the organization view the radar, through the “emerging technologies†             

channel and take advantage of the information. 

 

In some cases this may represent a “bottom up†scenario, such as an engineer finding an interesting new library which enables a feature the engineer likes... he quickly knocks out a prototype, shows it to senior management, and it is eventually adopted into a product release.  In another case, this may be a “top down†scenario, where a senior leader discovers a new technology, and issues a mandate that R&D investigate it’s applicability to their product.

 

Applicable Technologies:

              Fogbeam Labs “Project Shelleyâ€

Other corporate knowledge repositories (blog servers, forums software, document              

management systems, HR management systems, etc.)

Existing Data Warehouses / Databases / Knowledgebases

External information sources (web pages, databases, etc.)

 

Implementation Effort:

              TBD

 

Integration:

              Project Shelley can easily integrate any knowledge source which can be accessed via

HTTP and which exists in a format which can be parsed into text tokens for indexing by              

Lucene.  Where text extraction is not possible, location through metadata is still possible (ex, mp3 audio files, video, etc).

 

 

Integration Mechanism:

              RSS feeds, HTTP, OpenSearch

 

Integration Status:

              TBD

 

Name:

Collaborative Filtering for Information Retrieval

 

 

Intent:

              Use voting/ranking by individual users to tap into the “wisdom of crowds†effect to filter /                             select the most relevant information in a given context.

 

Description:

Knowledge workers - and especially executives - face nonstop demands on their time, and have to make key decisions in ever decreasing time spans, in order to adapt to the rapidly changing business environment.  Balanced against the need for rapid decision making is the need to consider and evaluate as much available information as possible before making a decision.  The information needed to make correct decisions often exists, either within your enterprise - often locked away in the collective, accumulated wisdom of every member of the organization - or somewhere outside your enterprise. In either case, it can be nearly impossible to solicit the correct information before risking a strategic mistake.  In any medium to large organization, it simply is not possible to review every document and poll every employee, customer, partner and vendor before executing a decision.  Even if time were available to do this, a small nugget of essential information could easily be lost in the sea of noise.  However, technological tools make it possible to rapidly filter, rank and correlate various sources of knowledge, helping to ensure that what is important makes it to the people who need it, despite it’s origin.

 

A group of individuals can often be “smarter†than any one member of the group.    By aggregating the wisdom of individuals via voting / ranking / correlation using collective intelligence it is possible to tap into the wisdom of crowds effect within your organization.   Collective intelligence ensure that relevant information is seen by those who need to see it, even if it “bubbles up†from an otherwise obscure source.

 

Solution Story:

 

              At MegaCorp, the worldwide leader in enterprise software with their flagship Flozzit product; sales were down and managers were scrambling to increase revenues.  A group of managers decided that the solution was to create a new, feature-enhanced Flozzit 3.0, which would add missing features and solve long-standing issues that were resulting in lost sales.

 

              Lacking sufficient resources to add every desirable feature, it was critical that the Flozzit Product Manager identify the features which would most directly impact sales.  So the Product Manager began scouring over enhancement requests in the bug database, and scanning old emails from account managers, field reps and engineers.  After a few weeks work and several meetings, the PM thought she had a pretty good handle on which features should go into Flozzit 3.0.

 

Before committing resources to the new roadmap however, she decides to peruse the “Flozzit†channel on the PS portal, and look for items tagged “complaint†or “enhancement.â€Â  At the top of the list is a report written by a customer support representative (who the PM had never met, or even heard of; she wasn’t even sure if he was still with the company) titled “Why BigCorp hates Flozzit.â€Â  Intrigued, she examines the filtering metadata and sees that nearly every CSR in the company has upvoted the report, as well as one or two of the engineers.   She downloads the report and digs in, to find a detailed summary of the top issues that end-users at BigCorp (the largest customer of Flozzit!) had complained about when talking to the CSRs.  The language was detailed and some of it was not kind to Flozzit.  After reading the report, the PM arranges to meet with the CSR who wrote the report, and identifies 5 top issues which had never been discussed in the many meetings held to identify the new Flozzit 3.0 roadmap.  She then calls her top contact at BigCorp to discuss the issues and the first thing he says about issue #1 is “Yes, our users have been very concerned about that.  We noticed that HyperCorp is releasing that feature in their 4.0 product and might consider switching if MegaCorp doesn’t answer soon.â€

 

Armed with this new information, the PM polls a sample of other Flozzit customers about the 5 issues identified and find that 3 of them are so important that they must go into Flozzit 3.0.

 

Six months later Flozzit 3.0 ships with the 3 new features and a slew of bug fixes.  BigCorp immediately commits to an upgrade, and are so happy with the new version that they purchase another 50 licenses a few months later.

 

Vintage:

Mature Commercialization             

 

Challenges:

              Corporate culture which stifles dissent. 

Lack of incentives for participation.

Lack of belief in the utility of the system.

Unsupported document formats, databases with proprietary formats which are difficult to

integrate.

 

Forces:

              TBD

 

Business Results:

              Better identification of actionable news and information which might otherwise remain              

lost in the sea of information inside the enterprise; leading to better decision making at

both the strategic and tactical levels.

 

Capabilities:

              Share links to web-sites, documents and other items of interest

              Categorize links by topics using channels

              Tag links with specific keyword

              Rank items by voting them “up†or “downâ€

              Search and filter by topic, keyword, and/or score

              Sort view by various statistical measures, such as “all-time scoreâ€, “hotnessâ€, and              

“controversiality.â€

 

 

 

Typical Use Scenarios and Guidance:

              Knowledge workers view channels of topical concern to their jobs, or of general interest, on a regular basis, voting and tagging existing items, commenting on existing items, and submitting new items, creating a view of what’s important - and adding to the corporate memory - using collective intelligence.

              Knowledge workers discover relevant information through casual browsing; and through directed searching by tag, channel, submitter, score, or other attribute, when specific topics are under review.   By limiting causal browsing to the most highly ranked items, an employee can maintain a “finger on the pulse†of what is considered important at a point in time, wthout reviewing every item.  But directed search makes all of the other items accessible when they are relevant to a topical query.  

 

Applicable Technologies:

              Fogbeam Labs “Project Shelleyâ€

Other corporate knowledge repositories (blog servers, forums software, document              

management systems, HR management systems, etc.)

Existing Data Warehouses / Databases / Knowledgebases

External information sources (web pages, databases, etc.)

 

Implementation Effort:

              TBD

 

Integration:

              Project Shelley can easily integrate any knowledge source which can be accessed via

HTTP and which exists in a format which can be parsed into text tokens for indexing by              

Lucene.  Where text extraction is not possible, location through metadata is still possible (ex, mp3 audio files, video, etc).

 

 

Integration Mechanism:

              RSS feeds, HTTP, OpenSearch

 

 

Integration Status:

              TBD