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Archive for the ‘Enterprise Search’ Category

by: Robert.hillard
26  Nov  2016


Information overload is as much an overwhelming feeling as it is a measurable reality. We often feel an impossible obligation to be across everything, which leaves us wanting to give up and absorb nothing that hits our various screens. Despite all this, the good news is that the majority of the information we need seems to appear just in time.

Where does that leave those of us who are control freaks? I am not comfortable to know that the right information will find me the majority of the time. I want to know that the information I need is guaranteed to find me every time!

The trouble is, guarantees are expensive. This is related to the debate between search based big data solutions and enterprise data warehouses. Google provides a “near enough” search solution that, given the massive amount of data it trawls through, usually seems to find what we need. Knowledge and business intelligence solutions provide the predictable information flows but come at a huge cost.

Of course, the real sense of serendipity comes when information arrives unsought just when we need it. It can come through the right article being highlighted in a social media feed, a corporate policy being forwarded or the right coffee conversation with a colleague. Of course, serendipity isn’t random coincidence and there is much we can do to improve the odds of it happening when we need it most.

Before doing so, it is important to know what things have to be predictable and reliable. A list is likely to include financial reports, approvals and other controls. What’s more, a scan of any email inbox is likely to show a significant number of messages that need to be read and often actioned. Despite its tyranny on our working lives, email works too well!

Serendipity depends on the quality of our networks, both in terms of who we know and the amount of activity the passes between the nodes. A good way to understand the power of relationships in an information or social network is through the theory of “small worlds” (see chapter 5 of my book Information-Driven Business).

Ironically, in an era when people talk about electronic isolation, social networks, that is who we know, are more important than ever. Serendipity relies on people who we know, at least vaguely, promoting content in a way that we are likely to see.

Just as control freaks worry about relying on serendipity, those that are more relaxed run the risk of relying too much on information finding its way mysteriously to them at the right time. Those that don’t understand why it works, won’t understand when it won’t work.

Far from making experts and consultants redundant, this increasing trend towards having the right information available when it’s needed is making them more necessary than ever before. The skill experts bring is more than information synthesis, something that artificial intelligence is increasingly good at doing and will become even better at in the near future. The job of experts is to find connections that don’t exist on paper, the cognitive leaps that artificial intelligence can’t achieve (see Your insight might just save your job).

The first thing is to be active posting updates. Networks operate through quid quo pro, in the long-term we get back as much as we give. In the office, we call this gossip. Too much gossip and it just becomes noise but the right amount and you have an effective social network. Those people who only ever silently absorb information from their colleagues quickly become irrelevant to their social circle and gradually get excluded.

The second is to be constantly curious, like a bowerbird searching and collecting shiny pieces of information, without necessarily knowing how they will all fit together. The great thing about our modern systems is that massive amounts of tagged content is easy to search in weeks, months and years to come.

Finally, have some sort of framework or process for handling information exchange and picking a channel based on: criticality (in which case email is still likely to be the best medium), urgency (which favours various forms of messaging for brief exchanges), targeted broadcast (which favours posts explicitly highlighted/copied to individuals) or general information exchange (which favours general posts with curated social networks). Today, this is very much up to each individual to develop for themselves, but we can expect it to be part of the curriculum of future generations of children.

No matter how often it seems to happen, almost by magic, information serendipity is no accident and shouldn’t be left to chance.

Tags: ,
Category: Enterprise Content Management, Enterprise Data Management, Enterprise Search, Enterprise2.0, Information Strategy, Information Value
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by: Realstorygroup
20  Nov  2010

ECM3 meets MIKE2.0

ECM3 ( has been without a doubt the most successful maturity model for ECM (Enterprise Content Management – aka Document Management) with downloads of the model passing the 5,000 mark recently. So how to top success with more success? Well we have decided to merge efforts with MIKE2.0, the de-facto maturity model for structured data. Our hope is that by adding our work in unstructure data to MIKE2.0, that we can spread the love even further and help raise the profile and importance of ECM.

Enterprises face ever-increasing volumes of content. The practice of Enterprise Content Management (ECM) attempts to address key concerns such as content storage; effective classification and retrieval; archiving and disposition policies; mitigating legal and compliance risk; reducing paper usage; and more.

However, enterprises looking to execute on ECM strategies face myriad human, organizational, and technology challenges. As a practical matter, enterprises cannot deal with all of these challenges concurrently. Therefore, to achieve business benefits from ECM, enterprises need to work step-by-step, following a roadmap to organize their efforts and hold the attention of program stakeholders.

The ECM Maturity Model (ECM3) attempts to provide a structured framework for building such a roadmap, in the context of an overall strategy. The framework suggests graded levels of capabilities — ranging from rudimentary information collection and basic control through increasingly sophisticated levels of management and integration — finally resulting in a mature state of continuous experimentation and improvement.

Level 1: Unmanaged
Level 2: Incipient
Level 3: Formative
Level 4: Operational
Level 5: Pro-Active

Like all maturity models, it is partly descriptive and partly prescriptive. You can apply the model to audit, assess, and explain your current state, as well as inform a roadmap for maturing your enterprise capabilities. It can help you understand where you are over- and under-investing in one dimension or another (e.g., overspending on technology and under-investing in content analysis), so you can re-balance your portfolio of capabilities. The model can also facilitate developing a common vocabulary and shared vision among ECM project stakeholders. And it is our fervent hope that the ECM model we work we started, will be continued, expanded upon and itself mature with the MIKE2.0 community.

Tags: , , ,
Category: Enterprise Content Management, Enterprise Search, Information Management, Web Content Management

by: Phil Simon
04  Oct  2010

Part III: The Semantic Web and Complementary Technologies

In my previous post, I discussed the business case for the semantic web and how it is affecting customer service. In the third part of the series, I address the semantic web in the context of specific enterprise technologies.

IE: No, the Other One

Throw out the term IE and most people think of one of the following:

  • Internet Explorer
  • The Latin term id est (i.e., or i.e.). This typically means that is. Example: Phil didn’t embarrass himself on the golf course today–i.e., he shot an 85. (Which I did, by the way, a few weeks ago. Polite applause…)

But there’s another type of IE and it’s critical from a semantic technology perspective: Information Extraction.

In their book Semantic Web Technologies: Trends and Research in Ontology-based Systems, John Davies, Rudi Studer, and Paul Warren define this type of (IE) as “a technology based on analyzing natural language in order to extract snippets of information.” IE allows users to easily find five types of information:

Contrast IE against what most enterprises rely upon today: basic information retrieval (IR). IR finds relevant text and returns a simple list. While this is useful, IR forces the user to determine the most relevant piece(s). IE, on the other hand, automatically does this analysis for the end user; it only returns the most germane results and, most important, in a superior format. This may be a spreadsheet that allows for sorting, filtering, and adding fields. In other words, there is greater context.

An Example

All of this may seem a bit abstract. Let’s make it more real. Consider searching for golfers with Google. No doubt that you know what basic Google search results look like. However, what if you performed that same search using a semantic technology? Consider the output below:

Pretty neat, eh? Note how each golfer’s picture, name, and date of birth appear by default. But what if you want to see more fields, such as earnings? No problem. Just type in earnings and you can see just how much money folks like Tiger Woods have made by being able to hit a white ball (when he’s not, er, doing other things).

Semantic Technologies

To make magic like this happen obviously requires, among other things, a great deal of technology behind the scenes. Beyond technology, however, accurate data, metadata, and tags are needed.

IE is more efficient and ultimately useful than IR. However, the technological requirements for IE far exceed those of IR. While IR can rely upon simple keywords or text, IE requires much more, often including technologies such as:

  • Natural Language Processing (NLP)
  • Artificial Intelligence
  • Machine Learning
  • Data Mining

To the layperson, all of this is irrelevant. They merely want a way to solve a problem, such as reducing the time required to find the most relevant emails. To this end, consider what one technolgoy company, Meshin, does. It takes a semantic approach to email, using NLP and other technologies to ultimately locate relevant emails quicker and better than traditional methods.

Simon Says

Information extraction trumps information retrieval. However, let’s remember that we’re in the early stages of Web 3.0 and the economy isn’t great. For these reasons, don’t expect simple IR to go away anytime soon. To be sure, accurate IR is certainly far better than the dismal search functionality of Web 1.0 in the 1990s. As semantic technologies develop, the social web matures, and the economy improves, expect search and the semantic web to do the same.

Don’t believe me? Check out what Eric Schmidt of Google said on a recent interview with Charlie Rose. Things are going to get interesting over the next five years.


What say you?

Tags: ,
Category: Enterprise Search, Semantic Web
1 Comment »

by: Robert.hillard
30  Sep  2007

Using Enterprise Search as an information tool

There is huge interest from clients in enterprise search, with the focus being how to create useful applications that go beyond documents or web pages.  Increasingly, we’re seeing organizations that have invested in metadata for regulatory compliance discovering the value of this asset using search technologies and techniques.

The original web experience was intended to be click-based navigating via a number of hubs to any point in the internet, but the last five years has seen the majority of users move to a language-based approach starting with a site like Google or Yahoo.  The example I often use is the rain radar, often when setting out to a meeting in a city I’ll check to see if rain is coming.  In Melbourne I can navigate from the website to the radar but it’s faster for me to type “Melbourne weather radar” into Google, with the added benefit that I can use the same interface when I’m in Auckland, Singapore, New York etc..

At work, users are still in the late 90′s relying on incomplete intranets and a poorly maintained web of links.  The problem is primarily access to the structured repositories and even more importantly access to the structures of those repositories (ie., the metadata.

In many cases, banks have been the early adopters of metadata repositories followed by insurers and then the very large government departments.  The main driver for these repositories has been compliance and (for banks) risk (Basel II).  These repositories are enormously rich in content, but extremely difficult to interface to the rest of the organization’s information.  Search can be the solution and I recommend the following three steps:

1. Interface to metadata repositories
In a bank, a user should be able to search for “Risk Weighted Asset” and find not only the relevant documents but also a list of the systems and databases that contain relevant data as well as appropriate controls, processes and business rules.  It isn’t difficult to build interfaces between structured metadata and the search tools.

2. Interface to master data
The next step is to build an interface that allows the user to type “Assets Walmart 2005″ and find, via the metadata, appropriate queries which can then be launched in a BI tool (eg., Business Objects or Cognos).  This is part of my view that search should be the kick-off point for all information analysis.  Again, this sounds difficult but really isn’t, you can use the metadata repository to define the dimensions of search and emulate hints (ie., “Did you mean xyz”) to help if the user is almost on target.

3. Better analysis of the quality of search
The search index increasingly becomes an asset in its own right.  Using the techniques in MIKE2.0, we can use do constant health checks on the usability and relevance of the search index itself.

Category: Enterprise Search, Enterprise2.0, Information Strategy, MIKE2.0, Web2.0
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