Above and Beyond KM

A discussion of knowledge management that goes above and beyond technology.

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This publication contains my personal views and not necessarily those of my clients. Since I am a lawyer, I do need to tell you that this publication is not intended as legal advice or as an advertisement for legal services.
  • “We’re here to tell you that size matters.”

    This is how we began a presentation today on Big Data. I had the pleasure of presenting with Maura R. Grossman, Counsel at Wachtell Lipton, Rosen & Katz, and Chad C. Ergun, Director, Global Practice Services & Business Intelligence, Gibson, Dunn & Crutcher.

    [These are my notes from the 2012 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those.]


    • What are the Hallmarks of Big Data? Some refer to the three “Vs” while others refer to the five “Vs.” All are agreed that Big Data has the attributes of huge Volume, Velocity and Variety. In addition, some would add issues of determining the Veracity and Value of the data.
    • What’s the Big Challenge of Big Data? We’ve been spoiled over the last few years — deluded into thinking that we can capture useful data, cram them into spreadsheets and then analyze them at our leisure. However, as data grow, that approach becomes increasingly unviable. According to the IDC, less than 10% of Big Data is structured data. That leaves over 90% that is unstructured content such as social media streams (e.g., Facebook, Twitter), video and images, mobile data (e.g., GPS), text messages, emails, documents, IT and operational data, transactional data, search engine data and sensor data. Our simple little spreadsheets can’t begin to handle this variety of data, much less make sense of it. Further, we can’t manually populate and analyze these spreadsheets quickly enough to keep us with the high speed at which the data are created.
    • Why Bother with Big Data? A recent McKinsey Global Institute study identified Big Data as the “Next Frontier.” Andrew McAfee and Erik Brynjolfsson of MIT say in Big Data: The Management Revolution that Big Data can measurably improve productivity and profitability:

    Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers.

    • Our Clients are Interested in Big Data Client interest and activity in the Big Data arena is growing. You can find many examples of how organizations are using the new insights that come from Big Data analysis: (1) Walmart analyzes 267 million transaction per day in order to stock its shelves more appropriately and profitably; (2) Google tracks flu trends based on search queries relating to flu symptoms; (3) medical scientists are uncovering the genetic and environmental causes of disease; (4) thousands of sensors provide data that helps scientists under the environment and changes in weather; (5) real-time analysis by computers of video feeds helps fight crime.
    • What are the Law Firm Opportunities and Concerns? The most obvious way to use Big Data analysis is in competitive intelligence research. Another possibility on the horizon is to use Big Data analytics to help us understand better the staffing, pricing and profitability of matters. Ideally, this would be done by searching all the unstructured and structured content within the firm to find the patterns. Another opportunity is in eDiscovery. The problem is that while it may be cheap to store data, it’s is still expensive to conduct an eDiscovery review of that data using current methods and technology.
    • Mashup Magic. Starting with the provocative question “What decisions could we make if we had all the information we needed?” the audience brainstormed to create a list of new data mashups that could solve law firm business problems. Among the suggestions were: (1) analyzing unstructured content to help uncover possible client conflicts; (2) analyzing emails to identify trending topics; (3) analyzing financial data filed with the Securities and Exchange Commission to find early warning signs of financial distress (or even bankruptcy) of current or potential clients; (4) comparing government economic data with initial public offering pricing data; (5) recording, indexing and analyzing phone calls to find opportunities for cross-selling; (6) comparing pitch document contents to pitch success rates; (7) comparing the content of pleadings with matter success rates; (8) comparing weather data with sales data in your law firm cafeteria.
    • What’s the Future of Big Data? For those of you who thought Big Data was a passing fad or a science fiction fantasy, think again. Surveys of senior business leaders indicate that Big Data is one of their primary strategic concerns. In fact, a recent McKinsey survey found that 60% of the CEOs and CIOs contacted believe their companies should use Big Data analytics to generate insights regarding customer preferences. Meanwhile, spending for Big Data is set to rise:

    Big data will drive $28 billion of worldwide IT spending in 2012, according to Gartner, Inc. In 2013, big data is forecast to drive $34 billion of IT spending.

  • Final Thoughts.  Used creatively and thoughtfully, Big Data can provide important insights into your firm’s operations and business environment. Our clients are embracing Big Data. Can law firms afford not to?
  • ***********************

    For additional notes on this session, see David Hobbie’s blog post.

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  • The speakers are Gene Berger (Manager of Financial Planning & Analysis, Dechert), David B. Hobbie (Litigation Knowledge Manager, Goodwin Procter) and Richard B. Friedman (Partner, McKenna Long & Aldridge).

    [These are my notes from the 2012 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • How Well-Established is their Alternative Pricing Effort? Even with strong senior support, it is an ongoing effort to bring partners around to the concept of alternative fee arrangements. There are pockets of high activity and other pockets in which AFAs are not even on the radar screen.
    • What was most surprising when they started? There were no legacy data to track client satisfaction and repeat business. From a business planning perspective, one panelist didn’t have the legacy data necessary to determine where future business development (including cross-selling) should occur. One firm had to start with basic matter coding (phase/task coding). David Hobbie noted that there were several different types of data that can be useful and you may already be tracking it. For example, they had a robust matter tracking system that proved very helpful for pricing analysis. They have had to augment this with some manual data mining.
    • Where do they start? At Dechert, they build “shells” that have the basic pricing model based on historical data. Each shell includes the following attributes: legal service line, staffing models, matter attributes (size, complexity, location, etc.). They provide this to the partner to fine-tune. Then they track actuals against budget and complete their forecasting o the basis of the budgets. Goodwin purchased Randy Steere’s budgeting tool, which is handled by administrative staff. Lawyers are given simple Excel spreadsheets into which they insert the requested data.
    • How do you track progress? At McKenna Long, they have a portfolio representation (patent prosecution) on a fixed fee basis. They have a collar arragement in place whereby they track the hourly charges. If those charges are within 10% of the agreed fee, then they will charge the actual fee. If the hourly charges are more than 10% above or below the agreed fee, the next year’s fee will be the actual fee minus the agreed fee, divided by two. In order to manage this, they track charges on a monthly basis and have a monthly conference call with the client to see how things are going. This is a method of risk-sharing with the client.
    • How to avoid going over budget? At Dechert, the partners have dashboards that report matter financial data that are updated daily. In addition, they check monthly to see if there are any unforeseen things that have occurred or are likely to occur and need to be addressed. At ReedSmith, they track the variances weekly and then have a 5-10 minute meeting with the responsible partner to discuss these differences and determine if they need to make a course correction in the way the matter is being handled or in the way the client is paying. Do this midstream. Don’t wait until the end of the month or the end of the matter.
    • What non-obvious things should we be tracking? David Hobbie says it’s important to start by asking the lawyers on the matter, what’s driving the cost. With respect to coding, Richard Friedman says to be careful not to include a “general” or “catch-all” code such as “other” because they obscure the necessary detail. This also means that you need the right number of codes that reflect your practice accurately. What’s the best way to validate codes? Do some systematic spot-checking to review the coding and then discuss differences with the partner responsible for the matter.


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  • The panelists are Thomas Kennedy (Partner & Global Head of Knowledge Strategy, Skadden, Arps), Kenneth Bender (Partner, Paul Hastings) and Jack Bostelman (President, KM/JD Consulting LLC and former partner, Sullivan & Cromwell).

    [These are my notes from the 2012 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • Why Are Partners Taking Charge of KM?Ken Bender believes that at the very highest levels of the firm, law firm leaders don’t have the negative personality traits discussed in Sally Gonzalez’s keynote address. His managing partner believes that there will be 15 firms that dominate the international practice of law and that those firms will have a well-developed knowledge management function. At Skadden, they had a change in senior firm leadership (and a McKinsey study), which led them to pursue more intentionally the idea of building a knowledge management effort.
    • Skadden’s KM Approach:For the associates, they are providing productivity tools so that they have the best training and resources to do their work. For partners, they want to provide real-time actionable information to manage their matters and support their client relationships. They also want to integrate this with their client communications. Finally, they want their KM function to be a nerve center that provides information on content and expertise. On the management side of the firm, technology, marketing, professional development report to senior management, but they also have regular meetings with Tom Kennedy and his team to discuss projects. In addition, each practice area appoints a partner who is responsible for KM efforts within their practice area. Their primary focus is their intranet presence. Their executive partner provides senior support for these efforts, which encourages participation by the partners. Skadden actively monitors user activity on practice websites, they also actively manage the efforts of each practice area to build internal support and client-facing work. Their practice group reviews include a KM component, just like they have a utilization component.
    • Paul Hastings Approach:They have hired a director of KM and several practice support lawyers. They also have great top-level support from their managing partner. Ken Bender is leading the effort. (He did not believe that a person from another firm — especially one who was not a partner and didn’t have strong relationships with Paul Hastings partners — would be effective.) Without this high-level leadership, it is hard to get buy-in from the other partners of the firm.
    • Next Steps at Skadden: A major challenge is keeping the focus on KM within the firm. In addition, they want to take more of a client focus so that their data-driven, information-driven approach meets client expectations. They are also investing in technology platforms such as experience management systems.
    • Next Steps at Paul Hastings: They want to develop some systems that they should have had years ago. This includes improving their enterprise search. They are also trying to build knowledge bases for individual practices. These will be maintained by KM attorneys. Their biggest challenge is to get lawyers within the firm to focus on something “beyond what they have to get done that day.”


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  • Sally Gonzalez is Global Chief Information Officer, SNR Denton US LLP.

    [These are my notes from the 2012 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • The Nature of Law Firm KM has Changed. The earliest form of law firm knowledge management (pre-2000) focused on know-how, current awareness, professional development and some sharing of know-how and current awareness with clients. The big challenges were getting lawyers to engage and contribute. Once the collection was built, how to maintain it and entice others to use it? If you wanted to extend this approach, how could demonstrate enough value to induce long-term investment in technology and platforms.
    • Lawyer Barriers. If you talk to lawyers, you see several themes emerging regarding why they didn’t want to share their content: knowledge hoarding because knowledge is power, the emphasis on billable hours, client willingness to pay for inefficient work, an unwillingness to expose less than “perfect” materials, political challenges in achieving consensus regarding standard form documents.
    • Lawyer Personality Traits. Based on 40 years of data derived from the Caliper Profile (measuring the generic lawyer against the average college-educated population), we find the following traits: very high levels of autonomy, skepticism, abstract reasoning (which makes them theorize that something can be more perfect), urgency (the need to jump in and get things done now). Meanwhile, they have very low resilience (which makes them highly sensitive to criticism).
    • What Worked? Know-how by trusted committees or highly-respected inviduals (practicing lawyers or practice support lawyers), submission of content by a highly-respected person other than the other, stealth inquiries for knowledge (e.g., enterprise search), promoting ways to share “good enough” stuff.
    • KM during the 2000-2007 Period. During this period, the focus was on Expertise (knowing ourselves and knowing others), the integration of KM and business development. While low personal resilience scores are the norm among lawyers, thus making them ill-suited for business development, personal resilience scores are very high for rainmakers.Query: should law firms cultivate young lawyers with high sociability, even though they may not be the best junior associates. Another challenge is that most lawyers have extremely low sociability scores. This makes it difficult for them to build relationships and to mentor others.
    • KM After 2008. Now KM personnel are asked to move outside know-how collection and into new disciplines: alternative fee arrangements, legal project management, business process improvement, etc. The challenge is that some of these KM personnel are themselves lawyers and, therefore, suffer from the same personality traits as the population to serve. Another challenge is that focus is now on teamwork, but the high autonomy and skepticism, as well as low sociability scores, make lawyers ill-suited for teamwork.


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  • Oz Benamram (CKO, White & Case) and Christopher F. Boyd (Senior Director of Professional Services) will present a framework for implementing some of the innovative ideas we’ve discussed at this conference.

    [These are my notes from the 2 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • Matter Profiling. Our dream would be to collect this information seamlessly and painlessly over the life of a matter. Unfortunately, that isn’t the reality for most firms. Many firms ask partners to provide this information before they know enough about the matter to be helpful or after they no longer care about the matter.
    • Legal Needs Assembly Lines. The legal industry is one of the few that does not have an established tradition of assembly lines. Rather, we tend to create things from scratch time after time. Nonetheless, if you examine the matter lifecycle, you have a framework on which you can hang some repeated processes. The life cycle starts with the pitch > opening the matter > completing the work > closing the matter > pitching for new work….Throughout this process, you need to manage each stage and that act of management also includes data recorded, work product produced and tools.
    • How to Use the Framework. For each phase in the matter lifecycle, you record data, create work product and associate useful tools (KM and technology). For example, during the pitch phase you record information regarding the prospective client, the lawyers/staff involved in the pitch, competitor information and the outcome of the pitch. The work product created includes company profile, a proposal, pitch slides, a pitch report. The technologies includes a matters database, a proposal generator, etc.
    • What’s in it for me? Earlier, the speakers noted that asking lawyers for information after a matter can be difficult because the lawyers have moved on. At White & Case, they have developed a great way of involving lawyers in providing matter information after a closing. The KM group at White & Case offers to create electronic closing binders. (This is something the lawyers and their clients want.) In the process of doing this, they undertake an after action review that generates useful information for their lawyers and for their clients.
    • First Steps. If possible build a dashboard called “My Matters” that shows each lawyer the tasks and financial aspects of all of their matters. If that is too ambitious, start by managing more carefully how you do the work, showing the tasks for a specific matter. This project management and reporting exposes data that will then compel people to make the necessary corrections.


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  • It was my great good fortune to attend KMWorld 2012 in Washington, D.C. When I arrived at the conference I realized that it was going to be an experience of extreme frustration — there were far too many interesting sessions offered at the same time. Without cloning myself, there was no way I could attend even a fraction of the sessions on my short list.

    On the assumption that others had this wonderful problem, I’ve gathered these links to the blog posts I wrote at the conference. Hopefully, a session you missed is covered here:

    Finally, I can’t end this post without thanking Bill Ives for suggesting that I attend KMWorld and then going above and beyond to help make that possible for me. I’ve learned a great deal over the years from Bill’s conference blogging. Here are links to Bill Ives’ blog regarding on KMWorld:



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  • Gordon Vala-Webb is former National Director, Knowledge Management at PwC Mnagement Services LP Canada.

    [These are my notes from the KMWorld 2012 Conference. Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • The charges against the accused. (1) Attempting KM alchemy. (2) Subverting KM novices. (3) Attempting to kill the knowledge management profession.
    • The theory of the DIKW Pyramid. The idea is that you start with an enormous amount of data which is then refined into information, and then refined again into knowledge.
    • 1st Set of Issues. What data to collect? (Conceptual framework) How to express it> (Language) What else is going on? (Context)
    • 2nd Set of Issues. What is Information? What is Knowledge? What is the difference? And, how do you accomplish the required KM Alchemy (i.e., turning the information “lead” into knowledge “gold”)?
    • The Top 5 KM Problems Resulting from the DIKW Pyramid: (5) Collection of data in the hopes that this will lead to information and, ultimately, knowledge. (4) Just-in-case collection and organization of content. (3) Build it and they will come. (2) Ignoring the context (of people, of knowledge objects). (1) The pyramid does not help you link your KM work to any business results.
    • A Path to An Alternative Model. What would we want in a new model? (1) Start from the desired business result. (2) Determine how you will link your KM strategy or intervention to that business result. (3) Focus your KM efforts and then measure your results (hopefully, your success). (4) Put people at the center as active doers. (5) Make sure it is context sensitive.


  • Thomas Hsu (Global KM) and Stephen Kaukonen (Senior Manager) are at Accenture.

    [These are my notes from the KMWorld 2012 Conference. Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • What’s Gamification?. Use of game elements and game design techniques in non-game contexts. (1) Game elements: the ability to earn points or badges for completing certain activities, a leader board showing the status of the players. (2) Game design techniques: this relates to the aesthetics of the game, the narrative, the journey, how the player progresses through the game. (3) Non-game contexts: applying gamification to solve business problems — even on an enterprise level.
    • Why KM and Collaboration?. KM is a perfect candidate for gamification because while collaboration is good and rewarding in and of itself, many people find it hard to do. Gamification can help people over the hurdles to starting and keep them motivated along the journey.
    • Some Examples of Gamification. (1) Fitocracy helps make you “super better” through exercise. (2) Nike Plus has built an enormous community of runners. (3)) Steptacular is an Accenture program.
    • Core Concepts. (1) Start by understanding your audience. What works for one part of your organization may not work for other parts of your organization. Conduct a user study to determine which elements of gamification resonate with your audience. One game does not fit all. (2) Impact: showing status is cheap and easier. However, it may lack meaning since it does not really demonstrate impact. Look for ways to demonstrate the impact of accomplishment within the game. At Accenture, they provide a report via gamification called “My Collaboration Impact.” It tracks activities such as posting a blog that represented thought leadership that lead to a specific number of people either commenting or reporting a new behavior.(3) Visibility: you can use gamification to make visible good behaviors and provide feedback and positive reinforcement to ensure more of that behavior. (4) Mastery: becoming good at an activity is reward in and of itself. Therefore, break the game down into logical steps that help participants progress towards mastery. The job of the game designer is to be the sherpa to help them up the mountain to mastery. (5) Autonomy: allow the player some independence, let them make some meaningful choices. (6) Purpose: this is the social element. Have the game communicate that you are involved in something bigger than yourself, you are making a difference.
    • Gamification Pitfalls. (1) Gamification won’t fix made KM. It’s like putting lipstick on a pig. Therefore, be sure that your KM approach and processes are good before adding gamification elements. (2) Making games is easy. It may be fun (at least serious fun), but it isn’t just a matter of slapping badges on something. (3) Focus on behaviors not activities. While activities are components of behaviors, they don’t by themselves bring about long-term change. (4) Da
    • Focus on Behaviors not Activities. While activities are useful and necessary components of behavior, they don’t by themselves bring about long-term change. Therefore, focus on the long-term change you are trying to achieve and then construct the game to help the player to complete specific tasks that will help cultivate the desired behavior.
    • Data is King. A well-designed game can help generate huge amount of useful data.
    • Spread the Recognition. Find different ways of recognize accomplishment. Don’t limit yourself to badges. Realize that sometimes a note from a senior executive will be more meaningful.
    • People Will Game the System. This is a fact of life. Therefore, set limits on the numbers of point you can receive for a specific activity. Equally, don’t communicate exactly how many points you can earn for particular activities because you don’t want people to focus solely on high-point activities or a large volume of low-point activities. Finally, remember that if you offer a prize like an iPad you will be inviting people to seriously subvert the game.
    • Start small and then evolve.. Don’t worry about getting it right immediately out of the box. Plan to iterate.
    • Gamification is Not a Silver Bullet.


  • Ian Coyne is Sector Knowledge Manager, Russell Reynolds Associates. Using the experience of KPMG as they tried to answer the “Brave Banana” problem, he shares insights and tips on how to use crowdsourcing to share knowledge.

    [These are my notes from the KMWorld 2012 Conference. Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • The Brave Banana Problem. “Late one night, a KPMG partner in the U.K. sent an email to 600 people asking for solutions to what he thought was an unsolvable problem: How could you peel a thousand bananas at the same time?” They reason he was asking was that a client was a maker of banana ice cream, but didn’t have a reliable and affordable means of getting peeled bananas to make that ice cream. Through this crowdsourcing effort they found at least one credible answer that was worth testing. (For those of you who are curious, the respondent had studied food sciences in university and proposed putting a banana in vacuum, which would cause the skin to split and eject the peeled banana. The idea didn’t ultimately work, but it definitely impressed the client.)
    • Lessons Learned. (1) Learning from failure: asking people about general work topics didn’t work that well. Equally, asking them about purely personal topics in a vacuum didn’t work either. What did work was asking a question for the purpose of serving a client. Once the crowd understood the reason for the question they were willing to respond for the sake of the client.(2) People do something if they are asked by their friends and peers. They are less likely to act if requested by a remote senior leader. (3) Make it easy. Ask questions to which there is no wrong answer. All that is required is to have an opinion that you are willing to share. (4) Don’t offer financial rewards — it encourages the wrong kind of behavior. (5) Seek forgiveness not permission. This is particularly important if you’re attempting something new for your organization. (6) Be brave. Doing simple stuff is less likely to have a transformative effect on your organization.
    • Methods for Tapping the Wisdom of Crowds. To begin with, don’t use corporate speak. Use every day language, not formal corporate messaging. In addition, don’t send an organization-wide request since that will feel a bit more like a corporate edict. Start by sending emails to a target group and see what kind of response you receive. Another method is to set up a simple discussion board (this can be done in SharePoint) and invite people by email to participate.
    • Don’t Expect the Ultimate Answer. In the Brave Banana example, they were hoping for the ultimate answer, but found that a credible answer was sufficient to impress the client. For most crowdsourcing exercises, focus on gathering as many answers as possible. Even if one of the ideas isn’t the perfect answer, it may point the way to the right answer. Equally, don’t edit or cull the responses — you don’t always know what will resonate with the client. Therefore, give the client ever single answer received. To make a little order out of the chaos of seemingly random answers, try grouping all the responses and then present the major themes to the client (supported by copies of all the answers).
    • Where do you draw the line between crowdsourcing and focus groups?. When you need responses from a specific demographic, it makes sense to organize a focus group. (You can do this by sending the email to a limited group of people.) However, if you want the widest range of responses, regardless of source, then make it wide open and try crowdsourcing.


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  • Jeremy Bently is CEO and Founder of Smartlogic.

    [These are my notes from the KMWorld 2012 Conference. Since I'm publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I've made any editorial comments, I've shown those in brackets.]


    • Unstructured Content. Unstructured content is highly varied: it can range from a Twitter feed to a Word document or a scanned image. It can cover a range of subjects — as many subjects as arise in our lives.
    • Information Management, circa 1950. In the 1950s, the focus was on manual tagging, content management, indexing, search and distribution. What is the same today? The scope of infomration and the process (e.g., tagging, indexing, search, etc.) The great crime is the continuing reliance on manual tagging. What is different? The techhnology and variety of information has changed. Further, we’ve moved from “information overload” to what it is currently called: “Big Data.” (Calling it Big Data suggests that we can cope with it, in a way that we couldn’t cope with Information Overload.) Other changes are the velocity of information and the complexity of requirements. The complexity relates to different audiences interested in different aspects of the information we have. It also relates to the different uses of that information.
    • Flow. For the purposes of information management today, Flow = velocity X volume. (This is not entirely accurate according to fluid dynamics, but works for KM.) Being able to harness information in real time (in that flow), gives you a competive advantage and efficiencies. It is the relationships between data that present the opportunities.
    • Content Intelligence Allows You to “Enrich” Your Content. This is now the Holy Grail for organizations. Knowledge our content helps us find opportunities, gives us competitive advantage and helps us stop it from leaking out of the organization. At the heart of content intelligence is labeling = metadata. Another key element of content intelligence is extracting the key information. We also need to classify the content so that we can provide indicators as to what it’s about. Given how much content there is and how many topics are covered by that content, it is impossible to manually tag it all effectively (especially since you don’t know who is looking for content and what they are looking for). Therefore, deriving metadata should occur at the point of use, not at the point of archiving. This is a huge reason why manually tagging can’t work. Content needs to be integrated with the existing collection. Finally, it needs to found so we need tools to help surface the content relevant to the person looking for it.
    • Closing Questions. Can you afford the risks inherent in manual tagging? Can you afford to ignore customer feedback via huge unstructured data flows (e.g., via social media)? Do you have the means to track trends reliably?


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