Measuring the Business Value of KM

Every knowledge management (KM) initiative needs to showcase results and business impact. Unfortunately, many organizations have no agreed business measures for their KM programs, and focus on metrics reporting without actually understanding whether value is being realized. Getting it right demands linking programs and investments directly to business outcomes, and reporting to leadership in terms they can comprehend.

The Role of Metrics

Because knowledge is an intrinsic asset to organizations, it is difficult to put a value on it, or the impact of KM efforts. This is critical because KM efforts represent a real investment, spending money and/or resources on technologies, dedicated staff, and changes to business process.

It is not always clear how to show a linkage between these investments to business value, and it is often tricky. For example, a common-use case in consulting firms is improving the quality of the proposal process through knowledge and collaboration, with the goal of improving win rates on new bids. The question is how to isolate and understand the impact of KM initiatives on the win rate, given that there are so many variables (people, geography, training, competition) influencing this critical business measure. But business leaders want and need to know the impact nonetheless.

Reporting should focus on outcome metrics. Outcomes need to be measured and communicated in business terms that align with the organization’s strategic objectives.

Most Organizations Fail

Because of this challenge, most organizations unfortunately revert to creating large binders full of “input” metrics, typically pulled from web analytics reporting tools. In our proposal example, this would include things such as searches and hits on the proposal repository, document downloads, click-thru rates, and so forth. These are often very detailed and impressive looking. And while these may provide much detailed information for the team managing the system day to day, this is not what business leadership wants to see. Leadership needs to know the impact of these investments on their win rates to understand how the investments can be improved over time.

This leads to frustration on the part of business leaders and CFOs, and it has the potential to prevent KM leaders from staying fully relevant to the business. And not understanding the business value created is also one reason why some organizations abandon their KM programs altogether.

Measuring Outcomes and Results

The key to overcoming this challenge is to focus reporting not on “inputs,” but on “outcomes.” Outcomes measures need to be communicated in business terms and aligned with the strategic objectives of the business unit. This isn’t always easy to do, and successful programs follow several common principles:

  • Keep it simple. Pick no more than five high-level measures—focused on business outcomes, not KM inputs, and linked back to the business drivers/goals of your strategy. This is hard to do but is where you will structure the right dialogue with business leaders.
  • Make it financial. Find creative ways to show tangible impact and turn it into financial value. For example, a recent KM initiative in a global consulting firm was able to save, on average, 40 hours of time to prepare each proposal. When multiplied by the value of staff time, these savings represented a very large financial benefit to the business, and was the genesis for leadership to roll out this same initiative elsewhere.
  • Use your people. The best way to capture impact is by talking to your users. Users will know when new tools and resources are helping them do their jobs, save time, or win new work. Regular surveys and interviews of key staff are critical for acquiring feedback on KM initiatives. This will also help you obtain success stories and testimonials from notable users—another key to enhancing the dialogue with business leadership.
  • Forget ROI. As tempting as it sounds, don’t attempt to calculate a holistic financial ROI calculation for KM. It is a losing battle and will cause a loss of focus.

KM and Compliance

Compliance is now a reality within every organization, requiring complex efforts to pull together, organize, and report vast amounts of their critical information to the government, regulators, or other entities. Conventional thinking says these requirements are an administrative burden; but in reality they are a catalyst for any organization to discover new insights and realize business results.

Defining Compliance

Compliance refers to a requirement for an organization to obey the laws, regulations, standards, and policies with regard to how it manages the business, their staff, and customers.

Compliance requires the capture and reporting of data, as organizations must collect and submit vast quantities of data on a regular basis to regulators, with serious consequences for any errors or misleading conclusions. Compliance also frequently requires the detailed review and validation of internal business processes, as well as IT systems used to process data on financial transactions, customers, and other business functions.

Compliance requirements are most onerous in heavily regulated industries such as financial service and healthcare, but all organizations in any sector are now subject to some sort of compliance reporting.  These requirements create many challenges (and costs), such as:

  • Data aggregation (pulling information on financials, customer, suppliers, and other entities from disparate systems and a range of formats). Data needs to be scrubbed, organized, and produced in reports, and this work is often conducted manually.
  • Duplication. Compliance reporting covers data from risk, finance, customer transactions, and human resources systems, and there is a significant overlap between different sets of regulatory requirements. As a result, there is potential confusion and wasted effort from preparing submissions.
  • Conflict with privacy laws. Newer laws and regulations ask data owners to retain extensive records with personal data beyond the time necessary for normal business operations.

Meeting these demands can be incredibly costly for organizations, often require hiring dedicated staff or consultants, and consume significant management attention. But are these costs just a useless administrative burden? Are there other business benefits?

Compliance can be viewed as a regulatory burden or as a catalyst to gain a deeper understanding of business operations.

The Opportunity to Unlock Value

There is a huge opportunity to harness the power of compliance-related data to improve the quality of decisions and business outcomes. Regulation can be a catalyst for improved data management, analysis, and reporting, offering some remarkable insights. Organizations with a strong data culture and a systematic approach to data management, reporting, and analysis will better understand customer behavior, identify market trends earlier, and compare the performance of business units and teams.

For example, healthcare providers collect and store vast amounts of patient and clinical data for compliance purposes, including socioeconomic, environmental, biomedical, and genetic factors; individual health status and health behaviors; as well as resource use, outcomes, financing, and expenditures. Pulling together these data (in an anonymized way), which are typically stored across electronic health records (EHRs), personal medical records, disease registries, and other databases, generates new insights on clinical guidelines, patient outcomes, and new areas for research. Healthcare providers who can apply these insights better than others have the potential to stand apart from their peers.

Another compliance-heavy industry is banking, where consumer-lending businesses are subject to compliance requirements such as Truth in Lending, Fair Lending and the Community Reinvestment Act (CRA). Banks are required to collect and report on all details of their lending activities to ensure compliance with these regulations. Many banks are now integrating this information with data on loan performance, market demographics and competitive intelligence, residing in different systems, to generate new insights on their business. Banks can use these insights to optimize their marketing, promotions and pricing activities, accelerating growth and profitability within the requirements of the law.

These and other stories don’t simply happen on their own. Organizations should take the opportunity to think systematically about how they can best apply and use the new insights which come from their compliance efforts.

Making It Happen

Best practice organizations generally apply several common lessons in harnessing lasting value from the Compliance process.

  1. Integrate data management across the enterprise

Given the high degree of commonality between different regulations, now is an ideal moment to establish standardized ways of managing data for every type of compliance requirement. By investing in middle- and back-office systems, and building an enterprise-wide data infrastructure, organizations can “do it once and do it right” with a single data warehouse rather than creating separate processes to satisfy each regulatory body. An integrated view of data across the business should lead to more consistent, accurate reporting, with clear sources that can be traced instantly.

  1. Align it with the business

Making this work requires senior business involvement and engagement. One way to do this is by creating a chief data officer (CDO), who would oversee how data is gathered, managed, protected, and monetized. He or she is a champion for global data management, governance, quality, and vendor relationships across the enterprise, working closely with the CIO and the head of compliance. As a senior business executive the CDO is ultimately charged with doing these to enhance business outcomes.

Another approach is to embed compliance-related insight into the accountabilities of business leaders by building this into regular management reporting to demonstrate how they are utilizing these new insights to improve their business.

  1. Focus on insights tied to business challenges

The biggest challenge for organizations is how to operationalize this vast compliance-related data into insight and value.

A common pitfall is to make data and reports the overriding priority, which results in lost momentum long before the first insight is delivered, frequently because a data-first approach can be perceived as taking too long before generating a financial return.

On the contrary, a key role of the business managers, supported by the CDO, is first to define the insights and questions needed to meet business objectives, and then identify those pieces of data needed for answers. By narrowing the scope of these tasks to the specific subject areas needed to answer key questions, value can be realized more quickly, while the insights are still relevant.

The insights delivered through this initial work will illuminate gaps in the data infrastructure and business processes. Most importantly, this approach can lead to organizations embedding this expanded data into standard business reporting and metrics, and using it to drive better decisions.

*          *          *

It is time for organizations to fully embrace their compliance requirements.  Every cloud has a “silver lining,” and this is an opportunity for business leaders to shift their mindset to one of opportunity.

KM Renewal

While some organizations have yet to embark on organized knowledge-sharing programs, others have well-established document repositories, people-finding directories, and intranet sites highlighting important content and ideas. However, many organizations find that their KM initiatives run out of steam after a while - knowledge capture momentum slackens off, people go back to relying on their immediate networks for answers, and the whole effort gradually loses relevance to front-line operations. What causes this drop-off and what can be done to re-energize KM in these situations?

When diagnosing the causes of a stalled KM initiative, several questions are worth asking, in order to get at the root causes.

  • Why did we want KM in the first place? Successful KM programs are always tightly linked to the organization’s strategic goals - leveraging prior experience to generate real impact on growth and operational efficiency. Look at whether these goals have changed since KM started up - do the content strategy, organization of knowledge, and key performance indicators need to be updated to reflect new objectives? Is KM enabling the “front line” staff or has it become another back-office utility?
  • Were we too successful at content collection? Sometimes, especially when parts of KM capture are automated, content repositories become so bloated that people give up using them - there’s too much to sort through, even with a good search engine and taxonomy, and a lot of what’s found turns out to be duplicative or outdated. Perhaps KM leaders focused too much on the tools and lost track of the core objectives – sharing critical knowledge and insights.
  • Are leaders engaging everyone in knowledge sharing? As organizations get thinned out, people get busier and despite their best intentions, won’t share what they’re learning unless someone specifically asks for it. Effective knowledge management needs an element of organized “pull” – leaders who visibly care about finding and sharing new ideas that will help the business. Is anyone doing the pulling? Are employees motivated to collaborate?
  • Did we forget how to use our existing KM resources? KM platforms should be designed to be user-friendly, with limited training requirements, but leaders often fail to communicate why KM is valuable in daily work and how to get the best out of it, especially after the initial launch and internal marketing push. A leading indicator for failure in this area: more frequent all-staff emails, titled “has anyone worked on topic X?”
KM initiatives often lose momentum over time. They can be re-energized through a combination of business focus, content management, and leadership behavior.

Whether it’s one or two or all of the above that led to the KM slowdown, the answer to these questions should suggest some viable pathways to renewal. Based on the same framework, the following key levers should be explored:

  • Re-focus on the strategic goals for KM, based on key organizational priorities. Go back to the overall business plan or vision and look at how an effective knowledge-sharing program could support it in terms of concrete deliverables. What specific types of knowledge sharing will help people do their jobs better and create the biggest business impact? Should we focus on best practices in customer service, innovative ideas in product development, or something else? (See Knowledge Management Is More Critical Than Ever).
  • Spring-clean existing repositories. Where knowledge bloat is the problem, someone needs to aggressively filter the most useful material and archive the rest, based on the content strategy. Often this will be a team of topic experts, working with usage statistics, surveys, crowd-sourced reviews and other group input (see Pearls of Wisdom in a Sea of Documents). KM taxonomy should also be reviewed, as some topics will have become more important and others less so. The spring-cleaning in itself will re-energize the KM effort by surfacing the most valuable “nuggets” and highlighting their value, as well as by reinforcing the overall business objectives.
  • Create and support “knowledge pull”. Leaders and managers, especially, need to demonstrate a passion for identifying and capturing shareable ideas and data from day-to-day work. If people feel their knowledge is valued, they’ll be much more willing to share it. Collaboration tools and enterprise social networks can re-invigorate this kind of knowledge flow, especially when they are effectively seeded with important questions and topics. As with the clean-out phase, topic experts can play key roles in codifying and curating what comes in, to improve relevance and findability.
  • Communicate and demonstrate effective knowledge sharing behavior. Again, leaders play a key role in encouraging their teams to actively look for ideas, best practices, and other relevant experience before reinventing the wheel (see Teaming Beyond the Team). KM leaders and trainers also need to emphasize how quick and easy it will be to find relevant content and people to talk with, once content libraries has been slimmed down and the new knowledge pipeline has been re-invigorated.

There are many other ways to re-energize stalled KM programs but these are good places to start. They address business value, ease of use, and content renewal, which are key factors for rekindling enthusiasm and energy around knowledge sharing programs.

New Approaches to Content Management

In some ways, we are living in a golden age for knowledge management, with the explosion of new sources of data and information creating enormous opportunities for organizational learning. Internal document repositories and external news sources continue to proliferate, augmented by social media, image-based content, and other sources of “big data”.

At the same time, however, we are outstripping our capability to extract useful knowledge from this flood of new content using traditional KM approaches, and easier document capture makes the problem worse. With boomer retirements, job churn among millennials, and greater time pressure on everyone, there are significant constraints on human-driven content collection and curation models.

Given these pressures, it’s more important than ever to have a tightly focused KM content strategy; a clear idea of which topic areas and which types of content will help people do their jobs better and will create the biggest business impact. Once these goals are in place, attention can shift to the most effective ways to gather and manage this content on an ongoing basis.

Technological and demographic trends are making KM content management and curation more complex. New tools, combined with a business-driven content strategy, can make things easier.

This is where new tools can be very helpful. Let’s look at four areas of current innovation with the potential to “turbo-charge” traditional KM content management.

  • Enhancing situational awareness. As the number and velocity of data and information sources increases, we need more help digesting and evaluating them, combining the results with insights from internal content, and understanding the implications. New tools are beginning to address these challenges using high precision text and semantic analysis, combined with business intelligence for effective presentation and internal communication. Features include exploratory search of incoming content streams as well as event-driven information delivery, based on business rules about relevant opportunities and threats, e.g. new or existing competitors, emerging customer requirements, or supplier activity. These innovations hold great promise for improving organizations’ environmental scanning, agility, and responsiveness.
  • Building automated content intelligence. To support improved information analysis and delivery, we need faster and more accurate identification of the facts, topics, and themes embedded in unstructured text. Content intelligence solutions use automated categorization to make sense of these enormous volumes of information and allow users to search and navigate through it. When this knowledge is harnessed into a comprehensive taxonomy, including cross-topic relationships, it becomes exponentially more useful; yielding insights that can be put together with structured data to solve business problems and generate appropriate workflows. For example, knowledge about car repairs extracted from technician’s logs can be combined with location and environmental data to pinpoint emerging reliability issues and solutions. Or sensitive content in document libraries can be automatically detected and routed into special repositories for audit and regulatory purposes.
  • Capture and codification of tacit knowledge. The retirement bulge, combined with increased turnover of junior staff, creates a critical need to capture the latent expertise of long-tenured employees, so that organizational learning does not evaporate with each passing year. Many organizations use exit interviews to gather this know-how, but the most specific, operational content requires a stronger organizing framework: which business processes does it address, under what circumstances, and with what implications? New decision-analysis tools can help significantly with this by capturing tacit knowledge about any business process in terms of “if-then” scenarios and implications, using rules and logic provided by subject matter experts. This approach has been successfully used in public-sector benefits evaluation settings, where complex eligibility rules need to be combined with the know-how of experienced caseworkers to make the right decisions and provide the best customer service.
  • Leveraging digital assets. The explosion of rich media digital assets (e.g., video, audio, images, design files) creates numerous issues and challenges for organizations, including storage of large files, rights management and workflows, and integration with traditional document-based KM platforms. New digital asset management (DAM) systems can address these needs and enable organizations to store, catalogue, search, and deliver digital assets alongside text-based content. The combination of these different types of knowledge asset can significantly enrich traditional document based KM systems and lessons learned repositories, using video, audio and images to reinforce the written word and provide a much more engaging experience for users. The latest DAM systems provide highly scalable storage, permissioning, metadata and publishing tools to support these initiatives, delivering huge benefits in terms of cost and time savings, risk reduction, and optimal usage of these critical assets.

The innovations and benefits described above are ultimately aimed at stimulating the use of knowledge resources throughout the organization, in the service of greater business agility, efficiency, and growth. For end-users of knowledge, these tools offer the potential for expanded and enriched access to content and insights, as well as easier navigation among related ideas. For knowledge managers, they provide new and more scalable ways to codify, organize, and analyze content in the service of enterprise goals.

Putting Knowledge Back Into Your Core Business Processes

I remember the excitement generated in the mid 1990s by the publication of several books on business process reengineering (BPR). In the classic, Reengineering the Corporation, authors Michael Hammer and James Champy made the case that value creation was only accomplished through business processes, and that optimizing the business processes, by removing idle time within and between steps, eliminating unnecessary steps, performing work steps in parallel, etc., should be at the forefront of any performance improvement or systems implementation initiative. Since then, almost every organization has invested quite a lot of work in improving their core business processes. We now have highly refined process workflows that stretch around the globe and involve combinations of onshore and offshore locations and combinations of employees and subcontractors.

What has surprised us during our consulting work is the extent to which knowledge has been extracted out of important decision-making steps. In the goal of achieving the greatest level of process efficiency, many of the process steps occur with little or no human thinking. Maximizing efficiency is generally appropriate for high-volume processes with identical and repetitive steps—those typically associated with a single output. But maximizing efficiency is not optimal for processes that involve decision making, issue resolution, and multiple outcomes.

Deconstructing a Business Process

Core business processes can be decomposed into subprocesses, tasks, and steps that show increasing specificity about how the process is carried out. Embedded in every process step are three types of knowledge.

  1. Knowledge about how to perform the activities in the step. Activities are the fundamental building blocks of a process and are grouped into discrete steps. Knowledge about how to perform the individual activities are captured in standard operating procedures and policy manuals and are explained during training programs.
  2. Knowledge about what decisions need to be made and how to make them. During training, employees are taught the decisions they must make and are given the policies, guidelines, and tools to help them make these decisions.
  3. Knowledge about the context for making decisions, including the values of relevant input parameters. For example, in a customer service environment, it is critical to know if you are interacting with a long-time, loyal customer (e.g., platinum/elite member) or an infrequent customer.
Greater Knowledge Yields Better Decision Making

When employees have access to better information, they make better decisions. While this may seem self-evident, the unique insight is that when business processes are deconstructed and the knowledge about the activities, decisions, and context is made explicit, then business processes can be redesigned to fully access and leverage the organization’s enterprise knowledge to improve the organization’s aggregate decision making.

Doing this right involves the systematic analysis of the core business process and the involvement of IT and HR. IT is necessary because a variety of knowledge management technologies can be used to put knowledge back into your core business processes. Among the most important include data and application integration across content repositories and systems, search, workflow automation, rule-based decision models and artificial intelligence, and real-time collaboration with experts. HR is necessary because training and a variety of organizational change management programs are necessary to change the internal culture by empowering employees with the initiative and creativity for issue resolution and problem solving.

An Example from a Customer Service Engagement

During a recent client assignment at a major customer service center, we analyzed the core business processes for responding to all types of customer inquiries. Our client had a set of antiquated and siloed IT systems and their customer service representatives (CSRs) often provided incomplete, inaccurate, and outdated information in their responses.

A major business process redesign effort was undertaken with two primary goals: (1) to empower customers to perform tasks using a new self-service portal; and (2) to empower the CSRs by providing them with the authority and the software tools to resolve customer issues directly. As part of the engagement, the processes for customer interaction were fully reengineered, leading-edge technologies were deployed (including online Q&A databases, collaborative user forums, and a customer-initiated trouble ticket system), and training and other organizational change management programs were developed and rolled out to the CSRs. To support the CSR decision-making, new guidelines were prepared to help them when interacting with customers. With up-to-date information about the case history, current product information (including information about upgrades, replacement parts, warranty information, software patches, etc.), and approved guidelines for problem resolution, the company greatly improved its customer satisfaction, loyalty, and retention metrics.


A great number of business processes, such as planning, R&D, vendor selection, sales, and most customer-facing processes, involve knowledge intensive steps. Our premise is that BPR, the use of appropriate knowledge management technologies, and organizational change management can be used to enhance decision making and put knowledge back into the core business processes. Real-time access to information enables process flexibility, effectiveness, and issue resolution.


Learn more. Reach out to Bernie Palowitch.

Career Legacy Planning: Knowledge Stewardship

There comes a time in your professional career when you begin to appreciate its vulnerability. You become more focused on the desire to leave a meaningful legacy for your employees and colleagues. Maybe you founded the company, maybe you run a large division or business unit, or maybe you head up a department. While your title is important, what matters most is how you will be remembered.

What man really fears is not so much extinction, but extinction with insignificance. Man wants to know that his life has somehow counted, if not for himself, then at least for a larger scheme of things, that it has left a trace, a trace that has meaning, its effects must remain alive in eternity in some way.

Ernest Becker, Pulitzer Prize-winning Author

We suggest that knowledge stewardship become a foundational element in your professional legacy planning.

The Business Case for Knowledge Stewardship

Most organizations recognize that enterprise knowledge is a precious asset. Company stock valuations are increasingly reflecting investor’s assessments of intangible assets. Unfortunately, most companies receive a failing grade for their knowledge stewardship activities. Preserving and protecting knowledge assets has not received much attention from senior executives. It’s ironic that large organizations spend hundreds of millions of dollars on security to protect their physical plant, property, and equipment, and smaller amounts on protecting software, databases, and patent and trademark portfolios. But in most organizations, safeguarding the vast majority of intellectual and knowledge assets—those that reside on laptops, in notebooks, and in the heads of every employee—has not yet become a top priority.

We define knowledge stewardship as the responsibility of executive management, acting as the agent for the company’s shareholders, to manage and safeguard the knowledge assets that are under their control. Knowledge stewardship is about preserving corporate knowledge and productivity, and it involves both a strategy for identifying, inventorying, and quantifying the value of the organization’s intellectual assets and the processes and practices necessary for protecting and enhancing their value over time.

The Lost Knowledge Crisis

Most enterprises today are facing an impending crisis in managing their knowledge assets. This crisis is a result of two significant workforce trends.

The first is an aging workforce. We characterize the aging workforce issue as a “crisis” for two reasons. First, there is increased recognition that the value of an enterprise is fundamentally related to the aggregate knowledge of its employees. Second, in some departments, business units, and companies, the rate of retirements and departures will exceed 50 percent or more within the next 10 years. The impact of this lost knowledge—the skills, learnings, relationships, and capabilities that are lost when experienced workers leave the enterprise—is especially critical for future success.

The second is a young and transient generation. Rates of employee turnover are increasing. For example, current data show that more than 60 percent of millennials leave their company in less than three years (Source: Millennial Branding, a Gen Y research and consulting firm, and Beyond.com). Other recent Millennial Branding reports show that 45 percent of companies experience high turnover with those employees identified as “millennials” – by a 2:1 margin versus older generations.

How executive leadership teams respond to both of these challenges today will have a significant impact on the organization’s long-term health and viability.

Making Knowledge Stewardship Part of Your Career Legacy Planning

Now is the time to move your professional life from one of success to one of significance. Your career legacy is the collage of your actions, achievements, and impact. Positive or negative, big or small, what you do and have done defines your legacy. Career legacy planning is a process whereby you determine what you want your legacy to be and then evaluate how best to use your time, talent and money to make a lasting impression on those you touch and the organizations and causes that you believe in.

Because organizations need to do a much better job of knowledge stewardship, consider making knowledge stewardship part of your career legacy plan. Numerous organizational challenges are involved in dealing with an aging workforce and with the issues involved in ensuring stability and continuity when people leave an organization. There is a clear window of opportunity to address these two workforce trends now, and this window will not last indefinitely. Seizing this moment requires elevating knowledge stewardship into the senior management’s strategic agenda.

Begin by establishing a knowledge management strategy and creating a detailed implementation plan. If you have an existing knowledge management program but it is underperforming, then you may want to consider revitalizing the program under your direct leadership.

Rest assured that knowledge stewardship will create a lasting legacy. The return on investment in knowledge stewardship will be a dramatic increase in the productivity of every employee during their entire tenure, arising from having access to and leverage of the company’s accumulated expertise. We have found that moderate investments in preserving corporate knowledge tend to pay great dividends.


Learn more. Reach out to Bernie Palowitch.

The Secret Sauce for Better Enterprise Search

The dream of everyone who has ever submitted an online search is to get that single best result at the top of the search results page. Period. No scrolling down the results page looking for something relevant. No clicking on the individual result links hoping to find that tidbit of information buried somewhere on the referenced pages. No iterative, back-and-forth revisions of the query terms in the search box hoping that the right answer will magically appear.

If you think about the time and frustration you have using your current search tools, then multiply that by the number of colleagues that you work with in your organization, the amount of energy spent looking for information is truly enormous!

Content enrichment is at the core of an organization’s quest for the perfect search experience. Many consider it the “secret sauce” for better enterprise search.

Enterprise Search

Enterprise search is the practice of making an organization’s content searchable. Enterprise search software indexes data and documents from multiple sources, such as file servers, document management systems, e-mail servers, databases, and the company’s intranet. The content typically includes both structured and unstructured data.

Enterprise search implementations are generally considered to be one of the most important elements of an organization’s knowledge management infrastructure because enterprise search uncovers and surfaces useful content that is generally unknown to other groups and departments.

The goal of enterprise search is to have the right content appear at the top of search engine results. Getting the desired result positioned at the top of the first results page depends on the quality and amount of enriched metadata added to the indexed content.

Content Enrichment

Content enrichment is the process of tagging every content asset with the correct metadata. Content enrichment is performed to improve the content’s findability.

The two key terms in the definition above are metadata and findability.

  • Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. A library catalog holds metadata for its books and other library materials. A product catalog holds metadata about the products a company sells or distributes.

There are several different types of metadata. Descriptive metadata is information that describes the intellectual content of the object. For a book, its title, author, publisher, and subject are examples of descriptive metadata. Administrative metadata is metadata used for managing and administering objects. For the book, the library branch that holds the book and whether it is checked-out are examples of administrative metadata. Structural metadata is data about the containers of data.

  • Findability is the careful engineering of content and its associated metadata to ensure that an organization’s content is findable and indexable by search engine software.
Findability Framework

The Findability Framework provides a roadmap for improving enterprise search. Re-inventing how your organization searches for information involves addressing the following six core elements of the Framework.

  1. Core business processes are defined. Understanding how search is used to support the organization’s value-adding business processes provides the context for search improvement.
  2. User requirements are collected and documented. The work tasks for each user role provide insight into the types of information they will need to perform their tasks.
  3. Work processes and tasks are mapped to the information sources and access to the content repositories is established. If the sources are currently not accessible, then subscriptions to external content may need to be purchased and data and application integrations may need to be performed. For internal sources, connectors need to be installed or built so the search software can access the content.
  4. Content is enriched. This is done by developing an appropriate metadata schema and adding metadata to each piece of content. The search engine then indexes the content with the metadata. Specifically, the search engine crawls the content so that it and its metadata can be indexed and stored in its database.
  5. The search engine software is configured to improve the precision of the search results and to enhance the end user search experience. Search engine tuning can be performed by adding additional weight to the metadata values as a way to improve the results ranking algorithm. Some of the user experience enhancements could include faceted navigation, browse, extensive linking across data sets, content recommendations, and powerful analytics.
  6. Governance polices are developed and instituted to maintain the ongoing quality of the search results.


Learn more. Reach out to Bernie Palowitch.

Forming a Tangible Relationship with Virtual Assets

In this age of digital assets, Airbnb, an accommodation company doesn’t own a single hotel; Uber, a transportation phenomenon, doesn’t own a single taxi; YouTube doesn’t own a single production studio.

What these companies do have are Digital Assets: the collection of software, images, videos, algorithms, and documents on their websites that has enabled them to grow phenomenally, providing a service that people readily pay for.

Increasingly, users are looking for an “immersive” experience before they buy a product or service. For example, real estate agents offer 360° views of the property online to prospective buyers, Google and Bing maps provide street, bird’s-eye, and satellite views of a property. High end clothing chains provide an online “Fitting Room” where customers can try their merchandize.

All of the above is made possible with digital assets: images, videos, snazzy animations, attractive brochures and such. And increasingly, the business value is driven by an intelligent, intuitive organization and presentation of a company’s products and services using digital assets: in the cacophony of digital free for all, if your digital marketing isn’t heard or seen, your business may not grow!

However, creating high quality, engaging digital assets is harder than it may seem. It requires a seemingly magical mix of artistic, technical, and marketing skills to develop and nurture the right visual identity for a business, and it will cost time and money.

And creating digital assets is just part of the story. As digital content around us increases exponentially, managing digital assets (think digital rights, security of digital assets, technical requirements and many more issues) is becoming an arduous, complex task. If not thought through, and not well integrated with rest of functions, chances are digital asset management will eventually create costly embarrassments for a business. And it’s not a one-time effort either: it has to be sustained over the life of the business, in synch with ever evolving market tastes and preferences as well as incessant social chattering of ideas and opinions.

So, what is a business to do in these social “rich” media times? Create, embrace, protect, and present digital assets related to its ideas, products, and services like no one else. For example, companies as diverse as CNN, Amazon, IKEA, and others have several visually integrated channels (website, advertisements, product demos, and social media) where they continually release fresh content based on a variety of feedback mechanisms. That keeps these organization’s products and services relevant and engaging in their customer’s minds.

To make things easier, several technology products and services are available that bring structure and security to digital asset management. However, organizations still have to go through a complex process of evaluating, selecting, and installing products and services.

A picture says a thousand words; a video can bring ideas, products, and services to life. Together with a catchy copy, a business should continually visualize how best their digital content can emotionally connect with its customers, and see the relationship bloom!


Learn more. Reach out to Pranay Kohli.

Knowledge Management Is More Critical Than Ever

Some notable observers have declared that “knowledge management is dead,” that it is an old, tired concept that creates little to no value in organizations. This drumbeat of negativity is misguided. On the contrary, knowledge management (KM) has evolved and matters more than ever to all types of companies and organizations.

Knowledge Matters

Roughly 70 percent of the valuation of public companies today comes from intangible assets, and one of the most important assets is an organization’s knowledge. It is the intellectual property created in R&D labs, insights on customer trends gleaned by marketing, new cost-reduction practices found on the shop floor, and the collective experience of the organization’s people. Knowledge workers are creating and discovering knowledge every day, and are conversely also seeking knowledge from others both inside and outside the organization.

Many organizations have mobilized to apply this knowledge to continuous learning and new value to deliver faster growth, more efficient and cost-effective operations, higher-quality products and services, and greater human resource development. These efforts have become more deliberate since knowledge management emerged as a management concept in the 1990s, with organizations of all sizes investing significant amounts in people, processes, and technologies.

Roughly 70 percent of the valuation of public companies today comes from intangible assets. Knowledge workers are creating and discovering new knowledge every day.

The Challenge

Unfortunately, some organizations have not realized their intended benefits from investments in Knowledge Management, with some abandoning their organized efforts altogether. Some business leaders have perceived these investments as not value-added, expressing frustration in many ways:

“We spent millions on a large content management system, but nobody can find anything.”

“Our expensive new collaboration tools go unused, as everyone seems fine using email.”

“Our people say it is a pain to collect and contribute knowledge to the database.”

“We have dozens of KM staff and I don’t know what they do or how they add value.”

This feedback is not isolated,, as these and other examples have attracted the attention of notable analysts and observers, with some writing openly about the decline, or even “death,” of KM. Some actual recent quotes:

“Knowledge management isn’t dead, but it’s gasping for breath…Any chance that this idea will come back? I don’t think so.”

“Knowledge Management is dead, thank God.”

Renewal of Knowledge Management

These fears are misleading. First, the need for businesses to drive results and competitive advantage through Knowledge has never been greater. This is being driven by ever-increasing business complexity, as well as the rapid explosion of new tools and data, while markets demand greater speed and performance than ever. In addition, in more emerging businesses (e.g. Uber, Amazon), “knowledge” is intrinsic to the service or product, putting knowledge at the front line of the business in ways it never was before. These require knowledge to be a central part of the end-to-end business model and cannot be an afterthought.

Second, many of the notable “failures” of Knowledge Management can be traced back to poor design and management of the KM program.  A common pitfall is organizations who don’t articulate what knowledge is for their business and people, or the practical business benefits they are trying to achieve.  The result is well-meaning efforts that are not aligned with the business’ strategy.

Finally, the concept of Knowledge Management has evolved. Historically, KM has typically been built as a central, monolithic department, with the goal of serving all business groups and all people thru a single set of tools and processes. Unfortunately, in most organizations, this is the wrong approach. The imperative is not to force-fit a single program everywhere, but instead align the right tools and behaviours to the right purpose, across the organization. For example, what works to enhance Customer Service is different that what is needed to improve Quality Assurance.

Businesses now have a wider and expanded set of tools and capabilities to choose from, including Enterprise Content Management, Digital Asset Management, Business Intelligence, Social Collaboration, Enterprise Search, Crowdsourcing, and many others. Each of these need to play a specific (and varied) role across any organization. Going forward, the successful “KM” program may not be a “KM” program at all, but rather good, smart business process enabled by technology.

As a result, we shouldn’t be talking about the “death” of KM, but its renewal!


Learn more. Reach out to Bob Armacost.

Extracting Knowledge from Big Data

Ninety percent of the data in the world today has been created only in the last two years, according to IBM. With the increase of mobile devices, social media networks, and the sharing of digital photos and videos, we are continuing to grow the world’s data at an astounding pace. Data is big…and getting bigger. 

Big Data is a big thing. It is not a passing fad.  It has become an all-encompassing, somewhat sprawling term that has defied conventional definition. Seemingly, it has as many definitions as it does applications.  In fact, it is most accurately described by its dimensions – the so-called “5Vs” - Volume, Velocity, Variety, Veracity and Value. Solutions for harnessing and leveraging Big Data have also been elusive. What is clear, however, is that technology alone is not the answer.

Data has always been used to develop high-level metrics and business intelligence. Smart organizations have long relied on data to help make strategic business decisions. But the power and allure of Big Data is how it enables organizations to leverage unconventional data points: the information that was previously ignored because there was no reasonable way to process it.

The key question is, “How do we extract big knowledge from big data?”

Unprecedented access to information (according to former Google CEO Eric Schmidt, every two days now we create as much information as we did from the dawn of civilization up until  2003) and emerging technology (allowing us to harness different types of data – both structured and unstructured) have resulted in the rise of big data analytics, which hold the promise of helping companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. This could include photos, sensor data, video or voice recordings, web server logs, Internet clickstream data, social media content and social network activity reports.

The technical challenge of using big data to drive innovation and business growth is only part of the solution.  A pervasive culture of change management needs to be in place to reap the full benefits of the effort.

Data Democracy. One of the most critical aspects of big data is how it can flatten hierarchical decision making.  Data is power and it is the great equalizer.  Most organizations have operated in an environment where there is a paucity of data.  This has resulted in a certain “fill in the blank mentality,” and decisions are made at the highest pay grade, on the basis of experience senior leadership has built up and patterns and relationships they’ve observed and internalized. This is a flawed approach. Data should be the final arbiter, not executive fiat. Senior leadership needs to encourage data-driven decision making by everyone in the organization.

The War for Talent. The new breed of analytics specialists need to have a combination of skills including statistical techniques, applied mathematical methods, advanced machine learning algorithms, data visualization, and business and communications skills. Many of the key techniques for using big data are rarely taught in traditional university courses. Perhaps even more important are skills in cleaning and organizing large data sets; the new kinds of data rarely come in structured formats. Not surprisingly, people with these skills are hard to find and in great demand. Human Resource departments need to develop new strategies and approaches to acquiring and retaining talent.

Ever Vigilant. According to Forrester, firms use only five percent of the data available to them, while created data is growing at 40 percent to 50 percent annually and only 25 percent to 30 percent of that total is being captured. This places a premium on constantly monitoring the technological landscape for tools that address the 5Vs of big data.  Improved predictive analytics, real-time computational capabilities and modelling techniques all dictate that technology upgrades be a significant part of any big data strategy. Movement towards open source software has kept cost of change comparatively low. Organizations need to develop an adaptive data strategy to address emerging technologies.

Big Data brings with it big promise.  There are many challenges, but the rewards are clear. As Peter Drucker noted, “You can’t manage what you don’t measure.”  The allure of big data is that it provides organizations with an unprecedented capability to measure.


Learn more. Reach out to Scott Leeb.