I’ve been in the business of Knowledge Management Consulting for the vast majority of my career and, in my experience, one of the most challenging aspects to KM is its intangibility. I’ve helped an array of organizations to define their KM Success Metrics and KM Key Performance Indicators (KPIs) in order to make KM measurable and tie it to business value and hard return on investment. In these cases, though, many of these KM KPIs are only measurable over years and often have a stronger demonstration of value to the organization rather than the individual. 

Since good KM is integrated into the business, enterprise KM programs are often largely invisible when they work, and only visible when they’re causing the end user “pain.” For example, a seamless tacit knowledge capture program feels like natural conversation, whereas a badly designed program will feel forced and overtly time-consuming. A natural content governance plan will be integrated into the enterprise and simply feel like how business is done, whereas a poorly designed governance plan will slow down work and create barriers to sharing and connecting.

As a result, KM runs the risk of not being “felt” by the average end user in a way that inspires engagement and support. Though a KM effort may be meeting long-term organizational goals, it nonetheless runs the risk of a decreased focus or dwindling support over time if the individual business stakeholder doesn’t feel the benefit of it.

One key area where the individual, as well as the business, can experience meaningful value from KM on a daily basis is through enterprise search. Though I’m not suggesting a technology is necessary for all aspects of KM, the reality is that for large organizations, a great deal of KM will be enabled through supporting technologies. A well-designed, implemented, and governed enterprise search is one of the key systems where KM becomes real for the average end user.

Several exciting things are happening within the enterprise search world at this point:

• Enterprise search tools are increasingly able to index both structured and unstructured information, creating greater linkages between different types of knowledge and information.
• It is becoming easier to design more creative user interfaces within search that better reflect the needs of the end user and the actions they want to take.
• Once advanced features, like type-aheads and faceting, are now readily available.

In order to really make enterprise search work, foundational KM activities are still critical. For instance:

• Content Audits and Cleanup – Content has to be cleaned up and enhanced with tags to ensure the right content appears in search and is weighted appropriately. Content cleanup alone is time-consuming and dry, but linking it to a search effort shines a critical light on why it is important. Without a content cleanup, search will end up being “garbage in, garbage out” no matter how slick it is.
• Taxonomy Design and Tagging – Taxonomies have to be designed and applied to key content repositories as well as integrated into the search design to ensure faceting works and different types of content from different sources can be seamlessly integrated. Taxonomy by itself can be esoteric and easily set aside, but when its value surfaces as faceted navigation, it becomes a critical tool for findability and discoverability.
• Content Types – Content Types continue to be one of the more misunderstood elements of a KM architecture, despite our efforts to make them more approachable. Content Types can serve as templates, guide workflows and security, and inform tagging. When designed correctly, they can also translate into search hit types. That said, they tend to be relatively confusing until seen in action.
• Tacit Knowledge Capture – Almost every organization with whom we’ve worked agrees Tacit Knowledge Capture is critical to ensuring expertise isn’t lost as employees leave and new employees are up-scaled faster and more effectively. Good Tacit Knowledge Capture can take a broad array of forms, from traditional mentor/mentee pairings, to email capture tools, to communities of practice (both live and virtual). Though there can be substantial visibility for a great deal of these mechanisms, their full value isn’t felt just in their existence. Tacit Knowledge Capture really only pays off when individuals can find and engage with the captured knowledge. Search can play a key role here, and can also allow for the integration of a range of result types in a manner that allows the end user to find the “official” published answer as well as related “social” answers from experts (as well as, potentially, the experts themselves).
• Knowledge Sharing Culture – Developing a strong culture of knowledge sharing is one of the foundational activities we seek to implement in the early stages of any KM engagement. Specific activities for this venture vary greatly amongst organizations and depend on from where they’re starting. Approaches may range from a simple commitment from leadership, to the establishment of a KM Leadership group, and to more advanced gamification and analytics efforts. At the end of the day, however, nothing shines a light on good knowledge sharing behavior like something that will surface that newly shared knowledge in a form that is easy to find and discover.
• Governance – Governance, specifically content governance, is another building block and truly foundational activity for enterprise knowledge management efforts. Like a culture of knowledge sharing, nothing helps to show the importance of governance as much as a search initiative that shows what happens in very real terms when people DON’T follow it. Content governance will get a huge boost in importance as soon as it’s easier to find and expose content.

Each of these pieces alone is an important part of a comprehensive KM strategy. Together, they make up many of the core KM foundations I seek to put on KM Roadmaps for my clients. Integrating a search pilot into that roadmap ensures the hard work that will go into the aforementioned efforts, as well as the overall KM transformation, will be seen, and made clickable, for your end users.

Information Architecture is an enabler for Big Data Analytics. You may be asking why I would say this, or how does IA enable Big Data Analytics? We need to remember that Big Data includes all data (i.e., Unstructured, Semi-structured, and Structured). The primary characteristics of Big Data (Volume, Velocity, and Variety) are a challenge to your existing architecture and how you will effectively, efficiently and economically process data to achieve operational efficiencies.

In order to derive the maximum benefit from Big Data, organizations must be able to handle the rapid rate of delivery and extraction of huge volumes of data, with varying data types. This can then be integrated with the organization’s enterprise data and analyzed. Information Architecture provides the methods and tools for organizing, labeling, building relationships (through associations), and describing (through metadata) your unstructured content adding this source to your overall pool of Big Data. In addition, information architecture enables Big Data to rapidly explore and analyze any combination of structured, semi-structured and unstructured sources. Big Data requires information architecture to exploit relationships and synergies between your data. This infrastructure enables organizations to make decisions utilizing the full spectrum of your big data sources.

Big Data Components

 IA Element                 Volume                                  Velocity            Variety

Content Consumption

Provides an understanding of the universe of relevant content through performing a content audit. This contributes directly to volume of available content.

This directly contributes to the speed at which content is accessed by providing initial volume of the available content.

Identifies the initial variety of content that will be a part of the organization's Big Data resources.

Content Generation

Fill gaps identified in the content audit by Gather the requirements for content creation/ generation, which contributes to directly to increasing the amount of content that is available in the organization's Big Data resources.

This directly contributes to the speed at which content is accessed due to the fact that volumes are increasing.

Contributes to the creation of a variety of content (documents, spreadsheets, images, video, voice) to fill identified gaps.

Content Organization

Content Organization will provide business rules to identify relationships between content, create metadata schema to assign content characteristic to all content. This contributes to increasing the volume of data available and in some ways leveraging existing data to assign metadata values.

This directly contributes to improving the speed at which content is accessed by applying metadata, which in turn will give context to the content.

The Variety of Big Data will often times drive the relationships and organization between the various types of content.

Content Access

Content Access is about search and establishing the standard types of search (i.e., keyword, guided, and faceted). This will contribute to the volume of data, through establishing the parameters often times additional metadata fields and values to enhance search.

Contributes to the ability to access content and the speed and efficiency in which content is accessed.

Contributes to how the variety of content is access. The Variety of Big Data will often times drive the search parameters used to access the various type of content.

Content Governance

The focus here is on establishing accountability for the accuracy, consistency and timeliness of content, content relationships, metadata and taxonomy within areas of the enterprise and the applications that are being used. Content Governance will often "prune" the volume of content available in the organization's Big Data resources by only allowing access to pertinent/relevant content, while either deleting or archiving other content.

When the volume of content available in the organization's Big Data resources is trimmed through Content Governance it will improve velocity by making available a smaller more pertinent universe of content.

When the volume of content available in the organization's Big Data resources is trimmed through Content Governance the variety of  content available may be affected as well.

Content Quality of Service

Content Quality of Service focuses on security, availability, scalability, usefulness of the content and improves the overall quality of the volume of content in the organization's Big Data resources by:
- defending content from unauthorized access, use, disclosure, disruption, modification, perusal, inspection, recording or destruction
- eliminating or minimizing disruptions from planned system downtime
making sure that the content that is accessed is from and/or based on the authoritative or trusted source, reviewed on a regular basis (based on the specific governance policies), modified when needed and archived when it becomes obsolete
- enabling the content to behave the same no matter what application/tool implements it and flexible enough to be used from an enterprise level as well as a local level without changing its meaning, intent of use and/or function
- by tailoring the content to the specific audience and to ensure that the content serves a distinct purpose, helpful to its audience and is practical.

Content Quality of Service will eliminate or minimize delays and latency from your content and business processes by speeding to analyze and make decisions directing effecting the content's velocity.

Content Quality of Service will improve the overall quality of the variety of content in the organization's Big Data resources through aspects of security, availability, scalability, and usefulness of content.

The table above aligns key information architecture elements to the primary components of Big Data. This alignment will facilitate a consistent structure in order to effectively apply analytics to your pool of Big Data. The Information Architecture Elements include; Content Consumption, Content Generation, Content Organization, Content Access, Content Governance and Content Quality of Service. It is this framework that will align all of your data to enable business value to be gained from your Big Data resources.

Note: This table originally appeared in the book Knowledge Management in Practice (ISBN: 978-1-4665-6252-3) by Anthony J. Rhem.

Similar to the old adage, “you can lead a horse to water, but you can’t make him drink,” you can deliver a solution that uses the most cutting-edge technology and beautiful design, but you can’t guarantee that your stakeholders will embrace it. This blog offers practical tips on how to maximize and measure user adoption to ensure that your new tool or process is fully embraced by those for whom you’ve designed it.

To deliver a project success story backed with quantitative and qualitative data to support it, you should take an objective-first approach to change management. This requires a shift in focus from what the change is (e.g. the implementation of a new tool or process) to what you aim to achieve as a result of the change (e.g. increased productivity or improved work satisfaction). Rather than only highlighting the features of the new technology, you’ll want to focus on the benefits the users will gain from using it. Taking this approach is particularly critical for Knowledge Management initiatives, which are initially often met with skepticism and a broad sense of concern that there’s not enough time in the already busy day to acclimate to another new tool or process. By following these guidelines, you’ll be able to say “our users love the new tool and they are so much more effective and efficient as a result of it…” and “here’s the data to prove it.”

The way to accomplish this is by setting “SMART” objectives at the start of your project and developing an anaytics strategy that will help you measure your progress towards achieving those objectives. These objectives should clearly express desired changes in user behavior and the impact these new behaviors are expected to have on overall productivity and effectiveness. In the words of Stephen Covey, “start with the end in mind” so that all your efforts are aligned towards achieving your expected results.

Let me put this into context using one of my current projects. I’m working with a global manufacturing organization to design and implement a tool that will help the communications department work in a more centralized and collaborative way. The team is responsible for delivering content about events, programs, and news items to internal employees as well as external stakeholders. The team is used to working in silos and each team member uses different tools for storing, sharing, and finding information such as a basic team site, email, and desktop file folders.

From the very beginning of the project, change management has been a priority. We knew that if we wanted the communications department to adopt the new tool, we had to think of ways to encourage them to do so well in advance of them even having contact with it. Here are ways to apply what my team has done to your change effort to help you maximize and measure user adoption:

Step 1: Align your metrics with desired outcomes

To encourage a more centralized and collaborative way of working for the communications department, we’re using Microsoft O365 tools such as MS Teams, MS Planner, and modern SharePoint team sitesas a platform for the new system. We chose this suite of tools because it offers various features that, if used, could save the department a lot of time, reduce wasted effort, and ultimately elevate their role to a more strategic partner within the organization.

Here’s how we’ve expressed our primary objective:

“Increase the team’s efficiency by managing all campaign content, including digital assets, in the new tool within 90 days of launch.”

When content is stored in various places, not everyone has access to the latest versions. This causes a lot of confusion and re-work. The challenge is that people defer to the processes they’re most used to, which is often saving information in their local drives and sharing it via email. The new behavior we wanted to encourage was saving information in a centralized location (in this case a SharePoint team site), so that everyone has access to the latest version, edits are being made to the same copy, and there’s a tracking history of the edits, as well as who made them.

The objectives you identify will vary depending on the challenges you’re trying to solve, so your success metrics should be aligned accordingly. In this case, defining our objective leads us to what we should measure: the percentage of campaign content that is stored and shared in the tool vs. outside of it.

Step 2: Capture baseline metrics and keep it simple

In order to be able to tell a story about the impact of a new tool, you need baseline metrics for comparing your results. For this project, we had three categories of metrics and different approaches for capturing each:

• Satisfaction Level: We deployed a survey that measured how useful users found their current system.
• Proficiency Level: We deployed another survey that measured their self-rated proficiency levels with basic SharePoint functionality such as uploading and sharing documents.
• Usage Level: We tracked activity on the system after launch. This includes number of active users, number of documents and multimedia files saved and shared via the tool, and number of interactions in the conversations space.

The key here is to keep it simple. We designed the surveys to be short and to the point, and only asked specific questions that would help inform the decisions we made on the project. We also didn’t measure everything. We kept it basic to start and the longer the users had to engage with the system, the more sophisticated our metrics became.

Step 3: Take actions that lead to measurable improvements

Our satisfaction survey, along with in-depth user analysis and testing, informed the features we included in our new tool. As we were prioritizing the features, we kept our objectives in mind. It was critical for us to ensure our tool had a separate space for managing content for each campaign. This space had to make it easy for the team to upload, edit, share, and find content, including text-based and multimedia assets.

Our proficiency survey helped us to design the training for the new tool. Had we made the assumption that our users were already familiar with SharePoint’s basic functionality, we would have gone into our training sessions ready to introduce all of its advanced features. Knowing that the team members were not as confident in their SharePoint abilities led us to design a basic SharePoint prerequisite training session for those that needed it. Meeting users at their proficiency level and guiding them towards the level they need to be to make the most of the new tool’s features prevents them from being so discouraged that they abandon the new tool prematurely. (Get more helpful tips on user training by watching Rebecca’s video, Top 5 Tips for Using Training to Promote Adoption).

This is important because we planned to deploy the satisfaction and proficiency survey again once we launched the new tool. Taking actions based on the results of the baseline survey created measurable improvements in how much the users liked the new tool(s) they were using and how confident they were in using it.

Step 4: Measure again once you’ve implemented your solution

This may seem like common sense, but let your users know that the tool is now available for them to use and train them how to use it! Often, the team members heavily involved in the project assume that users know it exists and will intuitively learn how to use it on their own. The team building the tool has spent the past few months or so immersed in the tool, so they are likely to overestimate other people’s awareness of the tool and underestimate the learning curve associated with it.

In our case, our baseline usage level was 0 team members because the tool was brand new. Our goal was to increase usage level to all 30 team members. Our strategy for getting all 30 team members to use the tool, rather than relapsing back to their old habits and systems, was the deployment of “early and often” messages about the tool, along with thorough training for each team member we expected to use it. Long before the tool was launched, we built excitement and awareness around the new tools via a teaser video, Yammer posts, emails, and messages from leadership during team meetings. Once the tool was launched, we conducted live training sessions and delivered helpful resources and guides.

Along the way, we were asking:

• What percentage of the team watched the teaser video?
• How many team members saw the Yammer posts? How many “liked” it, replied to it, or shared it?
• How many of the team members heard and saw the presentation?
• Did the team members react positively or negatively to the messages in the video, posts, and presentations?
• How many of the team members completed the optional pre-work and basic training?
• How many of the team members attended the live training sessions?

All of these metrics were indicators of the degree to which the users would adopt the new tool. You can then validate these indicators by measuring actual adoption, e.g. user activity within the tool and their satisfaction in using it.

Step 5: Give it some time, then measure again

As we were building the tool, the project team discussed how we were going to tell our success story. But, that really depended on how we defined our success. For us, did success mean that we launched the new tool on schedule and under budget? Or, did it mean that the communications team members were embracing the new tool and way of working? The latter for us was much more important so we developed a timeline for capturing feedback: one week after launch, one month after launch, 3 months after launch, and 6 months after launch. During these set time periods, we would capture metrics around how satisfied they are with the new tool and its impact on their work and how proficient they felt with their new skill sets. In addition to self-reported data, we would also track usage metrics such as what percentage of the team actively manages their campaign within the tool vs. outside of it.

Summary

Organizations invest large amounts of money on new technology with the intentions of improving employee productivity. The key to getting a significant return on these investments is to make sure your project team has what it takes to define, drive, and measure success. If you want to make sure the next solution you roll-out maximises user adoption and produces measurable results, contact Enterprise Knowledge at info@enterprise-knowledge.com.

In our last blog post, our featured author discussed how "knowledge change is not a technology project."  This week our author presents the case for the use of tech tools especially in dealing with issues of employee retention.  Endorsement of specific vendors by KMI should not be implied.

Implemented in organizations with high employee turnover, knowledge management tools are not only to facilitate knowledge accumulation and transfer but also to stimulate employees’ engagement and retention. Here how it works. 

According to the 2016 BenchmarkPro survey, the average employee turnover rate in the USA equals 18.1%. While the highest rates are characteristic of the states with a lower median household income such as Montana (23.0%), Oklahoma (22.1%), Idaho (21.5%) and New Mexico (21.5%), turnover rates in the richest US states are also above the ‘healthy’ 10-15% - Maryland (19.1%), Massachusetts (17.1%), California (16.8%), New Jersey (16.4%). High employee turnover is a true headache in such industries as retail, healthcare, banking and financial services, as well as the IT sector where employees rarely stay with a company longer than a year.

How high employee turnover impacts organizational knowledge

In terms of knowledge management, high turnover rates mean that companies can face multiple knowledge-related challenges, including:

• lost knowledge if employees leave and don’t transfer their valuable knowledge in any form
• knowledge leaks if employees leave and reuse acquired knowledge at their new workplace
• knowledge transfer and constant learning required any time to introduce newcomers to their work
• knowledge gaps that (re)appear in different knowledge domains, and more

If left unaddressed, these challenges can lead to discontinued business processes and cause serious mistakes that will end up with lost money and damaged reputation.

Why turn to a knowledge management system?

With so many knowledge-related risks, organizations with high employee turnover can do nothing but turn to knowledge management to achieve the following goals:

• Make the turnover less painful and hire new employees with the needed knowledge level quicker
• Facilitate regular employees’ work in an unstable environment and reduce their efforts on transferring knowledge to newcomers
• Support productive teamwork
• Minimize newcomers’ mistakes and eliminate knowledge gaps
• Create incentives to retain employees

Now, let’s analyze how companies can achieve these goals through relevant knowledge management tools and techniques.

Good news for those who already use SharePoint: turning to SharePoint consultants or developers, they can adapt the platform to knowledge management needs.

Outlining workforce gaps with a knowledge map

Usually, organizations create a knowledge map to structure corporate knowledge and understand if its current level is enough to cover particular business needs. Besides that, companies can use their knowledge map to smartly and timely find new employees. By identifying key knowledge areas essential to business processes, organizations can mark out risky areas and relevant knowledge owners. Guided by the map, HR managers can form and support a base of candidates suiting critical knowledge areas and find employees with specific knowledge faster.

Automating knowledge transfer

Constant staff changes disturb regular employees. That’s a real nightmare for experts who should transfer their knowledge to newcomers over and over again. With this in mind, it’s reasonable to automate knowledge transfer to key knowledge owners’ relief. For example, an organization can create a dedicated knowledge center for newcomers to access the working materials prepared by experts and study them. To check up their knowledge, newcomers can take relevant tests right in the KM system. Test results will be then presented to a line manager and an expert. If results are unsatisfactory, a knowledge owner can schedule individual training. This way, an initial knowledge transfer can be fully automated or coupled with only a few one-to-one consultations, which reduces experts’ involvement substantially.

Storing team knowledge on collaboration sites

The value of collaboration sites at companies with high employee turnover increases dramatically. When team collaboration takes place in a single collaboration hub (these can be dedicated SharePoint team sites), a new team member will be able to learn the collaboration history, view project-related documents, get consultations of teammates and dive into the working process much easier.

Eliminating mistakes due to knowledge gaps

To minimize mistakes made regularly by newcomers, companies can use at least two knowledge management tools. First of all, a centralized knowledge base with well-structured instructions and recommendations can guide newcomers throughout their working process. Secondly, newcomers can use a knowledge map to quickly identify knowledge owners and connect them in order to ask for a piece of advice and solve problematic or complex tasks successfully.

Reducing employee turnover with a knowledge management system

Unfavorable corporate culture, boring or unimportant work and the lack of recognition are constantly on the list of popular reasons to quit a job. To change this, companies can use their knowledge management system to create comfortable working conditions that will stimulate employee retention.

Recognizing employees’ contribution and ensuring personal growth

A knowledge management system can include a system of points attributed to employees who regularly contribute to the development of organizational knowledge (develop a particular knowledge domain, organize a community of practice, make research work, etc.). ‘Knowledge’ points can be included in a personal development plan so that employees could see their professional advance, as well as into employees’ general rating for line managers to reward top contributors with relevant incentives.

Turning off stress and enabling a supportive working environment

Employees are always afraid to make a mistake especially if they don’t have the needed knowledge and can’t find it anywhere. Provided with strong search capabilities, a knowledge management solution will allow employees to find relevant pieces of knowledge or knowledge owners who can assist a newcomer in solving a particular task. With the possibility to contact experts and teammates, employees will feel more comfortable at work, which increases employees’ satisfaction and confidence.

Creating extra opportunities

Companies can also think about giving extra opportunities to their staff. For example, employees can share their ideas and store them in a bank of ideas located in the knowledge management system. Best insights will be then discussed with line managers and experts and turned into real projects. This is how employees will get a chance to implement their own project and advance in their career.

Even one KM activity can bring several outcomes

Obviously, companies struggling with high employee turnover are focused on replacing employees and keeping business processes uninterrupted. Due to impressive investments into HR management, they can cut other corporate investments, especially the ones into such activities as knowledge management. However, it’s not always right.

High employee turnover is an exceptional situation when even scattered KM activities can be of a great value. Companies can adopt at least one of the described solutions to get multiple outcomes at once. For example, having a knowledge map on their hands, companies can simplify the hiring process and invigorate the connection between newcomers and experts, while collaboration sites will support knowledge transfer, uninterrupted teamwork and the introduction of newcomers to the working process.

There are three key questions to ask when developing a Knowledge Management (KM) strategy: where are you, where do you want to be, and how do you ensure you get there successfully? These are the three pillars crucial for the development of a sound KM strategy. At Enterprise Knowledge (EK), we define these as the Current State, Target State, and Roadmap. As simple as these terms may sound, developing a complete understanding of each is no small challenge. In this white paper series, one of EK’s KM strategy experts, Yanko Ivanov, addresses each step, starting with the Current State Assessment.

Step #1:  Understand the Current State

The challenge with creating a KM strategy that works is that one size does not fit all. In reality, your KM strategy must be intimately tailored to your specific environment, technology ecosystem, and business goals. In order to develop a practical, realistic, and successful KM strategy, you need an in-depth grasp of the current situation.

We are often approached by clients who, in their attempt to develop their KM strategy in-house, failed to fully grasp the fundamentals of their current challenges. Having a sound KM strategy is not only about having a SharePoint or Drupal installation with a nice interface or a set of KM policies documented. There are many crucial factors that influence how KM becomes a true and working part of an organization, many of which have little to do with technology.

To cover these various factors, at EK we approach the Current State Assessment from five perspectives: People, Processes, Content, Culture, and Technology. We purposefully list Technology last, as it reinforces a key point for our work, that Technology is an enabling tool for KM, not the complete solution itself.

1. People

Organizations often make the mistake of focusing solely on technology and underestimating the people aspect. However, no matter how cutting edge and cool technology is, if it doesn’t cater to the actual needs and preferences of your users, adoption will suffer drastically.

With that in mind, here are some critical questions to ask when developing your KM Strategy:

• Who are your users: demographics, business units and structure, roles, etc?
• What information do they need on a daily basis?
• How do they connect and communicate currently? How would they like to do that in the future?
• Are there established thought leaders? If not, what is preventing that?
• Do they like sharing expertise? If not, what is stopping them?
• Are there any informational and/or functional silos within the organization and what is the root cause for them to form?

It is important to understand the composition of your staff, their communication patterns, and their preferences, as well as the information they need. For example, in a past project with a global Fortune 500 company, we found the same search interface was going to be used differently by executives compared to directors and even more junior associates. Executives were more focused on most relevant results of a specific topic and type while associates were interested in relevant results across topics, business units, and document types. In other words, depth versus breadth.

Understanding who your audience is, their communication channels, and any existing or potential barriers to information flow will guide the next step of your KM strategy development, the Target State Definition.

2. Processes

Organizational processes are driven by information. Every step in a process consumes some kind of data, and produces some form of output, be it a document, the number of produced items, or a simple email. As such, analyzing organizational processes is integral for developing a working KM strategy.

When analyzing current processes in an organization, we address questions like the following:

• What are the main business processes for the organization, as well as for each business unit?
• How are the processes instantiated, applied, and followed? What are the gaps, and where can they be improved?
• Does your staff perceive the existing processes as efficient or more cumbersome than necessary?
• How are the processes being followed in “real life?”
• Are there established roles and well defined staff to fill these roles in each process step?

It is important to note that understanding an organization’s processes goes much deeper than what they have documented as “official” processes. Many organizations have created processes that work at various levels, but have yet to be expanded enterprise wide or established as “official” to the organization. A key component of our process discovery work is learning what is being done that is working and could potentially be expanded upon.

Understanding your processes and their data needs feeds important information to the next step of the KM strategy development: developing a Target State where a mutually beneficial relationship between your KM and business processes leads to improved efficiency and retention of organizational knowledge; where capturing, finding, and sharing information is an integral part of the business process rather than a burden.

3. Content

Hand-in-hand with understanding organizational processes, analyzing the information and content that flows through these processes and how, is another critical aspect for forming the Current State Assessment and guiding the Target State definition. There is more to understanding content than just a straightforward content analysis effort. While diving into the actual content analysis, also consider the following:

• How “fresh” is your content and what are the obstacles for keeping it current? How much does your staff trust the content they find on internal systems?
• Where is this content housed? How is it organized and accessed? Is there a defined access control in place? Are there security and confidentiality concerns that need to be addressed?
• Do people collaborate in contributing new content? Are there approval workflows with established roles in place or are they not needed?
• What are the current procedures for knowledge retention when staff leaves?
• Do you need to collaborate or share some of your content with external audiences?
• How has the content been enhanced (with tags, formatting, etc)?
• Again, what silos exists and why?

Much of the discussion on content is tightly intertwined with the analysis of processes and vice versa. Knowledge objects are the building blocks of knowledge retention and dissemination through established processes. Performing content analysis will help you identify gaps, stale content, potential security risks, missing or deficient processes, and other areas that should be addressed in the Target State Definition.

4. Culture

Another important aspect that has crucial impact on enterprise KM, yet is often overlooked, is company culture. Along with processes and procedures, company culture shapes staff’s behavior and attitude toward capturing, managing, and sharing information. For example:

• Is knowledge sharing fostered by your company’s culture? Are there incentives for thought leadership contributions?
• Do people like to share or do they prefer to keep their intellectual property to themselves?
• What about sharing across business units?
• Further, does your staff feel pressured to maintain high utilization and does “data entry” hinder that?

These are just a few questions, but the answers will help guide a realistic KM Target State for your company. For instance, if collaboration between business units is important, yet there is information that needs to be protected, then streamlined sharing and content security are two important factors to be considered in the next steps of the KM strategy development, namely the Target State Definition and the KM Roadmap.

5. Technology

Covering the above aspects, by this point you would have heard the most important technology-related pain points. It is important, however, to thoroughly understand the existing technology ecosystem and the restrictions it implies. We approach this effort by addressing questions like these:

• Is there an existing IT architecture plan? What is the level of integration between systems, e.g. user account management, content and document management, intranet, search, taxonomy management, marketing and finance applications, etc.
• What is the technology stack preference of the organization? Microsoft, open source?
• What are the technology development and maintenance capabilities of the organization? Is there dedicated IT staff? What are their skills?
• Where in their lifecycle are current systems? Are any of them planned to be sunset? Are any new ones already in the procurement process?
• What are the cost factors? Do license restrictions cause inefficiencies?
• Is there an access control plan for the full technology ecosystem? If not, what issues does that cause?

Depending on company size, technology infrastructure can be minimal or overwhelmingly large and complex. For instance, on a past project, we found that there were at least three separate content management systems with their own search engines and various integrations. That led to significant confusion among staff as to where to search for what type of content. The overwhelming sentiment was that they preferred to use external systems to do their research. Having a number of KM systems with overlapping functions strongly hinders adoption.

It is crucial to gain a solid understanding of all current systems, their functionalities, users, restrictions, as well as where they are in their lifecycle. For example, if your company recently purchased the latest version of SharePoint or SalesForce, chances are good that these systems will make an appearance in your Target State Definition.

Benchmarking

In addition to the above themes, having a clear picture of how your organization’s KM capabilities stack up to the industry can be a valuable tool to inspire support and leadership for your KM transformation effort. To help visualize organizational KM capabilities, at EK we perform a benchmarking analysis of the company’s KM maturity based on our proprietary KIM Maturity Model. We utilize a variety of categories to determine the current level of the company’s KM maturity compared to industry standards. This approach not only helps you visualize your current KM state, but it also identifies areas where your organization lags behind the industry benchmark, which in turn can spur needed actions.

Concluding Remarks

Gaining a comprehensive understanding of the Current State of your KM is paramount for the KM strategy development process. However, it is only the first step. In upcoming posts, we will discuss steps two and three:

• Define an achievable KM Target State;
• Develop a realistic KM Roadmap.