Data Does Not Equal Knowledge

Knowledge Management entails the systematic and purposeful process of organization, management, storage, retrieval, protection and utilization of data and artifacts generated and resident within a given entity. Islands of unstructured, disconnected, distributed documents, files, data, and other products of daily work create information friction and other inefficiencies.  Our staff is well-versed in industry best practices to improve the use of your unique trove of knowledge.

Past - Paper Files

Paper files may seem like an archaic medium ,you may think that your organization has moved far beyond paper-based processes. But think about this: how many reams of paper does your organization go through? Now think about how much time is spent generating, storing, updating, finding, transmitting, and archiving those artifacts.<br /> The reality is staggering

Present - Personal Storage

Go ahead, open the documents file on your local machine, the file that no one else can access, index, archive, search, or retrieve from. Is it large? Is there are large quantity of disorganized files? Chances are there is, and even if you are one of the disciplined few, are your coworkers?

Present - Shared Storage

Shared storage, its a little better than local storage, but not much. Maybe you have a naming convention that everyone adheres to, with well-structured file systems, and clearly defined ownership and access permissions, and terabytes of outdated, useless, aged data that serves no purpose other than to take up space.

Present - Cloud Storage

So you're moving to the cloud, leveraging shared or enterprise services, great! Now, is it going to be any better than the disorganized document sump that existed in other mediums? or is just the same mess of electrons on some else's servers?

Future - AI and ML

Do you plan to leverage the emergent power of Artificial Intelligence and Machine Learning in the future? Here's the thing: AI and ML feed off of very clean, standardized, tagged, and organized data sets. If you are going to leverage AI and ML, you first need to figure out what you plan to get out of it, second you need to make sure your data is in a form in which it will even work. You need quality Enterprise Knowledge Management


Knowledge is the summation of the intellectual capital of all the members of your organization. It resides in their minds, on their desktops, in their documents, and across your information networks.  Organizing and managing that knowledge such that it is accessible and applicable to problem solving and decision making elevates intellectual capital to an appropriate central role in your organization.


There are numerous data management and analytical tools available with a steady stream of new innovations flooding the market. The powerful ability to track internal and external trends, analyze your effectiveness, manage your resources, capitalize on innovation, and many more capabilities are already available as off-the-shelf tools. But your data needs to be discoverable.  It needs to be managed both at rest and in flow so that newly created knowledge is seamlessly incorporated across your enterprise in real time.


Your people are busy, they don’t have spare time to conform to a new Knowledge Management system, changing their habits, work flows and structures. A properly planned, designed and implemented Knowledge Management solution does not cost time – it saves time.  It does so by reducing or eliminating the wasteful recreation or curation of existing knowledge, the searching through disconnected data sumps, and by creating more efficient information flows than those that result from being subordinate to an email inbox.  Knowledge Management saves users time, and thus creates value for them, keeping them more engaged and invested in accomplishing your organization’s mission.


Moving to a Knowledge Management system requires first a baseline assessment.

  • What Knowledge is resident in your organization?
  • Where is it located?
  • How is it created?
  • Who creates the knowledge, and why?
  • What do you want to be able to do better?

Answering these questions is the first step in framing your system requirements.


Our certified Knowledge Managers will use the baseline assessment to analyze the opportunities for improvement in your organization. Our staff will apply industry best practices to design systems and structures that conform to your organization’s mission, structure, and culture to maximize the positive impact of Knowledge Management, while minimizing any disruption or potential for organizational resistance to adoption.


We focus on right-sizing a solution to your organization, your budget and goals. You may want higher levels of automation for routine tasks, centralized storage for data analysis tools, distributed accessibility to fully support networked access, and leveraging best in class cloud capabilities.  Conversely, you may only want to manage your data more efficiently so your people can stop generating the same slides over and over, in slightly different formats or with minor revisions, with marginally different effects.

Road Mapping

Proper prior planning prevents poor performance, and road maps make visible often complex implementation plans in a clearly defined, time-phased, logical path.  Our technology and capability road mapping tools can synchronize your enterprise work force by improving information access, discovery, utilization, sharing and decision making.

Implementation and Optimization

Build-test-build is a safe and effective way to implement new capabilities with minimal disruption and maximum assurance.  We build and integrate a component of our Knowledge Management system. We then test its functionality, effectiveness, adaptability, etc. Once you are reasonably satisfied that the component operates effectively, we build the next capability.  While this sounds like a slow, risk averse process, quickly implementing and verifying each component is faster and more effective than deploying a whole system, having it break on day 1, then de-bugging and re-testing across an entire implementation.