# Presentation: 139907

## Knowledge Management

**eRA Project Team Briefing**
**April 9, 2002 at 9:00 am**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
I read in some of the literature for the upcoming meeting on May 10:  “Each year, the NIH receives and processes approximately 45,000 biomedical research and research training applications, generating hundreds of millions of pieces of paper and countless manual activities.”

I am thrilled to be working for an organization that has as a primary agenda item “to reduce paper, improve access to information, and facilitate communications, and increase overall efficiency.” 

Many organizations have similar ambitions and are addressing efficiency issues with computer applications.

But KM strives to go beyond simple efficiency gains, by treating the stored knowledge of an organization as a primary asset and then mining that asset for all it is worth.

## to be discussed

**working definition**
**examples**
**possible pilot projects**
**next steps**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
Here is what I hope to cover today:

 I want to provide you with a working definition of knowledge management.

 I’ll review examples of various software tools that are considered KM apps.

 I’d like to discuss the central question:  “What’s in it for NIH?”

 Finally, I want to refer briefly to a case study from my own experience and mention the lessons learned in the KM exercise at the World Bank.

## working definition

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## promise

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
All of us know the promise of computers in the workplace.

In a paper-based world, the cost of maintaining stored information always on the increases, while the value of that information decreases the more data we accumulate.

Computers have always been expected to reverse this trend, by simplifying processes of handling data and documents.

Few of use can claim to be wholly satisfied that this promise has been fulfilled.

KM attempts to fulfill this promise in at least one way. 

KM concentrates on extracting value from stored data.

## what is knowledge management?

- Knowledge management is . . .
- systematic process by which knowledge is created, captured, shared, and leveraged
- driven by:
- organizational objectives
- need to share and identify trends
- IT tools
- search, analysis, and presentation
- serves _one_ or _many_ end-users

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
Knowledge management is . . .  

 defined as “the systematic processes by which the knowledge needed for an organization to succeed is created, captured, shared, and leveraged”. The Idiot’s Guide to KM, Rumizen, 2002

 driven by the need to share and identify trends in stored data across time and institutional boundaries.

 a set of IT tools that enable the search, analysis, and presentation of archived information for one or many end-users.

## what is value?

- Qualities that make information **valuable**:
**accuracy**
**timeliness**
**accessibility**
**engagement**
**application**
**rarity**
*Source:** *_Davenport &__ Prusak_* (1997)*

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
It is important to consider the qualities that make information valuable. 

  accuracy - inspires confidence
  timeliness - appropriately current
  accessibility - can be readily located when required
  engagement - making an impact, influencing decisions
  application - relevant and useful within the defined context
  rarity - provides a hitherto unknown or confidential insight.

These provide the basis for KM applications that are now proliferating.

## bi  vs  km

**SUPPLY CHAIN ****VS**** STORED DATA**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## KM focus

**KNOWLEDGE**

**INFORMATION**

**DATA**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
In any knowledge management exercise, the focus is on the value creation process: 

  What are we doing with data that makes it more or less useful to our organization?  
  What are we doing/not doing that transforms data into information and into useful knowledge.

The key is to understand
  acquisition
  conversion
  analysis
  management
  synthesis, and the 
  use of data. 
Workflow and tacit assumptions about the data are also key.

## aligned w/ group & end-user needs

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
It should never be forgotten that the value of any information is only as great as the extent to which it serves the needs of end-users and the mission critical objectives of the organization.

## KM functions & apps

| function | applications |
| --- | --- |
| Information
Retrieval | search engines, intelligent agents , push technology
 |
| Knowledge Discovery | data mining, visualisation 
 |
| Knowledge
Sharing | groupware, doc management, best practice DBs
 |
| Knowledge Development | pattern recognition, concept mapping |
| Intellectual
Capital | measurement systems, bibio- or scientometrics
 |
| Integration | mediation, portals, KM suites, After Action Reviews
 |

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
Knowledge management software basically enables three computer-driven routines:
  search databases
  data analysis, and the
  presentation of data or its analysis.
These computer-supported routines are also referred to as information retrieval, knowledge discovery, collaboration or knowledge sharing, knowledge development, intellectual capital systems, and integration systems.

Software applications within each of these functional families are 
Info Retrieval - search engines, intelligent agents , push technology
Knowledge Discovery - data mining, visualisation 
Knowledge Sharing - groupware, doc mngmt, best practice DBs
Knowledge Development – pattern recognition, concept mapping
Intellectual Capital - measurement systems, bibio- or sciento-metrics
Integration – mediation, portals, KM suites, After Action Reviews

## examples

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## example: mediation

**existing**
**content**

**preference**

**relevance**

**existing**
**content**

**existing**
**content**

**existing**
**content**

**existing**
**content**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
At this point, I’d like to briefly discuss one example of a knowledge management application.  

Whatever the organization – government, industry, science- or business based – we all have existing content that we’d like to learn from.

The first step in extracting new value from our databases is to clearly articulate what we want to learn. Frequently these knowledge preferences  are complicated and they tend to vary from one person to another. 

In knowledge management, computer applications are developed that assist with boiling down these preferences (what we want to know from one or more datasets) and giving us information that is tailored – made relevant -- to some specific task.

In this case, relevance refers to the process of giving us exactly the right information, when and where needed, in a readily usable form.

## example: mediation

**existing**
**content**

**preference**

**relevance**

**existing**
**content**

**existing**
**content**

**existing**
**content**

**existing**
**content**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**

The component parts of a KM application for mediated knowledge discovery might include:

  profile catalogues
  thesaurus
  abstraction engine
  matching engine.

## example: executive awareness

**VISION: communicate & set policy w/ more detail on funded projects**
**BARRIER: nomenclature differences and dynamics**
**ACTION: smart(**_**er)**_** text search**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## example: communities of practice

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## lessons learned: world bank

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
In closing, I’d like to leave you with the point that KM is not just about the computer software.  It is also about organizational issues and managerial challenges that must be addressed in order for KM to be effective. The best example I can think of to illustrate this point is the World Bank.

On May 24, 2001 the Bank was the only public organization in the top 20 in this year's Most Admired Knowledge Enterprises (MAKE) study. The World Bank Group took 4th place in 2001, up from 10th place last year. The organizations in the study are recognized for their world-class efforts in managing knowledge, leading to superior performance.  Others on the list include General Electric (1st), Hewlett-Packard (2nd), Buckman Laboratories (3rd), Microsoft (5th), BP Amoco (6th) and Siemens (7th).

Lessons learned from the WB experience are as follows:

  Organizational dimension, necessitated multiple knowledge flows, new communities of practice, redefined relations with end users (e.g., as partnerships).
  Budget allocated to knowledge communities on basis of clear work agreements.
  Technologies are essential, e.g., for knowledge capture and dissemination and to enhance knowledge re-use.
  Permanent capacity to facilitate the brokering of information and knowledge (Advisory Services/Help Desks).

## possible pilot projects

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## pilot A

**VISION: reduce review workload**
-   reviewer selection
-   conflict elimination
-   proposal assignment
**BARRIER: relevance**
**ACTION: mediated retrieval**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## pilot B

**VISION: better clinical decisions**
-   legacy data
-   many, distributed projects
-   multidisciplinary analysis
**BARRIER: nomenclature diffs**
**ACTION:  robust data models**

**Star Schema Data Model**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## pilot C

**VISION: timely program planning**
-   emerging research trends
-   relation of novelty to score
**BARRIER: discerning salience,**
-   signal-to-noise ratio
-   e.g., “naked” DNA
**ACTION: bibliometrics**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## next steps

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## next steps

**assess & develop a strategy**
**start pilot projects w/ key data**
**address organization (communities of practice)**
**commit to infrastructure**
*************************
**TODAY**
- feedback
- pilot projects
- tiger-team volunteers

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

## gather more information on KM

**Faculty of Information Studies, University of Toronto **_**http://www.fis.utoronto.ca/Programs/kmi/index.**__**htm**_
**IT Toolbox    **_**http://www.ittoolbox.com**_** **
**Upper Ontology **_**http://ontology.teknowledge.com/**_
**Health Services, Univ Washington, Module 38: Knowledge Management **_**http://courses.washington.edu/~hs590a/hs590a.html**_** **
**American Productivity and Qualty Center**
**KM Benchmarking Program, **_**http://www.apqc.org/km**_**  **
**David Skyrme Associates  **_**http://www.skyrme.com/services/ktrends.htm#Tools**_** **
**WARNING: BEWARE OF MARKET HYPE**
**& CONCEPTUAL INCONSISTENCIES!!!!**

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634

**Notes:**
Finally, let me leave you with some links to additional information.

Those who are really interested in KM are strongly encouraged to follow-up and learn more at these links.

## end

- Richard W. Morris, PhD   NIAID/DAIT   rmorris@niaid.nih.gov    301-594-7634