Knowledge Management Status Report to eRA Project Team · Knowledge Management Status Report to eRA...

27 February 2003 Richard W. Morris <rmorris@niaid.nih.gov> 1

Knowledge ManagementStatus Report to eRA Project Team

Transition to Pre-Production Phase

Life Cycle of Disruptive TechnologiesGOAL: DEMONSTRATION

STAGE 1

DRIVER: CONCEPT

GOAL: MATURITY / DIFFUSION

STAGE 3

DRIVER: COMPETITION

GOAL: NICHE APPLICATIONS

STAGE 2

DRIVER: TRUE BELIEVERS

Myers, et al “Practitioner’s View: Evolutionary Stages of Disruptive Technologies”; IEEE Transactions, v. 49, no. 4, Nov. 2002

Aims Today

1. Where we have been.

2. Where we are going.

3. How we’ll get there (if we answer a few questions).

Where we have been.

Concept Phase

pre-production phaseconceptual phase

performancerequirements

detaileddesign

build / testprototype

assessresults

modify(produce)

system requirements

functionalrequirements

design phase

Source: Management of Systems Engineering, Wilton P. Chase

Conceptual Phase

system requirements

MITRETEKconceptual phase

detaileddesign

assessresults

modify(produce)

collexis

stratify

inxight

Design Phase

detaileddesign

assessresults

modify(produce)

system requirements

design phase

Design Phase

detaileddesign

assessresults

modify(produce)

system requirements

conceptual phase

detaileddesign

assessresults

modify(produce)

Pilot #2: reviewer selection

Pilot #1: situational awareness

design phase

Project Management Plan

detaileddesign

assessresults

modify(produce)

system requirements

conceptual phase pre-production phase

design phase

Dec. 1, 2002 Feb. 1, 2003 Sept. 1, 2003

Next Steps: Pre-Production

detaileddesign

assessresults

modify(produce)

system requirements

design phase

Where we are going.

NIH KM OverviewCore KM Prototypes

Expanded NIH KM OverviewComplete System

Metrics for GRSEstimated Benefits•Reduced cycle time•Improved quality and consistency of referrals•Time saved by the organization

Methods Used•Sampling•Survey•Interviews•Internal Logs

Key Measures Key Outputs Key Outcomes System Performance

• Recall • Precision • Reviewer Selection • System Response time • Scalability

o Number of research proposals

o Number of reviewers • Institute Routing

• Time spent “selecting” candidate reviewers

• Time spent “screening” candidate reviewers

• Number of conflicts identified • Percentage of candidate

reviewers chosen • Percentage of correct institute

routing

• User satisfaction • Time saved by the organization

in “selecting” and “screening” candidate reviewers

• Savings or improvements in organizational quality and efficiency

• Time saved in institute routing

System Usage

• System down time • Scalability

o Number of users o Frequency of use

• User Feedback (real-time) • Usability survey (time-lag) • Training time /learning curve

• Usefulness survey • Feedback results • Duration of learning curve • Duration of training time

• User satisfaction • Savings or improvements in

organizational quality and efficiency

• Time saved by the organization • Reduced training time or

learning curve System Operation & Maintenance

• Frequency of Updates • System Downtime • Help Desk Support

• Number of Help Desk support requests

• User satisfaction • Reallocation of Help Desk

resources • Recency of Information

Metrics for GTSEstimated Benefits•Situational Awareness•Discovery of Patterns and Trends•Informed Decision Making•Time saved by the organization

Methods Used•Sampling•Survey•Interviews•Internal Logs

Key Measures Key Outputs Key Outcomes System Performance

• System Response time • Scalability - Number of research

proposals • Proposal Analysis

• Time spent in understanding proposals

• Time spent in analyzing and identifying relationships among concepts

• Percentage of successful document categorization

• Time spent in analyzing and identifying distributions

• User satisfaction • Time saved by the

organization • Awareness of relationships

among proposals • Improvements in document

categorization • Visual awareness of

distributions, patterns and trends

System Usage

• System down time • Scalability

o Number of users o Frequency of use

• User Feedback (real-time) • Usability survey (time-lag) • Training time /learning curve

• Usefulness survey • Feedback results • Duration of learning curve • Duration of training time

• User satisfaction • Savings or improvements in

organizational quality and efficiency

• Time and reduced cost saved by the organization

• Reduced training time or learning curve

• Visual identification of concept relationships

System Operation & Maintenance

• Frequency of Updates • System Downtime • Help Desk Support

• Number of Help Desk support requests

• User satisfaction • Reallocation of Help Desk

resources

How we’ll get there.

1. Understand impact of disruptive technologies.

2. Use KM to align workflows and data flows.

3. Answer the hard, but practical questions.

Life Cycle of Disruptive TechnologiesGOAL: DEMONSTRATION

STAGE 1

DRIVER: CONCEPT

GOAL: MATURITY / DIFFUSION

STAGE 3

DRIVER: COMPETITION

GOAL: NICHE APPLICATIONS

STAGE 2

DRIVER: TRUE BELIEVERS

Myers, et al “Practitioner’s View: Evolutionary Stages of Disruptive Technologies”; IEEE Transactions, v. 49, no. 4, Nov. 2002

workflows

managecaptureinputs release outputs

display

decisions decisions

taskstasks

data flows

managecaptureinputs release outputs

process process

extract/mergeprep/input collate/sort

display

decisions decisions

taskstasks

KEY QUESTIONS

• Do we have a credible means of verifying best practices?

• Do we have baselines and are we ready impacts? Assessment• Is the XML corpus ready? (If so, when and where?)

• Do we have the needed data sets?Inputs• Does the contractor have the resources and skillsets?

• Do we have the staff to manage and oversee the project?Staff• Does the contractor have needed resources / skillsets?

• Do we have staff to manage and oversee the project?Management• Do we have funds / plan for a full-scale implementation?

• Do we have Phase 2 funds — for pre-production piloting?Budget• Do we have the pilot sites identified, with buy-in?

• Is the organization ready?Readiness

IC of the Future

IC of the future must serve theneeds of several end-users:

– experimental biologists,– clinical researchers,– science administrators, and– even public health officials.

Biology today is quantitative;it depends on computers for the– production,– analysis, and– management of scientific data.

SJ Wiback and BO Palsson Biophysical Journal, 8:2002

Expanded NIH KM OverviewAssisted Specialized Taxonomy Generation

Expanded NIH KM OverviewResearch Proposal Archiving & Collaborative Resources

project plan

detaileddesign

assessresults

modify(produce)

system requirements

conceptual phase pre-production phase

design phase

PM plan

detaileddesign

assessresults

modify(produce)

system requirements

design phase

internal leadership end-userSource: Management of Systems Engineering, Wilton P. Chase

KM Project Overview

detaileddesign

assessresults

modify(produce)

system requirements

conceptual phase (pre-)production phase

design phase

Conceptual Phase

system requirements

detaileddesign

assessresults

modify(produce)

Pilot #1: situational awareness

collexis

stratify

inxight

Pilot #2: reviewer selectionMITRETEK

conceptual phase

Knowledge Management Status Report to eRA Project Team · Knowledge Management Status Report to eRA...

Documents

NEW ERA PUBLIC SCHOOL GENERAL KNOWLEDGE ASSIGNMENT-1 … · new era public school general knowledge assignment-1 class viii ... monuments of india ... 2.sanchi stupa villupuram,

El Tercer Status. La Era Del Espu

Knowledge Era Knowledge Management in Multinational Company – Role of KM in Project Management Scenario

Technology Management in the Knowledge Era

The current status of knowledge of herbal medicine and

History of Knowledge Management Moving from the industrial era to the knowledge era created a constant state of change. Peter Drucker identified a major

Hr in Knowledge Era

RT 201 Proudly Presents: Leadership in a Knowledge Era

Biodiversity Status And Indigenous Knowledge Systems In

Current Status of Knowledge of Molten Fuel/Sodium

Knowledge status for structure liming in Norway

Knowledge Dissemination in the Web Era

ERA-NET PathoGenoMics Status seminar participants Tenerife, 18th January 2010

Political Knowledge and Misinformation in the Era …kmunger.github.io/pdfs/uk_paper_KM_final_JN_rv12.pdfPolitical Knowledge and Misinformation in the Era of Social Media: Evidence

1 Leadership and Management in the Knowledge Era Dr. Charles M. Savage Knowledge Era Enterprises, Inc. Co-Creating the Organizations of the21st Century

High Energy Neutrino Telescopes. The current status of knowledge

EEL STATUS AND KNOWLEDGE IN THE GIRONDE - … · eel status and knowledge in the gironde – garonne – dordogne area : exploitation, ressource and environment mi.ga.do. – aadppedg

Knowledge Management in Organizations - Managing in the Knowledge Era

Knowledge Era Paradigms -agile indiaconf2016

Mothers Knowledge On Balanced Nutrition To Nutritional Status of