PROJECT DATA FLOW IS IS AN ENGINEERED SYSTEM PLANNING YOUR DATA - - PowerPoint PPT Presentation

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PROJECT DATA FLOW IS IS AN ENGINEERED SYSTEM PLANNING YOUR DATA - - PowerPoint PPT Presentation

Nov 29, 2023 37 likes •298 views

PROJECT DATA FLOW IS IS AN ENGINEERED SYSTEM PLANNING YOUR DATA SYSTEM TO REACH YOUR GOALS KEN JOHNSON NASA ENGINEERING AND SAFETY CENTER/ NESC INTEGRATION OFFICE NASA STATISTICAL ENGINEERING TEAM APRIL 5, 2017 R170329 HERES YOUR DATA

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SLIDE 1

PROJECT DATA FLOW IS IS AN ENGINEERED SYSTEM

PLANNING YOUR DATA SYSTEM TO REACH YOUR GOALS

KEN JOHNSON

NASA ENGINEERING AND SAFETY CENTER/ NESC INTEGRATION OFFICE NASA STATISTICAL ENGINEERING TEAM APRIL 5, 2017 R170329

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SLIDE 2

HERE’S YOUR DATA

April 5, 2017 2 Project Data System Planning

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SLIDE 3

WHAT’S HERE

  • Problem statement for this presentation:
  • Want to make sure the structure and process of passing information through a project is

recognized for what it is: A SYSTEM.

  • Show how system planning tools can make information interfaces preserve/ add value and

focused on SOLVING THE PROBLEM.

  • Goal of this presentation
  • Leave this room expecting to plan your next project data system through win-win

negotiation between data supplier and data customer

April 5, 2017 3 Project Data System Planning

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SLIDE 4

PROJECT DATA FLOW PER TEST, 100 TESTS

April 5, 2017 Project Data System Planning 4

Large-Data Flow (another diagram) Pre-Test Drawing (dimensional data)

Pdf of picture of drawing showing dimensions

Flash Report Analysis Test Requirements Post-Test Drawing (dimensional data)

Pdf of same drawing with ink pen markups

File Storage Timing Diagram Oscilloscopes Strain Fields from Imaging High-Speed Videos Analog Data Acquisition Systems

Pdf of Excel Worksheet

Camera Spec Sheet

Excel File Many Files and Formats Word File

Everything

Verbal Info

Problem statement: This is inefficient. We want less waste.

Pdf

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SLIDE 5

WHAT’S DATA? WHERE’S IT GOING?

  • Quantitative
  • Qualitative
  • Numbers
  • Images
  • Configuration info
  • Dimensions
  • Instructions
  • Drawings – specs – models – ……..

April 5, 2017 Project Data System Planning 5

Data is any kind of raw information that flows through a project Generated and gathered for a purpose: ANALYSIS

Analysis Post-Test Drawing (dimensional data)

Pdf of drawing with ink pen markups

File Storage

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SLIDE 6

THE SIMPLE SYSTEM TRANSACTION MODEL

April 5, 2017 Project Data System Planning 6

Analysis Post-Test Drawing (dimensional data)

Pdf of drawing with ink pen markups

Pdf of drawing with ink pen markups

Drawing Dims Analysis

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SLIDE 7

Pdf of drawing with ink pen markups

Drawing Dims Analysis

THE SIMPLE SYSTEM TRANSACTION MODEL

  • Information products flow from a

data supplier to a data customer

April 5, 2017 7 Project Data System Planning

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SLIDE 8

Pdf of drawing with ink pen markups

Drawing Dims Analysis

THE IDEA

  • Think of this as a system
  • The grey arrow represents a

system interface

  • … showing a transaction

between subsystems

  • The transaction is carried out

using data as the product

April 5, 2017 8 Project Data System Planning

Information Products

Data Supplier Data Customer

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SLIDE 9

GOOD NEWS: WE HAVE SMART HUMANS IN THE LOOP

  • Subsystems generally currently

include people who can negotiate directly to plan an optimized transaction and interface

  • No competition here: all

negotiations are win-win

April 5, 2017 9 Project Data System Planning

Information Products

Data Supplier Data Customer

Data

Engineer Another Engineer

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SLIDE 10

SYSTEM INTERFACE/ TRANSACTION QUALITY

Effective Interfaces

  • Test: can the data move from source directly into the

intended analysis engine without unnecessary work?

  • All in one place
  • Electronic spreadsheet/ database
  • Easy to integrate disparate data (key fields)
  • NOTES in their own fields integrated into the database
  • Standard rules followed
  • Row-column format
  • One piece of information per cell, one kind of

information per column…

  • All fields necessary to reach goals are present
  • Negotiated between data supplier and analyst

Poor Interfaces

  • Test: does the data user have to do something to be able

to analyze the data?

  • Scattered
  • Multiple pages, documents, formats, even sites
  • Hard to relate one dataset to another (config control)
  • Verbal information
  • Unanalyzable format: pdf, handwritten, graphs,

summaries

  • Pretty
  • Information stored in formatting but not in cells
  • Key information … where?
  • Specified by the data supplier

April 5, 2017 10 Project Data System Planning

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SLIDE 11

ANALYZABLE DATA TABLE FORMAT

  • Standards such as ADaM (see backup), IEEE, …
  • Easy and basic example: UCLA Institute for Digital Research and Education

http://stats.idre.ucla.edu/other/mult-pkg/faq/general/tips-for-creating-an- excel-file-that-can-be-easily-moved-to-a-statistical-program-for-analysis/

  • Uses a particular analysis program, but easy to get the idea – and links to an example of a

lousy data table

  • High value? Talk to an expert
  • Data Mining Team (DMT/ Bob Beil) …

April 5, 2017 Project Data System Planning 11

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SLIDE 12

PLANNING A SUCCESSFUL TRANSACTION: FOCUS ON SOLVING THE PROBLEM

  • Goals of the transaction

1. Make sure the final customer gets what s/he needs: a solved problem 2. See Goal #1

April 5, 2017 Project Data System Planning 12

Data

Data Supplier Data Customer

What data do you need to solve the problem? To solve the problem, I need:

  • 1. Location
  • 2. Dimension
  • 3. …
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SLIDE 13

DATA SUPPLIER’S RULE #1: EXPECT YOUR DATA TO BE ANALYZED

  • Deliver data in analyzable format
  • Find out how the analysis will be

performed

  • Set system/ transaction

requirements

  • … negotiate/ iterate as necessary
  • Don’t make extra work
  • … for supplier OR customer

April 5, 2017 Project Data System Planning 13

Data

Data Supplier Data Customer

The data recorder can give you

  • utput in .csv or

Excel format. .csv is better. Be sure that column headers are in exactly one row.

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SLIDE 14

DATA SUPPLIER’S RULE #2: TAKE IT EASY

  • KISS BNTS – keep the interface system

simple (but not TOO simple)!

  • Record it all
  • Columns are cheap; cleaning and

reconstructing data are expensive

  • Consider delivering raw data with reduced

data (ask customers)

  • Pass all important information through the

same interface

  • Consider recording data in the finished

form right at the source

  • Make the machine do it

April 5, 2017 Project Data System Planning 14

Data

Data Supplier Data Customer

How are we handling configuration control? Can you set up the data recorder so it outputs test date, test number and test article number as data columns?

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SLIDE 15

DATA CUSTOMER’S RULE #1: COMMUNICATE WHAT YOU NEED

  • Have an analysis plan focused on

solving the problem

  • Know what inputs are necessary

for the analysis

  • Know the format needed for

analysis

  • Communicate this to your

supplier

April 5, 2017 Project Data System Planning 15

Data

Data Supplier Data Customer

What do you need? Glad you asked. Let’s grab lunch and talk about it.

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SLIDE 16

Data

Cu

Data

Data Supplier Data Customer / Supplier

DATA CUSTOMER’S RULE #2: REMEMBER: YOU’RE A SUPPLIER, TOO

  • Remember the goals of your

analysis – and of the overall task

  • Negotiate concurrently with your

customers and suppliers

  • Better yet: negotiate together as a

system

April 5, 2017 Project Data System Planning 16

What do you need? Glad you asked. Let’s grab lunch and talk about it. I’ll get my coat!

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SLIDE 17

Pdf of drawing with ink pen markups

Drawing Dims Analysis

ADD VALUE

  • Build in visualizations
  • This is where you do pretty
  • Report summaries, graphs, …
  • Consider real-time visualizations
  • Pass data forward in standard

analyzable format for future users

  • Give future projects a database

to add to

April 5, 2017 Project Data System Planning 17

Data

Data Supplier Data Customer

… and my analysis software reads the data directly off the same database.

Standard- Format Relational* Database

Drawing with Data Entry Fields Analysis Software

The tech just enters the data into form fields right on the drawing…

* Look it up. You want one of these.

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SLIDE 18

WHAT WE NEED TO DO: SYSTEM AND STATISTICAL ENGINEERING

  • Problem statement (ALWAYS)
  • Goals
  • Plan for success
  • Expect to analyze data
  • Data transfer in a format that makes sense
  • Negotiate between supplier and customer
  • Think about the system
  • How can waste be pulled out of the system?
  • How can value be added?
  • Think about the future
  • Expen$ive data
  • Future customers are customers
  • Longevity
  • Complete
  • Well-documented
  • Useful
  • Efforts
  • NESC’s Data Mining Team (DMT – Bob Beil)
  • Big Data Working Group …

April 5, 2017 18 Project Data System Planning

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SLIDE 19

THANK YOU

THANKS TO BOB BEIL (NESC/ KSC), JILL PRINCE (NESC/ LARC), CHRIS KOSTYK (AFRC-RS), JON HOLLADAY (NESC/ GSFC), VICKI REGENIE (NESC/ AFRC)

April 5, 2017 Project Data System Planning 19

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SLIDE 20

BACKUP

April 5, 2017 20 Project Data System Planning

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SLIDE 21

TMI: FUNDAMENTAL PRINCIPLES OF THE ANALYSIS DATA MODEL (ADaM)

April 5, 2017 Project Data System Planning 21

Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model Version 2.1 https://www.cdisc.org/system/files/members/standard/foundational/adam/analysis_data_model_v2.1.pdf

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SLIDE 22

ENGINEERING

  • Engineering is the application of mathematics and scientific, economic, social, and practical knowledge

in order to invent, innovate, design, build, maintain, research, and improve structures, machines, tools, systems, components, materials, processes, solutions, and organizations.

  • The discipline of engineering is extremely broad and encompasses a range of more specialized fields of

engineering, each with a more specific emphasis on particular areas of applied science, technology and types of application.

  • https://en.wikipedia.org/wiki/Engineering

April 5, 2017 22 Project Data System Planning

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SLIDE 23

STATISTICAL ENGINEERING

  • Statistical engineering is the application of statistical concepts, methods and tools along with

information technology and other relevant sciences in order to invent, innovate, design, build, maintain, research, and improve structures, machines, tools, systems, components, materials, processes, solutions, and organizations.

  • The discipline of statistical engineering is extremely broad and encompasses a range of more specialized

fields of statistics, each with a more specific emphasis on particular areas of applied science, technology and types of application.

  • The focus is on:
  • Uncertainty and variation
  • Large, messy problems

April 5, 2017 23 Project Data System Planning

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SLIDE 24

STATISTICAL ENGINEERING

  • Statistical engineering is the study of how to best utilize statistical concepts, methods,

and tools and integrate them with information technology and other relevant sciences to generate improved results. In other words, engineers—statistical or otherwise—do not focus

  • n advancement of the fundamental laws of science but rather how they might be

best utilized for practical benefit. – Roger Hoerl and Ron Snee quoted by Tom Pyzdek http://sixsigmatraining.com/statistical-tools-for-six-sigma/statistical-engineering.html

April 5, 2017 24 Project Data System Planning

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SLIDE 25

NASA STATISTICAL ENGINEERING TEAM/ NSET

  • Ken Johnson, NESC/ MSFC, Lead
  • General stats, DOE, statistical quality
  • Lee Allen, MSFC
  • DOE
  • Amy Braverman, JPL
  • Large-dataset stats
  • Bob Graber, Stargroup
  • Risk/ reliability, probabilistic modeling
  • Ray Kacmar, JPL/ HQ
  • Risk analysis
  • Peter Parker, LaRC
  • General stats, DOE, UQ
  • Jim Womack, The Aerospace Corp, Deputy
  • General stats, DOE, risk/ reliability, math stats
  • Jim Rogers, MSFC
  • Risk/ reliability, Bayesian methods
  • Anne Ryan-Driscoll, VA Tech
  • General stats, DOE, math stats
  • Walt Thomas, GSFC
  • Reliability
  • Jon Tylka, WSTF
  • DOE
  • Dan Wentzel, WSTF
  • DOE
  • Pres White, UVA
  • Probabilistic modeling, UQ

April 5, 2017 25 Project Data System Planning

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SLIDE 26

SMQS_Module_201.04_Structure_InternalBlockDiagrams.pdf

April 5, 2017 26 Project Data System Planning