Profile Meeting 6/24

What drives changes in data models, and how can we best manage those drivers and/or changes?

June 24, 2020, 3pm Eastern

Email eva@makingbetter.us  to be added to the invite 

Team

  • Host: Aaron Silvers

  • Community Manager/Secretary: Eva Rendle

  • Producer: Megan Bowe

  • Attendees:

    • Jonathan Poltrack (Veracity)

    • Tom Creighton (Veracity)

    • Kristi Eager (Boeing)

    • Brian Duck, DuckWorks LLC

    • Jeff Clem

    • Lang Holloman

    • Marcus Birtwhistle (ADL)

    • Ivan Martinez-Ortiz

    • Tobi Echevarria (Veracity)

    • Jason Haag (Veracity)

    • Viktor Haag (D2L)

    • Christina (on phone)

    • Rob Chadwick (Veracity)


Please join us for a discussion on the topic, “What drives changes in data models, and how can we best manage those drivers and/or changes?”’

Slides: https://1drv.ms/p/s!Av4nfZFOBTgagVGDm5LgvQ4SuYcC

Discussion:

•Objective:

Through regular, open discussions, participants will help identify enabling and/or disabling policies and practices, as well as technical considerations, in leveraging xAPI Profiles (from one or a network of xAPI Profile Servers).

•Exit Criteria:

TAGxAPI will submit an IEEE LTSC technical report for publication, documenting learnings from this series of discussions in Q3 2020

Data Models:

  • Account for the data to be generated and used by a system or systems, both logical and physical.

  • Provide names, definitions and notes describing the data model elements.

  • Accompany implementation guidance, such as data types, keys, indexes and views that are critical to the challenges addressed by the data model(s).

  • Describe business rules in terms of relationships (referential constraints) and values (restrictions

  • Identify factors to secure data, data sources and any rules to be maintained.

  • Define governance plans, including enterprise-level collection details (Master Data Management), data quality and retention policies.

Benefits of Data Models

•Design

•Manage Redundancy

•Integrate & rationalize

•Increase quality

•Implement & Maintain

•Increase discoverability

•Improve comprehension

•Data dictionaries

•Business glossaries

Business Glossaries

  • Maximize understanding of the core business concepts and terminology of the organization

  • Minimize misuse of data due to inaccurate understanding of business concepts and terms

  • Improve alignment of the business organization with the technology assets and technology organization

    • Help provide a source of truth for stakeholders – what they are looking for and why

  • Maximize the accuracy of the results to searches for business concepts and associated knowledge

Drivers that Change Data Models

  • Deleting something

  • Adding something

  • Renaming something

  • Changing the data type of something

  • Changing the constraints or rules for something

Responsible Parties

Different organizations approach data models differently, so drivers for data models, and changes to existing data models, vary.

    • Who understands the value of the data models-

      • Use of data models across projects

      • Strong variance between organizations and who in the organization cares about them


Communication Gaps

  • Only 45% of projects tend to include a data modeling team early enough in the processes to influence development

  • On 3% of projects surveyed, the data modeling team was included only after decisions were made that could not be changed

  • 28% of organizations only document a data model AFTER delivery of an application

  • In 25% of projects where a data modeling team is available, the team finds out about the project too late.

Shared understanding required – gaps become exacerbated with lack of communication

Data Model Change considerations

  • Must work in concert with any workflow – not just waterfall; not just agile

  • Check-in and Check-out capability

  • Associate data model changes to requirements such that they can be controlled for

  • Audit trail of changes: what was done; why; compliance with governance policies/rules

  • Ability to compare models to databases and other models and identify changes that need to be merged into the source of the target

  • Capability to create branches from a model baseline and merge them back in or roll back to restore a previous version

  • Ability to generate the necessary code to implement the desired changes

How do xAPI Profiles Fare?

  • Supports different working styles and workflow approaches to data modeling

  • Enables machine-readable data dictionaries via the JSON-LD representation of the profile.

  • A proper xAPI Profile document (human-readable) serves as a business glossary

  • When managed  through an xAPI Profile Server…

    • We can track *why* changes are made, in addition to *what* changes are made

    • We can correlate changes with development changes

    • We can implement model version control

    • We can provide an audit trail for compliance or governance


Discussion question-

What are some challenges to consider when establishing or changing a data model?

Jono Poltrack:  SCORM example – SCORM has a data term of SCO (when written, SCO was created)

  • Data perspective – everything is a SCO

    • Chapter, learning objective, lessons, modules – whatever you call it is fine in your organization

 

Realizing that Activity type (lesson) is overloaded in xAPI

 

Abstract concept (lesson – certain set of data)

         Technical definitions vs instructional designer is trying to create (lay vocabulary)

  Technical artifacts are created

 

Aaron Silvers: Instructional design does not have same set of vocabulary across the industry, little bit like the wild west- no normalized training, people come into it from different ways

 

Need common vocab or labels

 

Jono Poltrack: Elearning example:

  • Lesson – technical perspective – needs particular data around it (score, pass/fail, completed)

  • Needs rules around it – how underlying data should look, but not putting constraints on organizations and their language

 

Kristi Eager:  Video Profile example

  • New products come out and they reference old versions of profiles

  • How likely will data model need to change, capture data differently over time?

  • If something changes, how make sure some data point doesn’t get abandoned

 

Jono – IEEE could be helpful in this problem, especially with the more general profiles like audio, video

         Create a standards process

         9272.3 – standards for Profiles

 

Profile server administration –

  • Are profiles verified - 

    • Yes, has verified Profile – like a verified twitter account

  • Verified over time

 

Kristi: How long is xAPI data valuable for?

  • Landmark statements?

  • Currently all statements are not deletable

  • As more hardening enterprise management and analysis comes into play – probably a time limit, or maybe a state or maturity.. longer term question

 

Eva Rendle