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For many organizations, the promise of ALM has been undermined by disconnected tools, process, organizations and practices. But as organizations pivot business models and deliver faster, the need for ALM is much greater. Increased visibility and flow are the corner stones to increased business Agility. But for many organizations, adopting Agile at the team level has left enterprise ALM in a worse state than before, with teams adopting tools, practices and even process models for their own needs. So how do organizations increase the visibility of software delivery whilst software is being delivered by a diverse set of project teams, all following slightly different methods and using a variety of tools?
In this talk, Dave West, Chief Product Officer at Tasktop Technologies, and Tim Mulligan, ALM Architect at Fidelity Investments, discuss the value of introducing an ALM Data Model and how it can provide a common currency for visibility, collaboration and flow for large complex organizations. They describe what an ALM data model looks like and how it can form the backbone for any federated ALM strategy. The talk draws on practical experience by Fidelity of starting this endeavor and industry experience by Tasktop, who have worked with many organizations connecting information from disconnected tools. The talk will cover:
1. Why your organization needs an ALM data model.
2. What an ALM data model looks like.
3. How the ALM data model increases the value of CLM data.
4. Example implementation plan for an ALM data model.
Business Challenges
Want: increased visibility, transparency and governance with a heterogeneous, multi sourced development organization. Need: to increase control without forcing people to adopt one tool. Have: external suppliers developing software using a variety of practices and tools.
Business Benefits
A clear understanding of what ALM is, what the terminology is and how tools support this model. This enables an organization to build a data warehouse, integration strategy and technology / tools roadmap.
2. 2
Agenda
Why do we need an ALM Data Model?
• What is wrong with ALM ?
• The business value of the model
The future is connected, traceable,
historic and reported
• Introducing the ALM data model
How do you get there?
4. 4
ALM has not been that successful, with 30-70% of software
projects failing…
DevOps
Testers
Developers
Project
Managers
Business
Analysts
Silo tool focus has resulted in a
lack of focus on the end-to-end
business process.
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Which has led to ALM confusion
Not a pretty sight
Lack of ALM architecture
Data confusion and chaos
Governance problems
Traceability problems
Maintenance pains
Manual processes
Communications failing
Siloed thinking
Makes reporting, analytics and
ALM really difficult…
7. 7
It’s not that the brakes don’t work,
it’s the traceability of the software.
Pat Shanahan,
GM, 787 Project
8. And it will only get worse…
Software supply chains
becoming ecosystems
Shorter iterations
Build/measure/learn
ProcessEcosystem
Technology
API Economy provides fabric connecting
systems of record
Tools
Developers and teams selecting tools
that make them productive
9. What we can learn
– Process affects model
– App lives longer than project
– Need for manufacturing view
– Support part of the model
– Variants are complex and
important
PLM have done this before
10. Fidelity – Need for Standard ALM Data Model?
• Single vendor ALM tool stack is not realistic
• Best of breed means multiple ALM tool vendors
• Open Source ALM tools cannot be denied/ignored
• Rapid introduction, change, evolution
• Technologies require multiple ALM tool stacks
• Java versus .NET (square peg in round hole)
• Each vendor does what they think best
• Point to point ALM tool integrations …
• Provide traceability and synchronization
• Can be costly and fragile
• Do not address „full picture„ view of ALM
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11. Fidelity – Need for Standard ALM Data Model!!
We are manufacturers of applications and systems
– We should capture and analyze data related to our manufacturing process
– We should expect our suppliers to adhere to standards in the machinery we purchase from
them
– How have we gone this long without an industry standard SDLC/ALM Data Model?
– It has literally become the “Wild West” out there
Management should focus on facts/data/metrics - not controlling perception
We want a full picture of the application lifecycle
–Portfolios / Product Lines / Products / Applications / IT Assets
–Programs / Projects / Maintenance
–Resources
–Financials
–Measurements / Metrics / Analytics
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12. In The Future
Process / Flow
Activities
Reporting / Analytics
Detailed cross project and product
analysis including historic information
and analytics
Work moves seamlessly between silos
allowing collaboration, and governance
Practitioners are supported in doing
their work with practices they selected
and use
13. What we need
Consistent terminology, defined
relationships and structure
The Model Data warehouse
Single normalized warehouse of data
for cross project / product reporting
The Bus
Infrastructure that connects
the data that runs the
process
14. Consistent definition of the
artifacts
A clear understanding of their
relationships
Description of the key state
transitions of each artifact
Details of the reports required
Mappings to the locations
Defining the model
Consistent terminology, defined
relationships and structure
The Model
15. Org Structures
Portfolios
Products
Applications, Releases
IT Assets
Programs, Projects
Financials
Resources
Measurements
Describing the Data Warehouse
Data warehouse
Single normalized warehouse of
data for cross project / product
reporting
16. Enabling the infrastructure
The Bus
Infrastructure that connects
the data that runs the process
Support for integration to multiple tool
end points from different vendors
Can support 1000s of users and
projects across organizational, project
and company boundaries
Works in real time to enable real time
reporting and analytics
Supports open standards such as TRS
and OSLC and vendor formats
Does not require any changes to tools
and is invisible to teams
17. Example Data Model
Artifact
Lifecycle Asset
(value)
Executable Build Spec
Source Code
Rqrmnt
Definition
Product
Social Task
(work)
Work Item
Issue
Defect
Plan Item
Epic
User Story
Rqrmnt Incident
Project
context
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Scenarios Help Add Detail
Daily work in a development team
Team status reporting
Release software to production
Project planning
Test development and planning
Defect planning and development
Etc…
20. Example: Day to Day Work
Story
Defect
Task
Change
Set
Build
Test Plan
Test
Environment
Test
Result
Deployed
on Test
Built on
Builds
from
Build
Plan
Executes
on
Creates
Is executed
on
Failed or
passed
Is a type of..
Analysis
Policy
Evaluate
against
Source
Code
Comprises
Observ
ations
Analysis
Plan
Acts on
Acts on
Tech
Debt
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Fidelity - Tactical ALM Objectives
Standardize on a manageable number of ALM Tool Stacks
– Proposition: Here is what you get if you use the standard/preferred tools (value add)
Work with Tasktop to drive industry toward standard ALM Data Model
Develop an ALM Data Warehouse
– A governed repository of application lifecycle data
– To serve as a singular point for all data necessary to report/analyze the lifecycle of applications
throughout the enterprise
– Initial purpose will be Metrics & Analytics
Utilize ALM tool vendor supplied APIs whenever possible to extract desired data
Figure out how to store our “primary keys” within each ALM tool
Develop role-based dashboards
–Upper Mgmt, Portfolio Mgr, Program Mgr, Project Mgr, Resource Mgr
23. Recommendations
Treat ALM as a key business process
–Ownership
–Value
–Architecture
Create a data model
–Share model with teams and partners
–Use model to drive conformance
–Take advantage of model for reporting and analytics
Introduce technology to make it happen
–Think about how data moves around the model
–Employ technology to make it happen
–Make ALM a reality in your organization
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24. Become part of the working group
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We have a cross vendor organization working
group on defining an industry ALM data model
and best practices
Email – dave.west@tasktop.com to join
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