Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern. This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.

1. Building the Analytics Environment

2. Look Whose Talking
@tasktop
• Nicole Bryan, VP of Product
Management, Tasktop
– Passionate about improving the
experience of how software is delivered
– Former Director at Borland Software
– nicole.bryan@tasktop.com |
@nicolebryan
• Dr Murray Cantor – Senior Consultant,
Cutter Consortium
– Working to improve our industry with
metrics
– Former IBM Distinguished Engineer
– mcantor@cutter.com | @murraycantor

3. What we’ve learned so far….
• Webinar 1: There is no “one size fits all” metric nirvana
• Webinar 2: Use GQM to design the metrics that are right
for your mix of development
Today…
It’s all about the execution! Let’s get practical!

4. ©2015 Murray Cantor
Choosing metrics big picture
Agree on goals
- Depends on the levels and mixture of work
Agree on the how they fit into the loop
1. “How would we know we are achieving the goal”
2.” What response we should take?”
Determine the measures needed to answer the questions
- Apply the Einstein test (as simple as possible, but no
simpler)
Specify the data needed to answer the
questions
Automate collection and staging of
the data
4
Today

5. ©2015 Murray Cantor
From Goals to Measures to Data (GQM-ish)
1. Identify a set of corporate, division and project business goals and associated measurement goals.
2. Specify a sense-and-respond loop to steer to the goal.
3. Generate questions based on the goal that if answered:
• Let you know have achieved, are trending to the goal?
• Provide the level of detail necessary to take action
– Where is the problem, bottleneck?
• Communicate progress to stakeholders
– Summaries, rollups
4. Select or specify data needed to answer the questions in terms of state transitions of the relevant artifacts
5. Study the data to specify the data set and statistic needed to be collected to answer those questions and track process and
product conformance to the goals.
6. Develop automated mechanisms for data collection.
7. Collect, validate and analyze the data in real identify patterns to diagnose organization situation and provide suggestions for
corrective actions.
8. Analyze the data in a post mortem fashion to assess conformance to the goals and to make recommendations for future
improvements.
5

6. The “Last Mile Problem”
A phrase used in the telecommunications and technology industries to describe the
technologies and processes used to connect the end customer to a communications
network. The last mile is often stated in terms of the "last-mile problem", because
the end link between consumers and connectivity has proved to be
disproportionately expensive to solve.
Read more: http://www.investopedia.com/terms/l/lastmile.asp#ixzz3dAdJpzAQ

7. The Last Mile Problem

8. Aspiration without execution is useless!
No wait … It’s actually worse than
useless…

9. – If execution for your analytics solution is difficult it can quickly leads to
“The Light is Brighter Here” anti-pattern
Danger!!!!

10. How Do I Unlock All This Goodness?
PortfolioMgmt Agile
PM
Require
ments
TestDev
Operations

11. Why So Difficult?
– Tool Reality
• You have lots of them! So it’s not one ETL, its many ETLs! That gets
very hard to maintain.
• You’ve got disparate tools but your GQM needs single source fed by
variety of tools
– I’ve got defects in HP QC, Rally and JIRA – how do I calculate cycle
time!!!
• Yes, tool vendors have analytics solutions…. and these solutions are
focused on their particular areas of specialization

12. Why So Difficult?
– Logistics problems
• SaaS problem – sometimes data only available for limited time
• Transaction based data vs. reporting based data
• Many of the smaller more purpose built tools have no thought that
the transactional data they are producing needs to participate in a
larger analytics strategy
• You say tomato, I say tomato
Remember – you want your point tools to stay
focused on their domain expertise

13. What is the solution?
– Collated data across tools
– Abstraction away from specific tool representations of artifacts
– Near real time access
– Mix of simplicity so that you can just “get going” combined with
the ability to “get sophisticated” when you need to/are ready to

14. Powering software lifecycle analytics

15. 0
2
4
6
8
May June July Aug
0
2
4
6
ETL
Customer
Data
Warehouse
“Raw” Data
Storage in
customer
Database
(etc.)

17. Remember what Murray taught us?

18. ©2015 Murray Cantor
Kinds of Development Efforts: What is your mix?
18
1. Low innovation/high
certainty
• Detailed understanding
of the requirements
• Well understood code
2. Some innovation/
some uncertainty
• Architecture/Design in
place
• Some discovery required
to have confidence in
requirements
• Some
refactoring/evolution of
design might be required
3. High innovation/Low
Uncertainty
• Requirements not fully
understood, some
experimentation might be
required
• May be alternatives in choice
of technology
• No initial design/architecture

19. ©2015 Murray Cantor
Descriptive example: Cycle times
19

20. Let’s Bring Cycle Time to Life!!!!!!!!!!!

21. First, some key concepts of
Tasktop Data

22. Defects
Requirements
Test Cases
Timesheets
A tangible by-product produced during
the development of software.
Artifacts Collections
A set of artifacts from
your repository
Collection #1
Collection #2

23. JIRA Defects Collection
Priority
• High
• Medium
• Low
• Trivial
Summary
Fix Version
Description
Priority
• High
• Medium
• Low
Released In
Tags
M O D E L
Project #1
Project #2
Project #3

24. HP Defects Collection
Priority
• 1
• 2
• 3
• 4
Description
Release
Description
Priority
• High
• Medium
• Low
Released In
Tags
M O D E L
Project #A
Project #B
Project #C

25. Event Collection
Priority
• High
• Medium
• Low
Description
Released In
Description
Priority
• High
• Medium
• Low
Released In
Tags
M O D E L
* Raw database
collections are
a little bit
special

26. Reporting Integration
Flow Specification

27. And it will results in a
database table like
below
Another way of looking at it…
Use this
Model feeding
defects from
JIRA, HP, etc

28. Artifact
ID
Project Type Created Modified Severity Priority Status Release Assignee
DEF-1 Project A Defect 1/1/15 1/1/15 1 High Open
DEF-1 Project A Defect 1/1/15 1/2/15 1 High In
Progress
John
DEF-1 Project A Defect 1/1/15 1/5/15 1 Med In
Progress
John
DEF-1 Project A Defect 1/1/15 1/7/15 1 Med Shipped 1.0.0.1 John
1 Artifact, 4 Rows in Database
Event Log Concept

29. And once you’ve got that, you can easily get things like this….

30. Demo

31. (2) Create or reuse a model
(3) Create collections
(And Map the Collection to
the model)
(4) Create an integration
Four Easy Steps
(1) Connect to your system
1234

32. (1) Connect To Your System

33. (2) Create or reuse a model
• Identify the fields to flow
• Configure to Normalize the Data

34. (3) Create Collections (and map them)

35. (3) Create Collections (and map them)
One Core
Artifact Type
Sourced from
One Repository
Many Projects
Mapped to
One Model

36. • Configure fields and field values to
conform to the normalized model
values
• Transform values
Mapping Artifact to Model

37. (4) Create an Integration

38. Solves the Last Mile Problem
– Collated data across tools
– Abstraction away from specific tool representations of artifacts
– Near real time access
– Mix of simplicity so that you can just “get going” combined with
the ability to “get sophisticated” when you need to/are ready to

40. Stay in touch
@tasktop
nicole.bryan@tasktop.com
@nicolebryan
mcantor@cutter.com.com
@murraycantor

41. @tasktop
@cuttertweets