Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
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Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automated and Continuous Data
1. 1
THE GLOBAL LEADER IN CONTINUOUS TEST AUTOMATION
Data Integrity
Automation
Drive better business
outcomes through data you
can trust
2. 2
Data Integrity Automation Agenda
Why? To reduce your risk of failures, you must test your processes for integrity. Data Integrity can ONLY be
achieved by testing ALL your data processes and ensure integrity end-to-end, with automation, and do it
continuously
Problem to be Solved Why it is not Solved
How to Solve it Benefits of doing so
What are the Consequences
3. 3
Data Governance for Data Trust
Metadata
and Data
Catalogs
Thousands of Hours to Create Complex Reports
People – Process – Technology
Data Delivery Culture – Access – Stewardship – Quality - Utilization - Acquisition
Data
Quality
Master
Data
Data
Security
Data Governance is the
Epicenter of Data Disruption
comes with Information
Stewardship
Data
Lifecycle
4. 4
Change is constantly at work
across your digital landscape
Application
Changes
Data
Changes
Environment
Changes
On-Prem
Cloud
Partner
ecosystem
web apps
AI/ML-
driven
initiatives
6. 6
Accelerated Digital Change
*Sources: Mayfield CXO Survey – Post COVID-19 Impacts to IT, IDC FutureScape IT Industry 2021 Predictions, ASUG Tricentis Survey 2021 – Future of SAP Delivery
CLOUD
MIGRATION
85% plan to shift to
cloud-centric
infrastructure &
applications twice as
fast as before the
pandemic
APPLICATION
MODERNIZATION
67% plan to migrate
half of on-prem
applications. 91% plan
to upgrade to SAP
S/4HANA in next 24
months
RAPID AND
REGULAR UPDATES
released by enterprise
apps like SAP, Salesforce,
ServiceNow
DATA
MIGRATION
43.5% say data
migration is the main
challenge when moving
to advanced
versions/upgrades
DIGITAL
OPTIMIZATION
50% plan to digitize
eCommerce, deliver
new features to
improve customer
self-service & UX
7. 7
If you don’t deal with change, expect consequences…
Delivering poor quality Being late Inefficient resource
management
Large multinational bank
24h downtime = $7 million loss
and reputational damage
Telecommunications provider
Manual testing = 10K tests
3 releases/yr, delayed innovation
Global oil & gas company
Required highly technical skills,
>$45 million maintenance costs
8. 8
If you don’t deal with change, expect consequences…
Accounting Failures Costly Compliance
Fines
Major insurer ERP consolidation of
data flows,
Leads to business operations
failures (40K Invoices)
Major Bank
>$50 million in
due to bad data quality used
for AML/KYC regulatory
requirements
Loss due to Data
Analytics Platform
Mistrust
For WorldPay, we helped deliver
decision-grade data to business
teams from massive volumes of
transactional payment data.
Thousands of hours of manual
effort saved per month AND
Trust in the numbers regained
9. 9
Data can break anywhere in the process
because of dangerous data management gaps
DATA WAREHOUSE
DATA ANALYTICS / BI
ECOSYSTEM
Information
Steward
Data
Services
Advanced Data
Migration by Syniti
MDG
10. 10
Why? - Data Gaps in Testing Coverage
Gap Reasons
• Point Solutions only check one point in the process. Examples, MDM, ETL Test Tools..
• MDM is a production problem catcher, not a fixer, and not a tester solution
• Solutions like Snowflake are focused on the data processes themselves not guaranteeing the
quality of the data OUTSIDE their processes.
• ETL providers
• Report Testing
11. 11
So, the race is on to find the data errors
EXTRACT
Enterprise data
warehouse
Data marts/
cubes
Business data
sources
TRANSFORM LOAD AGGREGATE TRANSFORM REPORT
?
Did the problem
originate in the
source data?
Was there an issue
with a data load?
?
Did a transformation
job go wrong?
Did a job fail to run or
run too many times?
Were there
issues with the
transformation logic?
?
Is the report pulling
from the right
data mart?
Is there a problem with
the report logic?
Is the report rendering
incorrectly?
REFINE
Reports,
dashboards,
visualizations
12. 12
Manual “stare and compare” is slow
and doesn’t scale.
And is not a great use of your team’s brainpower.
So why isn’t your data
better already?
13. 13
To Trust the
Production
Environment:
ENTERPRISE
DATA WAREHOUSE
DATA LAKE
DATA ANALYTICS / BI
ECOSYSTEM
Information
Steward
Data
Services
Advanced Data
Migration by Syniti
MDG
ENTERPRISE
DATA WAREHOUSE
DATA LAKE
DATA ANALYTICS / BI
ECOSYSTEM
APPLICATION
ECOSYSTEM
You must End
to End Test in
the Test
Environment:
14. 14
It’s time to think differently
about how you maintain
the integrity of your data
15. 15
It’s time to bring the discipline
of end-to-end testing
to the world of data
17. 17
Automated Continuous
A data testing solution that’s…
Includes data, UI, and
API testing across your
landscape.
End-to-end
Enterprise
Data Integrity Testing
18. 18
Catch more data
issues up front
Get higher data
quality
Test at scale
and at speed
So, you can…
Enterprise
Data Integrity Testing
19. 19
Automation — the key to moving from data integrity to decision
integrity
EXTRACT
Enterprise data
warehouse
Data marts/
cubes
TRANSFORM LOAD AGGREGATE TRANSFORM REPORT
REFINE
Reports,
dashboards,
visualizations
PRE-
SCREENING
Metadata checks
Format checks
VITAL
CHECKS
Completeness
Uniqueness
Nullness
Referential integrity
FIELD
TESTS
Aggregation
Value range
Transformation
RECONCILIATION
TESTS
Detailed source to
target comparison tests
ETL validation
REPORT & APP
TESTING
UI automation
Visual checks
Content checks
Security checks
CONTINUOUS MONITORING Row counts, Job run times, Data distribution
20. 20
Keep your automation easy, faster and at scale
with model-based test automation
Make quick
tweaks to either
layer as things
change.
Create Maintain
Auto-
generate
tests.
Auto-
update
tests.
Business logic
Template
2
1 3
4
Separate out what you need to test into layers for
fast, easy creation and maintenance.
Build a flexible
testing model.
Test case
design
sheets
Test
cases
21. 21
8 advancing opportunities
Proven strategies to tackle test automation challenges
Empower any user
to contribute to automation
through a codeless approach
that removes programming
resources.
Test what matters
Aligning testing with business
risks delivers high ROI in
the shortest time and with less
effort.
Cover all testing needs
Seek a comprehensive toolset
that is technology-agnostic
to avoid relying on specialists
as and to expand testing use
cases.
Eliminate maintenace
The less maintenance is
required, the lower your total
costs. Seek resilient, codeless
test automation approaches.
Get on-demand test data
Eliminate wait time and false
postives by enabling testers to
create and access test data
when needed.
Promote collaboration
through reusable test artifacts
that can be plugged into a
central repository for end-to-
end test automation.
Shift left testing
Test at the API layer
and use AI-based technologies
to create UI tests before UIs
are completed.
Simulate environments
Get rid of access fees and
ensure that testing can
proceed even if test
environments are unavailable
or unstable.
SUSTAINABLE
AUTOMATION
RESILIENCY
AND REUSE
SELF-SERVICE
+ SHIFT LEFT
22. 22
How CI/CD with CT works
to continuously test across
your digital enterprise
FIX
QA
Collaborate across teams to
build better tests—end-to-end
Business Data
No-code, low-code solution
No expert programming skills required
AUTOMATE
Tests in parallel
at scale and
speed
Get detailed,
actionable reports.
RUN
requirements
& test case
design
PLAN
CREATE
model-based
tests.
Pinpoint the root
cause of problems
REPORT
We understand the need for enterprise data governance and can help in the process.
This is the umbrella I will talk to with the “pillar” of data quality our focus today…
Changes are constantly happening throughout your IT landscape
One end-to-end business process within that landscape could touch several interconnected systems and technologies
typical enterprise portfolio contains thousands of applications, (2000 – 3000) applications in production
MuleSoft research - Single transaction touches an average of 83 different technologies from mainframes, legacy customs apps to microservices ad cloud native apps.
Next slide:
New and real challenge is not just dealing with application changes, but dealing with environment changes and data changes
Examples of environment could be – as simple as a Microsoft update, or a deployment of a new browser version
Due to the move to the cloud, we now have an extremely short cycle to keep up with those environment and data changes
Three dimensions are happening in parallel: application, environment and data changes = complexity
New and real challenge is not just dealing with application changes, but dealing with environment changes and data changes
Examples of environment could be – as simple as a Microsoft update, or a deployment of a new browser version
Due to the move to the cloud, we now have an extremely short cycle to keep up with those environment and data changes
Three dimensions are happening in parallel: application, environment and data changes = complexity
The global pandemic has accelerated change across the digital landscape (remote work, remote sales, remote everything)
Companies need to go digital
Mayfield CXO survey: 85% of organizations are planning to move to the cloud twice as fast
Application Modernization – related to cloud transformation but could mean many different things:
Migrating your applications to more modern ones in order to deliver more business value or enhance customer/employee experience (for example, shifting Namely and Expensify to Workday – for more scalability)
Application modernization could also mean migrating your legacy applications to scalable, cloud-native app environments – 67% plan to migrate on-premise applications. Example: according to the ASUG Tricentis 2021 survey Future of SAP delivery, 91% plan to move to S4HANA. But SAP ECC is more popular. Organizations say cloud is great, but need to justify the cost and risk involved
Challenges include rapid and regular updates: keeping up with the pace of updates release by SAP, Salesforce, ServiceNow
Other challenges include data: 43% say data migration in cloud migration projects is a huge problem (for SAP – ASUG survey)
It could also mean Digital optimization – delivering new business/digital functionality: 50% of companies said they are planning on moving their eCommerce, marketing and sales to digital platforms in order to improve customer self-service and experience
if you don’t deal with this inevitable change, you will experience consequences
Example 1: Large multinational bank (Barclays – not our customer)– has online banking down, leaving customers locked out of accounts as payments also delayed:
The average cost of downtime is $5,600 per minute (according to Gartner research), So it is estimated this company suffered 7 million dollars in loss, loss of customers, reputational damage
Barclays online banking goes down: https://www.thesun.co.uk/money/12788031/barclays-online-banking-down-2/
Average cost of downtime: https://www.atlassian.com/incident-management/kpis/cost-of-downtime
Why the cost of network downtime is so high in the banking industry: https://www.garlandtechnology.com/blog/why-is-the-cost-of-network-downtime-so-high-in-the-banking-industry
Barclays tops list of banks with most IT shutdowns: https://www.bbc.com/news/business-49412055
Example 2- One of the largest telecommunications providers in Europe (A1 – our customer, before scenario). They were doing manual testing which resulted in more than 10,000 tests performed. Business process was distributed across 60 critical systems and enterprise applications. As a result, they were getting only 3 to 4 major releases a year, delayed innovation. Additionally, if a program is late, it costs money to get people to deliver that program on time.
Example 3: inefficient resource management. World’s most established oil and gas providers – Exxon Mobil (our customer, before scenario). They needed highly specialized people to develop and maintain their testing. This lead to high costs not only in technical IT staff but also high maintenance to keep up with those changes
if you don’t deal with this inevitable change, you will experience consequences
If you're like most enterprise organizations, you've already invested in lots of tools to get and keep your data in good shape. Fantastic, they do a great job at what they do — keep on using them. The issue is that they're designed with a particular task in mind — like governing your master data or profiling data at specific points in time, like after a transformation event. In complex enterprise landscapes where you're dealing with masses of structured and unstructured data from many different systems, these point solutions leave dangerous data management gaps.
These gaps are risk in your data process. I listed some examples of the gaps they often don’t fill.
We must find the problem ad fix it, but where in the process was the failure.
At the end of the day all the testing of the gaps in the data process is a manual effort, we call stare and compare
Points to make:
Manual “stare and compare” methods that involve people spot checking data in source systems and the final data destination
This doesn’t scale or get them the coverage they need
It’s also a real waste of expensive human resources it’s estimated that all knowledge workers in an organization spend 50% of their day, fixing data, when automating this task makes much more sense
TO be clear, to mitigate the risk of changes to the processes you must test ALL the processes End to END BEFORE they are in production. Not just a single point like an ETL change but preform true REGRESSION that ensures the process is golden end to end after any change. Also, you can’t rely on production testing or profiling or monitoring they only check a part of the process and if you are catching problems in production, it is TOO LATE!
Point “testing” solution like Informatica’s IQA, their testing solution, only have coverage on their intersection points. You must look at all the integration points, you can now test all the intersection of the data, and much more that just a “sample” of the data. All the data at all the intersection points.
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[Jeanette]
- Talk Track – To solve this business problem every enterprise has you need an automated, end to end solution, that can run continuously and integrate with a company’s DevOps or DataOps CI/CD structure.
[Jeanette]
- Our major differentiator, is that SAP EDIT (Tosca DI) has unique ability to test across 160 UI and API technologies as well as across the over 250 different data technologies enterprises have deployed across the enterprise. An we do this testing in an automated, scriptless framework we call Model Based Test Automation. Truly our secret sauce for accomplishing the automation at scale.
[Jeanette – handover to Curtis to walk through B of A examples]
- Talk Track – Once we implement it we can catch the problem at the root cause. Finding issues early, identifying the root cause and remediating them is the technical purpose behind all testing. Doing it with automation ensures the coverage needed, and in the end you get higher data quality for better business decisions.
Use this slide to show the value of model-based test automation (and test case design – turn one test model into dozens of tests, easy update them in this one model)