Collibra Partnership

1. Partnership
Overview

2. Agenda
O1 Collibra Implementation
O2 Ataccama Implementation
O3 Joint Product Sheet
O4 Joint Clients

3. 3 | ©Collibra 2019
©2019 Collibra Inc
Maximize the value of your data
Introduction to Collibra
April 2019

4. 4 | ©Collibra 2019
Solving today’s biggest data problems requires a new approach
Impact
Macro changes
Proliferation of Data Sources
Explosion of Data Volume
Data at the center of
Digital Transformation
Continued increase in
Data Regulation
Analysts waste 70% of their time finding the
right data
Only 10% describe their company as being
open about sharing data1
Financial regulations: BCBS, CCAR,…
Data Privacy regulations: GDPR, CCPA,…
Data Democratization
Companies need help migrating data from
on-premise to the cloud
50% of CEOs are concerned about the
integrity of the data on which they base
their decisions2
1 Research Report: Analytics as a Source of Business Innovation, MIT Sloan Management Review, 2017
2 CEO Outlook, KPMG 2017

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Data needs a System of Record
CDO CRO/CMO
AI/ML
Analytics
Data
Science
Big Data
Cloud
Data
Management
CIO
Gartner: “by 2021, the office of the CDO will be a mission-critical function comparable to IT,
business operations, HR and finance in 75% of large enterprises.”

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Collibra – The system of record for the data team

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Recognized by industry analysts
Forrester Wave:
Machine Learning Data Catalogs
Gartner Magic Quadrant:
Metadata Management
Forrester Wave:
Data Governance Stewardship

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Illustration: Time wasted finding data
The below example illustrates an email chain that was needed to align on the definition of a metric.
This one of many examples the business has given to help quantify the need for certification.
Example: 15 emails involving 12 people over nearly 31 hours
11:18am 2:08pm 2:40pm 3:52pm 8:48pm 10:59am 1:37pm 6:02pm
2:03pm 2:29pm 2:52pm 4:54pm 8:25am 11:27am 5:30pm
Business
Lead
Analyst Analyst
Business
Lead
Analyst Analyst Analyst Analyst
Analyst
Business
Lead
Business
Lead
Business
Lead
Business
Lead
Analyst
Business
Lead
“How do we define
Customer Lifetime
Value Metric?”
“Here is how we
define Customer
Lifetime Value”
“A Customer is
someone we have
done business with”
“In Finance Customer
Lifetime Value is
calculated based on
who creates the
report”
“There is a new focus
in creating definitions
through the Data
Governance Council”
“There is no single
source of truth, here’s
a recommendation for
defining the Customer
Lifetime Value Metric”
Forward to an analyst
“Analyst provides a
calculation for
Customer Lifetime
Value in
Email 1”
“Concurrence on the
recommendation in
email 6”
“Reiterating the need
for a ‘certified’
definition of the
calculation”
Business leads
coordinating
“Explanation on where
the business is with
defining how customer
lifetime value should
be calculated”
Business lead
contacts Enterprise
Data Governance
Office
“Detailed explanation
of the definition of
customer lifetime
value calculation”
“Align this to the Data
Governance POC”

9. 10 | ©Collibra 2019
Customer Story
Headquartered in San Jose, California, Adobe is one of the largest
software companies in the world with over 18,000 employees and
revenue of approximately $7.3 billion in fiscal 2017. The company’s
mission is to give everyone – from emerging artists to global brands –
everything they need to design and deliver exceptional digital
experiences.
Company
Adobe wanted to strengthen the organization’s data culture by making
definitions of terms that are part of the vocabulary used for discussing
data – housed in Collibra’s Business Glossary – available to everyone
while they were working in other solutions.
Goal
Seamless access to definitions for business and data terms has helped
to drive a deeper understanding of data across Adobe. Today, Adobe
has 50 to 100 unique Collibra users per day via this channel. As of the
summer of 2018, more than 4,000 unique users had come into Collibra
to get information, which is about 22% of Adobe’s employee population.
Results
Creating a seamless way for Adobe employees to view definitions of
terms, and have the information from Collibra readily available in other
solutions such as Tableau was key. Accomplishing this allowed the
organization to open up new approaches to understanding data and
unlock the power of the company’s data to every employee.
Strategy
Adobe
“The integration of Collibra with our business intelligence
and other tools has helped us foster understanding in our
organization by bringing data governance to our users.
We are excited about the next phase of our data
governance journey and see many more possibilities
ahead.”
Ryan Cook
Senior Business Intelligence Developer, Adobe

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Collibra’s data governance and catalog solutions give
teams powerful tools that make it easy to consume
data across the enterprise.
Our flexible and configurable solution puts people
and processes first – empowering everyone to
maximize the value of their data.

11. Data Quality

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Known Data
Issues
Suspected
Data Issues
Unexpected
Data Issues
 Risk manageable
 Business rules tractable
 Expectations clear
 High business user involvement
 Risk unmanageable
 Business rules unknowable
 Missed expectations
 Little business user involvement
Profiling: Lowering The Waterline

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Data Quality Metric roll-up in Collibra

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Rules and Data Quality Metrics in Traceability Diagram

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Metadata, Data Quality & MDM Framework
DATA
MANAGEMENT
core
DEFINE &
MANAGE
TERMS
DEFINE
DQ RULES
PREVENT
BAD DQ
Define and manage
Reports Catalogue
Resolve
issues
AUTO-
MATE
Monitor DQ
over time,
manage trends
Prevent bad data
to enter systems
Discover
data issues
quickly
Define
DQ Criteria
DATA
MANAGEMENT
extend
Data mappings and lineage
Glossary terms in reports
Integrated DQ issue
management
Automated data cleansing
and enrichment
DQ Firewall
Define and edit
Business terms
collaboratively
Share and
collaborate
Manage reference
codebooks
and dictionaries
Create DQ
dashboard
MONITOR
DATA
QUALITY
RESOLVE
DQ
ISSUES
MANAGE
REFERENCE
DATA
GOVERN
REPORTS
Big Data Platform

16. Ataccama
Implementation

17. ATACCAMA FAST FACTS
350+
CUSTOMERS
GARTNER
RECOGNITION
AI-POWERED
DATA CURATION PLATFORM
350+ Global Customers
55,000+ Freemium users
Both DQ and MDM Gartner
Magic Quadrant
Data Discovery & Profiling
Data Quality & Governance
Master Data Management
G

18. ATACCAMA ONE | Platform Components
Data Discovery
& Profiling
Data Quality
Management
Master Data
Management
Big Data
Processing &
Data
Integration

19. SaaS
Hadoop
On Premise
Cloud
ROBUST DATA PROCESSING ENGINE
Any Data / Any Domain – Integration – Performance –
Scalability
ENTERPRISE-PROVEN CAPABILITIES
High Availability – Auditing – Identity Management –
Data Lineage
COLLABORATIVE DATA STEWARDSHIP UI
AI & Machine Learning – Self-Service – Collaboration
– UX/UI
›
ATACCAMA ONE | Platform Features

20. ATACCAMA ONE | High Level Architecture
BUSINESS
VALUE
VALUE
CREATION
DATA
CURATION
DATA
DELIVERY
DATA
ACQUISITION
Process
Improvement
New Campaign
Regulatory
New Product
New App
Analytics
SMART
ALGORITHMS
DATA
STEWARDS
›
› ›
CURATED DATA
CATALOG
DATA APIs
METADATA APIs
Provide
Discover | Profile | Catalog | Cleanse
Govern | Consolidate | Master

21. Our collaboration – high level summar
› Our integration allows for metadata produced by Ataccama ONE DQM/MDM platform offerings to be
cataloged by the Collibra Data Catalog platform
› Collibra’s Data Catalog will define and describe business rules
› Ataccama ONE will execute business rules and feed back through to Collibra for client facing visualization
and logging.

22. Ataccama Data Quality Engine
Framework application originally designed to solve various DQ use cases.
Allows user to create custom ETL-based jobs via Eclipse based GUI.
Contains built-in application server and workflow allowing scheduling and triggering jobs via HTTP.
All jobs may be deployed as an online service.
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23. How is it implemented?
Collibra Reader
Utilizing Collibra Rest Api v2 to read Assets of a
certain type.
It can read meta-information of an Asset as well as its
Attributes and Relations.
Each Asset is represented by one row in the DQE
processing.
Collibra Writer
Utilizing Collibra Import api to create or update Assets
in Collibra.
It creates Collibra import jobs from rows on the step
input.
It can write Assets, Attributes and Relations.
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24. Why Data Quality Engine?
PROS
Already contains support for HTTP calls.
It is easily extendable.
It serves as an integration tool or backend processor
for all Ataccama installations.
It can work without any front-end.
CONS
The configuration is not business friendly.
Very complex use-cases might be hard to implement.
›
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25. DQ Issue
Tracker
DQC Engine
DQ
Dashboard
Exports
Retrieve data
for DQ processing
1
Summarized DQ Results
pulled by DGC
2
3 Summary
DQ Reports
4 DQ Issues for Manual
Resolution
5 Data Corrections & Extensions
of the DQ rules
Cleansed & Merged data
(exports)
6
Collibra DGC
Reference
Data Manager
7
Why Data Quality Engine?

26. DQM Use Case
DQC Engine Collibra DGC
Ataccama
DQM
Retrieving metadata
From Collibra. 1
3 Sending the DQM
Results back to Collibra.
2
Retrieving Data
for DQM processing.

27. Issue Tracking Use Use
DQC Engine Collibra DGC
Send Recorded
DQ Issues for resolution. 1
2 Update the resolution
status
DQ Issue
Tracker

28. Live Ataccama
Demo

29. Joint Product
Sheet

31. 33 | ©Collibra 2019
Collibra Ataccama
Contacts
• Technical / Sales
Bas van Reeuwijk
Bas.vanReeuwijk@Collibra.com
#tech-partnerships
• Marketing
Margaret Guarino
Margaret.Guarino@Collibra.com
• Sales
Nick Stammers (EMEA)
Nick.Stammers@Ataccama.com
• Drew Stark (NA)
Drew.Stark@Ataccama.com
• Technical
Pavel Franek
Pavel.Franek@Ataccama.com
• Marketing
Pamela Valerio
Pamela.Valerio@Ataccama.com