Partnership Overview Agenda O 1 Collibra Implementation O

Partnership Overview

Agenda O 1 Collibra Implementation O 2 Ataccama Implementation O 3 Joint Product Sheet O 4 Joint Clients

Maximize the value of your data Introduction to Collibra © 2019 Collibra Inc 2019

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

Data needs a System of Record CIO CDO CRO/CMO Analytics Data Science AI/ML Data Management Big Data Cloud 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. ” 5 | ©Collibra 2019

Collibra – The system of record for the data team 6 | ©Collibra 2019

Recognized by industry analysts Forrester Wave: Gartner Magic Quadrant: Forrester Wave: Machine Learning Data Catalogs Metadata Management Data Governance Stewardship 8 | ©Collibra 2019

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: 18 am 2: 08 pm 2: 03 pm 2: 40 pm 2: 29 pm 3: 52 pm 2: 52 pm 8: 48 pm 10: 59 am 4: 54 pm 1: 37 pm 8: 25 am 6: 02 pm 11: 27 am 5: 30 pm “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” Business Lead Analyst Analyst “Concurrence on the recommendation in email 6” “Reiterating the need for a ‘certified’ definition of the calculation” Analyst Business Lead 9 | ©Collibra 2019 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” Business Lead Analyst Business Lead

Adobe Customer Story Company Results 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. 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. Goal 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. Strategy 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. 10 | ©Collibra 2019 “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

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 | ©Collibra 2019

Data Quality

Profiling: Lowering The Waterline Known Data Issues § Risk manageable § Business rules tractable § Expectations clear § High business user involvement Suspected Data Issues Unexpected Data Issues 13 | ©Collibra 2019 § Risk unmanageable § Business rules unknowable § Missed expectations § Little business user involvement

Data Quality Metric roll-up in Collibra 14 | ©Collibra 2019

Rules and Data Quality Metrics in Traceability Diagram 15 | ©Collibra 2019

Metadata, Data Quality & MDM Framework DATA MANAGEMEN T extend Data mappings and lineage Glossary terms in reports Integrated DQ issue management GOVERN REPORTS DEFINE & MANAGE TERMS Big Data Platform 16 | ©Collibra 2019 Define DQ Criteria Define and manage Reports Catalogue Define and edit Business terms collaboratively DATA MANAGEMEN T core Prevent bad data to enter systems Manage reference codebooks and dictionaries Automated data cleansing and enrichment DQ Firewall Share and collaborate PREVENT BAD DQ DEFINE DQ RULES Discover data issues quickly Create DQ dashboard Monitor DQ over time, manage trends Resolve issues MANAGE AUTOREFERENCE MATE DATA MONITO RDATA QUALITY RESOLVE DQ ISSUES

Ataccama Implementation

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

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

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

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

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.

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.

How is it implemented? Collibra Reader › Utilizing Collibra Rest Api v 2 to read Assets of a certain type. Collibra Writer ›Utilizing Collibra Import api to create or update Assets in Collibra. › It can read meta-information of an Asset ›It creates Collibra import jobs from rows on as well as its Attributes and Relations. the step input. › Each Asset is represented by one row in ›It can write Assets, Attributes and Relations. the DQE processing.

Why Data Quality Engine? PROS CONS › Already contains support for HTTP calls. › The configuration is not business friendly. › It is easily extendable. › It serves as an integration tool or backend processor for all Ataccama installations. › It can work without any front-end. › Very complex use-cases might be hard to implement.

Why Data Quality Engine? 6 Summarized DQ Results pulled by DGC Collibra DGC Reference Data Manager 2 Cleansed & Merged data (exports) 7 1 Exports 3 Summary DQ Reports Retrieve data for DQ processing DQ Dashboard DQC Engine 4 DQ Issues for Manual Resolution DQ Issue Tracker 5 Data Corrections & Extensions of the DQ rules

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

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

Live Ataccama Demo

Joint Product Sheet

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