Zurich Insurance is implementing the Alation Data Catalog to improve its data management and empower data scientists. Key points: 1) Zurich's previous metadata system was less user-friendly and did not allow dashboard views or efficient searching of objects. Alation automates metadata ingestion and provides improved search capabilities. 2) Users found Alation easier to use than the legacy system, with features like natural language search, data lineage tracing, and collaborative functions like commenting. 3) Zurich aims to integrate Alation with its data engineering tools to enable easier data discovery and reuse by data scientists. The catalog is a critical part of building a more data-driven organization.

1. Empowering Data Scientists with the Alation Data Catalog
11/6/2019
Ming Yuan
Zurich North America

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INTERNAL USE ONLY
Zurich Insurance Group is a leading multi-line insurer that serves its customers in global and local markets.
With approximately 54,000 employees, Zurich provides a wide range of property and casualty, and life
insurance products and services in more than 210 countries and territories. Zurich’s customers include
individuals, small businesses, and mid-sized and large companies, as well as multinational corporations. The
Group is headquartered in Zurich, Switzerland, where it was founded in 1872. The holding company, Zurich
Insurance Group Ltd (ZURN), is listed on the SIX Swiss Exchange and has a level I American Depositary
Receipt (ZURVY) program, which is traded over-the-counter on OTCQX.
Zurich North America (ZNA), part of the Zurich Insurance group, is one of the largest providers of insurance
solutions and services to businesses and individuals in the North America. Our customers represent industries
ranging from agriculture to construction and include more than 90 percent of the Fortune 500. We’ve
backed the building of some of the most recognizable structures in North America — from the Hoover Dam
to Madison Square Garden to the Confederation Bridge.
Introduction of Zurich Insurance
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 Data warehouse system serves as the single source of truth in the enterprise
 Metadata is collected and stored in Information Governance Catalog system
 Governance processes on data access and utilization are established
 Data is used in day-to-day decision makings in key business domains
 A strong data science team delivers predictive models and business insights
 We are an early adopter of big-data analytics and cloud analytics
Zurich has leveraged data for decades
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Multiple Databases
On-premises Data Warehouse
Hadoop Data Lake
Cloud Data
Lake

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Customer-led
Improve service quality and
customer experience
Innovative
Better products, services and
customer care
Easy to work with
More agile and responsive
organization
Zurich is a data-driven innovative company
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Zurich
Strategy
Data Driven
Business
Impact
Personalized offers
Predictable behaviors
Prescriptive insights
Optimized business performance
Improved operational efficiency
New flexible propositions
Tailored pricing
Real-time service delivery
Drive customer and business impact through Data & Analytics solutions
Data Driven Customer Experience
Generate actionable customer insights to
improve customer experience
Data Driven Automation
Improve data flows and
intelligent processing
Data Driven Products
and Services
Use data insights to create and upgrade
products and services
Data Driven Decision Making
Embed critical business data in decision making processes and systems
Data
Strategy
Core
Pillars

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INTERNAL USE ONLY
What is the focus of Data and Analytics at Zurich?
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We build and deliver new data and insights that help our customers and
Zurich make better decisions and drive business impact
Customer ClaimsUnderwriting
 Share data and insights with our
customers to help our customers better
understand and manage their risk
 Improve outcomes in the claim handling
process – based on potential for severity
 Enhance risk selection, program structure
and pricing decisions
 Support portfolio management decisions
Data Foundation
 Invest in data (internal and external) and enhance accessibility and usability
 Invest in building a more data-enabled organization

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INTERNAL USE ONLY
 Legacy metadata management system is less
user-friendly, not allowing for dashboard view for
objects and search
 Metadata is not fully reviewed and certified as part
of an automated stewardship workflow
 Metadata system doesn’t provide data-utilization
patterns from machine learning
and crowdsourcing
 Data samples and logs not available to
improve usability
 Metadata import not automated within UI, requires
API or manual entry
Finding the right data took a lot of time and effort
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Key features that data stewards look for from a data catalog
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Maintain business glossary for data domains that are owned by function or business unit
Import technical metadata and catalog it as data assets
Curate technical metadata relating them to logical business terms
Maintain data-flows mapping transformations
Specify data quality measures relating to the logical data model
Specify data quality reports & measures to be applied to concrete technical data assets

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Alation automates time-consuming steps in metadata ingestion
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Database
Data
Warehouse
Cloud Data
Lake
JSON
Streams
Ingest and refresh schema, table, and column definitions
Build data lineage, popularity, common queries, and more
Profile and store sample data sets
Collect user information and usage metrics
Open APIs to programmatically import business glossaries
2,053,632

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Key features that data consumers look for from a data catalog
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Search, explore and discover data assets
explore assets by policy level, owner, business terms, metrics, systems, physical structures, etc.
Interpret data with correct meaning and context
find terms, scope, granularity, relations to other data sets, processes & use cases, etc.
Retrieve unique, validated golden record of master- & reference data
look at actual data values available of managed master & reference-data and understand change process
Navigate data flows to analyse processes and assess change impact
visually trace data lineage on detail level or get an overview of data-flows on an system interface level
Understand quality of data by looking at quality rules
analyse how suitable the available information is by seeing what rules it adheres to on various dimensions
Evaluate data quality reports and drive improvement actions
visualize quality status on management and detail level and make improvement actions trackable

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Natural-language search makes it easy to discover unknowns
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English in, answers out.
No need to translate into technical labels, jargon or
coded data
Find tables, files, queries, articles, experts and
BI workbooks in one place
Keep results fresh with scheduled metadata and query
log extractions
Different objects…
Article
Published Query
Table (Description)

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Users and Stewards actively curate Alation Catalog pages
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Endorsements, deprecations,
and warnings propagate
best practices
Sample entries for better
understanding the data
Query sharing to
encourage reuse
Find subject matter experts by
table usage
View popularity rankings
to determine the most
used asset

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Everyone collaborates with Articles and Conversations
Seamlessly communicate
directly in Alation
Reference past conversations
Increase transparency
by tracking Contributors

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Query intelligently with the Compose feature
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Download and examine query
results
Help develop SQL statements

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What did the users say about the tool?
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Functionality Legacy Metadata Tool Points Alation Points
Functional
Fuzzy Search It does not have fuzzy
search.
0 It has fuzzy search 1
Search Algorithm Not very efficient 0 Very efficient 1
Advanced search Advanced search is
available
1 Advanced search is available 1
Data Lineage Very complex 0 Less complex 1
Data Profiling Not readily available 0 Its available 1
Sample Data Not readily available 0 Its available 1
SQL query capability Not available 0 Its available 1
SQL query log access Not available 0 Its available 1
Auto-completion suggestions in SQL Not available 0 Its available with @ 1
Connect to DataIKU Not possible 0 Connected to dataIKU 1
Query on Metadata Its available 1 Not available 0
Search multiple words on single search Its available through query 1 Not available 0
Machine Learning capability Not available 0 Its available 1
REST API Its available 1 Its available 1
Download the search result Its available 1 Not available 0
Add reference and comments with easy
steps
Not available 0 Its available 1
Glossary terms Its available 1 Its available 1
Saving and Publishing the Data Not available 0 Its available 1
How often a particular table or schema
accessed
It is not possible to track 0 It is possible to track 1
Who accessed the data It is not possible to track 0 It is possible to track 1
Metadata and Data side by side Not possible 0 It is possible 1
Non Functional
Collaboration with teams No 0 Yes 1
Need a team for metadata management Separate team needed 0 Users are the contributors as
well.
1
Improve productivity No 0 Yes 1
Search performance Slow 0 Fast 1
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Alation seems very easy to use and was easy
to follow.”
-- Enterprise Data Specialist
“
 Ability to endorse, warn, deprecate almost any item (source,
table, column, query) and redirect users from a deprecated
item to an endorsed item - depth of tool is very impressive
 Has potential to dramatically improve quality of metadata
through crowd-sourcing and maintenance
(see Dependencies)
 Integration with Dataiku - ability to add a table in Alation to a
Dataiku project, either starting from Alation or from Dataiku
 Traceability of most frequent users and popularity of Alation
items
 Use of text analytics for data discovery and smart query
composition
 Ability to publish queries for other users to view and leverage
-- Data Scientist

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Data catalog is a critical component in self-service analytics
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We are integrating Alation with data engineering and data processing tools for data scientists.
Easy imports
Easy searches

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INTERNAL USE ONLY
 Modern data catalogs increase the business value of data and analytics investments
 Listen to the data consumers
 Choose one data catalog that can naturally fit into your data and metadata management
processes
 Automate metadata ingestion and validation processes
 Collaboration, contribution, and self-service enablement are key functions
 Work with a small group of experts to start the adoption
Data catalog fulfills the promise of building data-driven culture
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Search QueryCurate Collaborate Reuse

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INTERNAL USE ONLY
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Thank you
ming.yuan@zurichna.com