In this session, learn how to create an HR data lake on AWS to develop a more wholistic view of people data to enable secure, self-service reporting for HR business partners and managers, and to use more advanced data science tools to unlock new insights to reduce retention, enhance hiring practices, and improve employee productivity. We provide examples of HR insights and the business value they can drive, walk through a reference architecture example for an HR data lake, and outline key steps and best practices as you design and launch your HR data lake project. AWS services addressed in this session include AWS Lambda, Amazon S3, AWS Glue, and Amazon Athena.

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How to Build HR Data Lakes on
AWS to Unlock New Business
Insights
Robin Medlin-Braat
WW Business Development Manager, Enterprise Solutions
Amazon Web Services (AWS)
D A T 3 6 7

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Agenda
• HR analytics opportunities, challenges, and use cases
• HR data lake reference architecture
• AWS resources to help you accelerate your journey

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The promise of HR analytics

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HR analytics challenges

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Common HR analytics focus areas
People Results
Process
Programs Performance

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Example use cases – Talent acquisition
Dashboards for:
• Time to fill
• Employee acquisition cost
• Employee ongoing cost
• Candidates/hires by source
Analyze downstream results:
• New hire retention
• New hire performance
• Top candidate sources
Top performer capability profile
Capabilities with greatest impact on customer
experience impact
• Identify and learn from outliers
• Reduce time users spend on spreadsheets
BenefitsExample use cases
• Improve ROI from recruiting
• Accelerate recruiting cycle time
• Hire more top performers
• Accelerate revenue growth
• Improve customer experience
• Improve employee productivity

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Example use cases – Retention
Dashboards for:
• Voluntary resignations
• Cost to replace
• Time to replace
Identify and assess factors driving attrition:
• Common features and experiences of departing
employees
• Programs which have the greatest impact on
retention
Proactively identify and recommend action
for “at-risk” key employees
• Ability to compare peer groups
• Ability identify and learn from outliers
• Democratizing standard analytics for
users
BenefitsExample use cases
• Improve ROI on programs
• Reduce replacement cost
• Reduce business risk

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Characteristics of a data lake
Future
proof
Flexible
access
Dive in
anywhere
Collect
anything

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Important components of a data lake
Catalog
& search
Protect
& secure
Access &
user interface Ingest & store

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Data lake benefits

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Your data sources
AWS Glue ETL
AMAZON
QUICKSIGHT
HR data lake on Amazon Simple Storage Service
(Amazon S3)

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Job authoringData Catalog Job execution
Apache Hive Metastore compatible
Integrated with AWS services
Automatic crawling
Discover
Autogenerates ETL code
Python and Apache Spark
Edit, debug, and share
Develop
Serverless execution
Flexible scheduling
Monitoring and alerting
Deploy
AWS Glue components

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What is the AWS Glue Data Catalog?
• Unified metadata repository across relational databases, Amazon RDS,
Amazon Redshift, and Amazon S3 … with support for more coming soon!
• Get a single view into your data, no matter where it is stored
• Automatically classify your data in one central list that is searchable
• Track data evolution using schema versioning
• Query your data using Amazon Athena or Amazon Redshift Spectrum
• Apache Hive Metastore compatible; can be used as an external metastore for
applications running on Amazon EMR

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How to query data on Amazon S3?

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Amazon Redshift Spectrum

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Build event-driven ETL pipelines

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AWS data lake best practices
• Configure your data lake to be flexible and scalable
• Leverage managed services for multiple methods of data ingestion and analysis
• Empower users with of BI and analytics tools best suited to their needs
• Implement granular access control policies and data security mechanisms
• Catalog all your data

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AWS data lake acceleration resources
• AWS Data Lake Solution
• Partner Data Lake Solutions
• AWS Professional Services

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Key steps to start your HR analytics journey
• Take an agile experimental approach.
• Start small. Focus on a specific challenge to drive quick results.
• Surface and test known hypotheses early.
Please contact medlr@amazon.com if you are actively working on an
HR analytics initiative and would like to have a more in-depth
discussion on your specific priorities and needs.

24. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Robin Medlin-Braat
medlr@amazon.com

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