The document outlines a fireside chat led by Tony Baer from Ovum Research on data lake governance, emphasizing the importance of managing data lakes effectively. Key points include the necessity for data lakes to be shared resources, the involvement of business users in data curation, and the ongoing development of maturity and readiness in tools and best practices. The reference architecture for data lakes highlights various stages of data processing, challenges, and the significance of collaboration in modern data management.

1. Fireside Chat with Tony Baer, Ovum Research
Developing a Strategy for Data Lake Governance
Wednesday, May 18, 2016
1:00 pm EST

2. Meet today’s speakers
Tony Baer Principle Analyst, Information Management, Ovum
Tony Baer leads Ovum’s Big Data research area. His coverage focuses on how
Big Data must become a first-class citizen in the data center, IT organization, and
the business. He has a multi-disciplinary background touching the different tiers
of enterprise software. He is an author and sought after speaker.
Scott Gidley Vice President of Product, Zaloni
Scott is a nearly 20 year veteran of the data management software and services
market. Prior to joining Zaloni, Scott served as senior director of product
management at SAS and was previously CTO and cofounder of DataFlux
Corporation. Scott received his BS in Computer Science from University of
Pittsburgh.

3. • Award-winning provider of enterprise data lake
management solutions:
Integrated data lake management platform
Self-service data preparation
• Data Lake Design and Implementation Services:
POC, Pilot, Production, Operations, Training
• Data Science Professional Services
Delivering on the business of big data
Funded by top-tier technology
investors:

4. Key Findings
• Data lakes must be managed
• Data lakes must have the capability to ingest all data &
related metadata
• Data lakes will only succeed if they become shared
resources
• Business users must be prepared to take responsibility
for curating data.
• Maturity & readiness of tools, technologies & best
practices are works in progress
• Mgmt. & governance of data lakes should be a phased
process
Ovum Big Data Report:
Developing a Strategy for Data Lake Governance

5. Group Multi-department Enterprise
Log analytics
Sentiment Analysis
DW offload
Data Lake
Exploratory Analytics
Line of business analytic applications
Operational analytics
Data lake is later stage of Hadoop adoption

6. IT Data Scientists Business
Bulk storage of raw data
Exploratory Analytics
Line of business
analytic applications
Operational analytics
Migrate I/O-intensive operations (e.g., ELT)
“Deep” analytics
(e.g. segmentation, predictive, prescriptive modeling)
Data lake use case maturity model

7. Availability/Reliability
(FT,HA,BackupDR)
Monitoring&troubleshooting
Perimeter
Security
Data platform (Hadoop)
Query/Analytics tools, programs
Cost Optimization & Integration
Data Inventory
Data Curation
Data-level security
Self-service
tier
Data Lake building block
Hadoop platform management
End user tool
Ovum’s data lake reference architecture

8. Data lake challenges and complications
• Ingestion
• Lack of Visibility
• Privacy and Compliance
• Quality Issues
• Reliance on IT
• Reusability
• Rate of Change
• Skills Gap
• Complexity
Building: Managing: Delivering:
Zaloni Confidential and Proprietary8
Engage the business
• Discover
• Enrich
• Provision
Govern the data in the lake
• Cleanse
• Secure
• Operationalize
Enable the data lake
• Ingest
• Organize
• Catalog

9. Data Curation
Build your library of
information
Physical Inventory
Know/manage what data is in
the data lake
Data profiling, data preparation,
collaborative data enrichment,
catalog, match data, derive master
data, record data lineage
Business & Analytics teams Technology team
Manage data access, track
data lineage, tag for security,
data retention
Manage data access, tag for
security, data retention, lifecycle &
workflow, track data lineage
Collaboration key to modern data management

10. Data lake reference architecture
Consumption
Zone
Source
System
File Data
DB Data
ETL Extracts
Streaming
Transient
Loading Zone
Raw Data
Refined
Data
Trusted
Data
Discovery
Sandbox
Original unaltered
data attributes
Tokenized Data
APIs
Reference Data Master Data
Data Wrangling
Data Discovery
Exploratory Analytics
Metadata Data Quality Data Catalog Security
Data Lake
Integrate to
common format
Data Validation
Data Cleansing
Aggregations
OLTP or ODS
Enterprise Data
Warehouse
Logs
(or other unstructured
data)
Cloud Services
Business Analysts
Researchers
Data Scientists
Zaloni Proprietary
10

11. DON’T GO IN THE DATA
LAKE WITHOUT US
Zaloni Proprietary