The document summarizes the findings of a survey conducted with 300 business technology professionals about business glossaries and automation in business intelligence. Key findings include that over two-thirds of respondents listed implementing a business glossary as one of their top challenges and that teams currently spend many hours per week manually tracing data flows and conducting impact analyses when changes are made. The document advocates that an automated business glossary integrated with metadata and data tools could help overcome these challenges by automatically generating, refreshing, and providing insights into organizational data assets and flows.
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Slides: The Automated Business Glossary
1.
2. www.octopai.com
The Survey: Business Glossary Automation
• We conducted a survey with Dataversity in Jan-Feb 2020
• ≈ 300 business technology professionals
• Across different vertical industries
• GOAL: to learn about respondents’ understanding of business
glossaries and the role automation in business intelligence operations
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How are users defining a
“business glossary”?
1. All the metadata in reporting tools
2. Same as a data dictionary
3. Same as a data catalog
4. I don’t really know
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What’s important for you
in a business glossary?
1. Out-of-the-box product
2. Fewer/no resources required for implementation
3. Updated automatically
4. Integrated with other capabilities (i.e. Lineage)
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What’s keeping you from
having a business glossary?
1. Requires too many internal resources
2. Too costly
3. Unclear expectations about what it should accomplish
4. Not automated
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68% of respondents said
implementation of business glossary is
one of the top 3 use cases challenging
their BI & Analytics team
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The Daily Struggle: Locating the Source of an Error
Question: If incorrect data is discovered in a report, how long, on
average, would it take you or your team to find the source of the error?
A few
minutes
A few
hours
A few
days
A few
weeks
Don’t
know
Other
(please
specify)
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1/3 of respondents
say it can take hours
and over 1/4 go so
far as days
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The Daily Struggle: Tracing data flows between
reporting tools and source systems
Fewer
than 5
Between
5-10
Between
10-15
More than
15
Don’t
know
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
Question: How many hours per week on average do you spend on tracing how data
flows between reporting tools and source systems (impact analysis, root cause analysis)?
More than 1/3 of respondents
report they spend between 5 - 15+
hours on impact and root cause
analysis due to manual work
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The Daily Struggle: Impact Analysis Ahead of a Change
A few
minutes
A few
hours
A few
days
A few
weeks
More
than a
few
weeks
Don’t
know
Other
(please
specify)
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
Question: If you need to make a change to a field in the environment (ETL or database),
approximately how long would it take you or your team to conduct impact analysis and find
all reports and processes throughout your entire environment impacted?
45% report they would
spend anywhere from a few
days to a few weeks to find
the impact of a change
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Top 2 Capabilities to Deal With BI Use Cases
Over 200 respondents said: Data Lineage
Over 200 respondents said: Data Discovery
“One business glossary integrated with all data access layers and all data access
tools (from raw SQL to standard canned reports to ad-hoc query tools -
including custom-built in-house ones - to BI and visualization”
17. www.octopai.com
Data Teams Want Need Automation
Question: To what extent do you think that automation of metadata operations (data lineage,
data discovery, automated business glossary) can be important to your team’s success?
Why?
They spend so much time on daily operations
like impact analysis ahead of a change to a field,
or locating the source of an error in a report
What’s Even Worse?
• These use cases occur 40-400 times per
month in an average organization
• Multiple BI departments in larger
organizations; every person on each BI team
has to spend days or weeks on each task
18. What’s an automated business
glossary and why is it critical for
BI intelligence?
19. www.octopai.com
The Automated Business Glossary
• Overcome the implementation
barrier - generated on your own
metadata, automatically!
• Glossary environment must
represent the actual environment
otherwise its obsolete – automatic
refresh and syncing
• Real collaboration, no more
separating the concepts, no more
separating the tools
• Make self-service BI work better
20. DB Sources
Marketing
CRM
ERP
Finance
HR
Other Sources
ETL Tools
Informatica
DataStage (IBM)
SSIS (MS)
Others
(ODI, Talend, etc.)
Data Warehouse
Oracle
Others
(Hadoop, etc.)
SQL Server(MS)
Teradata
Vertica HP
Reporting & Analysis
Tools
Cognos (IBM)
BO (SAP)
Power BI
Tableau
OBIEE (Oracle)
Others (Qlik, etc)
SSAS-OLAP,
TABULAR
Complete data lineage to and from reports
OCTOPAI AUTOMATED BI INTELLIGENCE PLATFORM
It’s All On The Cloud
21. www.octopai.com
Use Case 1: Insurance Company
The Business Request:
“I need to be able to trust the data that I see because I need to
make quick decisions in this changing environment.”
The BI Challenge:
“How do I find the exact ETL process and database tables that
collectively land the data in this particular report?”
22. www.octopai.com
Use Case 2: Healthcare Company
The Business Request:
“I need more data so I can make more accurate decisions during
this uncertain time.”
The BI Challenge:
“How do I understand the impact of all the changes when I enrich
an ETL process?”
23. www.octopai.com
Use Case 3: Manufacturing Company
The Business Request:
“I need to understand what this specific data element means”
The BI Challenge:
“Where can I find the definition of that specific data element?”
24. Get in touch for a free trial!
amnond@octopai.com
www.octopai.com