Presented at Bitkom AK Big Data & Advanced Analytics strategy workshop on 30 June 2021. We point to scalability across data schemata as a major current bottleneck on the road towards building a data-driven organization, and illustrate on the example of Analyst-2 (https://analyst-2.ai/) how Autonomous Analytics may provide a way forward.

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www.inspirient.com
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@inspirient
Scalability and
Autonomous Analytics
Dr. Georg Wittenburg ▪ Co-founder / CEO Inspirient GmbH
Bitkom AK Big Data & Advanced Analytics
Strategieworkshop ▪ 30 June 2021
Winner BNP Paribas
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Startup Award 2016
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Top 10 NATO ACT
Innovation Challenge 2018
Top 10 Deloitte
RegTech Universe 2019
Selected for 2021
AI German Landscape
Trusted AI Startup 2020
European AI Startup Landscape

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Big Surprise: Data Keeps Growing
…but focus on data volume alone does not capture all business challenges
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Quick Example
The Cost of Analyzing a Data Lake
• Assume a company’s Data Lake contains
10,000 datasets
• A data analyst may need three days on
average for one initial exploratory analysis
– One analyst can explore 68 datasets per
year (255 work days with 20% overhead)
• A data analyst earns €60K p.a.
Exploratory analysis of the Data Lake
takes ~147 person years and cost >€8.8M
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How Much of a Data Lake Will Ever
Be Analyzed?
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Bottleneck Is Schema Scalability
Recap: A data schema defines the structure of a dataset or database
Scalability issues in Big Data / Analytics
• Data volume
• Bandwidth (transactions per unit time)
• Explicit algorithms
• Trained classifiers (ML / AI)
• Data structure / schemata / semantics
State of the Art
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Autonomous Analytics
Also known as the “Self-driving Car of Data Science”
… to autonomously generated, relevant results
From diverse dataset…
5
Autonomous
Analytics
30.06.2021

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Introducing Analyst-2
Analyst-2 crawls data repositories and autonomously analyzes structured datasets
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https://analyst-2.ai/
https://data.europa.eu/

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• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
State of the Art and Way Forward
An informal assessment, focused on structured data
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current 2025 2030

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Join the Discussion!
Implications of Autonomous Analytics
• Data quality can be addressed a posteriori
• Difference between internal and external
data will fade away
• Inexpensive valuation of datasets is feasible
• We barely scratched the surface!
Our questions for the community:
• Which common analytical methods need
automating; where are the limits?
• How will the role of the analyst evolve?
• Who would like to try this on their Data Lake /
SharePoint / Cloud storage / … ?
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Request our white paper
at contact@inspirient.com!

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Dr. Georg Wittenburg
Phone: +49 30 2007 4820
Email: georg.wittenburg@inspirient.com
Web: www.inspirient.com
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11. Appendix
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Deep-dive // Demo
Autonomous Analytics in Action
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Analyses of
public datasets
at analyst-2.ai
30.06.2021

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