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Big Data Tomas. Lancinger@bighub.cz Ukázaná platí!
2
BIGHUB FROM DATA TO AHA!
Focused on Artificial Intelligence, Big Data and Information Management.
Based in Prague, Czech Republic
Formed in 2016
teambackground
Data analytics practitioners who built data warehouses, big data platforms and artificial intelligence-based solutions with a real business impact.
TEAM MATTERS
Graduated from
60%Faculty of Nuclear Physics
20%Economics
20%Other (Law, ...)
SECTORS
CLIENTS
SKILLS• Information Management• Data Warehousing• Big Data technologies & Hadoop• Data Quality & Master Data Management• Data Governance & Security
• Advanced Analytics• Deep learning• Operation research• Image and signal processing• Graph analysis• Predictive modelling• Text-analytics
• Data Exploration and Visualization
• Advanced visualizations
• IoT• Near real-time analytics
• Financebanking, insurance
• Telcomobile, internet
• Retailsupermarkets
• Utilitiesgas, electricity
• Logisiticstransportation, post
• Mediaads, all channels
International client-base, often within world-wide holdings - eg. largest US investment broker
SOLUTIONSpropensity models, churn models, supply chain analyses, customer segmentation, workforce optimization, near realtimeanalysis, big data platform implementations, recommendation systems, ...
Cases!
Financial Sector
Maximize Client Understanding§ Microsegmentation§ Behavioral patterns§ Transaction networking§ Text processing§ Web analytics§ Act just in time§ Client retention§ ...
Big Data role§ Restore 15y+ history from tapes back to online!
No!You don‘t haveto choosebetween ...
Big Data role§ Restore 15y+ history from tapes back to online!
§ Massive data enrichment
Ratings
Persons
Connections Ownership
Businessline
@Contacts
Financialindicators
$
AnyCorporation
Big Data role§ Restore 15y+ history from tapes back to online!
§ Massive data enrichment
§ Customer-Supplier analysis
Big Data role§ Restore 15y+ history from tapes back to online!
§ Massive data enrichment
§ Customer-Supplier analysis
§ Life-Style segmentation
Big Data role§ Restore 15y+ history from tapes back to online!
§ Massive data enrichment
§ Customer-Supplier analysis
§ Life-Style segmentation
§ Branch optimization
Big Data role§ Restore 15y+ history from tapes back to online!
§ Massive data enrichment
§ Customer-Supplier analysis
§ Life-Style segmentation
§ Branch optimization
§ Corporate analytics
Prod
ucts
Prof
ile
Unknown Company
Deep
Lea
rnin
g
Company 1 Company 2 Company 3 Company .. Company N
Artificial Intelligence trained to suggest optimal Product portfolio for a completely unknown customer just based on public registers.
Energy Sector
MaximizeOperational Efficiency§ Workforce Management§ Regional segments reconsiderations§ Dispatching optimization§ SCADA analytics§ Early Warning Systems§ ...
Big Data role§ Data liberalization!
Big Data role§ Data liberalization§ Big Data Platform in Cloud
Big Data role§ Data liberalization§ Big Data Platform in Cloud§ Workforce Management
Dispatching
Transport
Repair
Grid
Service Requests
Stack Dispatcher
Inspector
Planner
LeadInspector
Plan
Accidents, Failures
Asset Assessment
External requests
Automated
(re)planning
Automated
task assignment
Route optimization
Monitoring
Predictive maintenance
Big Data role§ Data liberalization§ Big Data Platform in Cloud§ Workforce Management§ SCADA analytics
Obstacles ...
Typical issues§ Immunity to change
§ technology, processes, architecture patterns, standards
Typical issues§ Immunity to change
§ technology, processes, architecture patterns, standards§ Business hesitations / Low trust
Typical issues§ Immunity to change
§ technology, processes, architecture patterns, standards§ Business hesitations / Low trust§ Experts missing
Typical issues§ Immunity to change
§ technology, processes, architecture patterns, standards§ Business hesitations / Low trust§ Experts missing§ „Last mile“ problem
§ locked data§ visualizations – rigid systems§ CRM releases
Google Trends
SanFrancisco
SanFrancisco
China
CzechRepublic
![[ Michelle Kolbe] 12c Partitioning for Data Warehouses OOW 2014 12c Partitioning for Data Warehouses OOW 2014.](https://static.documents.pub/doc/80x56/56649cc55503460f9498fa5b/-michelle-kolbe-12c-partitioning-for-data-warehouses-oow-2014-12c-partitioning.jpg)
