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Big data @ magyar telekom

Budapest Data 2016

Gergely vItárIus

CLASSIC BI DEVELOPMENT & OPERATIONS

NEXT GENERATION BI TOOLS BIG DATA TECHNOLOGIES

ADVANCED / REALTIME ANALYTICS

BI innovation center HEAD OF BI INNOVATION CENTER

IT DIRECTORATE MAGYAR TELEKOM PLC.

Big data fever (2013/14)

Event based churn (teradata / marketing)

Big data fever (2013/14)

Event based churn (teradata / marketing)

Social media command center (hadoop / T-SYSTEMS)

Big data fever (2013/14)

Event based churn (teradata / marketing)

Location based ads (EMC / product house)

Social media command center (hadoop / T-SYSTEMS)

Big data fever (2013/14)

Event based churn (teradata / marketing)

Location based ads (EMC / product house)

Social media command center (hadoop / T-SYSTEMS)

DPI reporting (CLOUDERA hadoop / IT)

Big data fever (2013/14)

DPI REPORTING PILOT (2014)

9

DPI REPORTING PILOT (2014)

4bn records per day (1,5 TB raw data)

10

DPI REPORTING PILOT (2014)

Original report: once a year, 3 months delay

4bn records per day (1,5 TB raw data)

11

DPI REPORTING PILOT (2014)

Original report: once a year, 3 months delay

Classic DWH trial: 1 day of data aggregated in 3 days

4bn records per day (1,5 TB raw data)

12

DPI REPORTING PILOT (2014)

Original report: once a year, 3 months delay

Classic DWH trial: 1 day of data aggregated in 3 days

4bn records per day (1,5 TB raw data)

Hadoop trial: 1 day of data aggregated in 15 secs

What is important?

What is important?

„BIG DAtA analytics is the future of our company”

„FIrst FIX tHE bASICS, BIG DATA and other fancy things can come after that”

What is important?

„BIG DAtA analytics is the future of our company”

„FIrst FIX tHE bASICS, BIG DATA and other fancy things can come after that”

What is important?

„BIG DAtA analytics is the future of our company”

„I have to wait too much for data, i need self service tools”

FUTURE architecture

Self-service framework

Analytical excellence

Data monetization

BI governance

Bingo structure

Future architecture

Data warehouse platform upgrade

SAS environment upgrade

Introduce etl tool

Big data infrastructure

Data quality monitoring

Bi governance

Agile delivery model

Bi governance framework

data governance

Household data model

21

Self-service bi

Business sandbox model

Data discovery tool

Automation framework

Unified Bi portal

governance security privacy

Key challenges

Hadoop in action?

Build data lake

Hadoop in action?

Build data lake Build use cases

Hadoop in action?

Example use case: network customer experience

takeaways

One Data Lake is

essential Develop classic

BI and next generation BI simultaneously

Holistic view of

Big Data is a must for success

Business

should be driving

Thank you

Gergely Vitárius Head of BI Innovation Center vitarius.gergely@telekom.hu