BIG DATA AT MAGYAR TELEKOM. Márton Kelemen Head of BI Innovation Center

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Magdalene Atkinson
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1 BIG DATA AT MAGYAR TELEKOM Márton Kelemen Head of BI Innovation Center

2 Content/agenda 01 BIG DATA JOURNEY 02 WHERE ARE WE TODAY? 03 BIG DATA ENABLEMENTS 04 EXAMPLE USE CASES

3 Big Data Journey

Scope: Big Data Analytics Self Service/Data Discovery Data

4 Strategic program 2014 strategy: AS IS TO BE TO DO IT & Business together Top Management Buy-in 3 year program Scope: Big Data Analytics Self Service/Data Discovery Data Quality Network of Truth Agile BI Future-proof Architecture

5 BIG data WHY NOW? (2014) Big data fever (2013/14) Event based churn (teradata / marketing) Social media command center (hadoop / T-SYSTEMS) Location based ads (EMC / product house) DPI reporting (CLOUDERA hadoop / IT) DWH-BI-Bingo-Big Data

6 DPI REPORTING PILOT (2014) 4bn records per day (1,5 TB raw data) Original report: once a year, 3 months delay Classic DWH trial: 1 day of data aggregated in 3 days Hadoop trial: 1 day of data aggregated in 15 secs 6

7 Big data Architecture v /13/2017 DWH- BI- Bingo- Big Data

8 WHERE ARE WE TODAY?

9 BIG DATA SUMMARY Network data Customer data Facebook data DATA SOURCES TV data Movement data Webshop data Competitive advantages, growth, transparency, Anonymization, cleansing, stitching, enrichment, event stream processing CONSUMERS OUTCOMES Network optimization Customer Experience Marketing E-Commerce Data Monetization Security, fraud management ENTERPRISE DATA LAKE 80 GB/day ingested 100+ TB data in total 200+ Big Data Users Business modeling, insight understanding 100+ Business Reports/Analytics DATA PROCESSING INSIGHTS Combine internal & external sources Search for patterns Visualize data Create models, analyses, predictions Machine Learning

INTERFACE LAYER CONSUMPTION LAYER ANALYTICS LAYER ENGINEERING

10 DWPedia BI LANDSCAPE BI Portal COMMON BI LAYER Data Mining Data Discovery Reporting Dashboards DWH DATA LAKE DN LAYER INTERFACE LAYER CONSUMPTION LAYER ANALYTICS LAYER ENGINEERING LAYER STAGING LAYER MIRROR LAYER DWH-BI-Bingo-Big Data 6/13/

Big Data Development Process Overview Big Data DEVELOPMENT process Big Data DATA SCIENTIST activity Regular Prod. process Read only Upgrade Prod.

process) Local development OR Big Data TST environment (developer user account) Testing newly developed applications Big Data TST environment (technical user account)

11 Big Data Development Process Overview Big Data DEVELOPMENT process Big Data DATA SCIENTIST activity Regular Prod. process Read only Upgrade Prod. Algorithm (release mgmt. process) Local development OR Big Data TST environment (developer user account) Testing newly developed applications Big Data TST environment (technical user account) Production deployment Only BI Inn. team! Big Data PROD environment Advantages: Dedicated space in PROD for Data Mining and other Data Science activity Dedicated resource queue (separated for Prod process and Data Sciences) Better performance, suitable for real-life tests Access to prod. tables, more data to work on ML algorithms

12 Data Flow Management Source systems Staging S M E A C Mirror M Data Lake Target systems, consumers Special Approval Sensitive data repository Allowed data flow directions: MIRROR ENGINEERING ANALYTICS CONSUMPTION No data transformation Anonymization/ Pseudonimization Only entry point for DL Data transformation based on bus. rules Data stitching Data enrichment Facts Complex data transformations Advanced analytics (ML) Assumptions Optimized for data consumption Reports, dashboards Only Consumption point for DL Non-sensitive data Full anonymization Aggregation (min. 5 record level) Exclude sensitive columns Sensitive data

13 Big Data Enablements

14 Enablers MOVING FROM USE-CASE BASED DEVELOPMENT TO ENABLEMENT BASED APPROACH Data ingestion from many relevant data sources Consolidate and cleanse data Enrich and analyze consolidated data Use in various data products Solutions Mirror layer Engineering layer Analytical layer Consumption layer dd.mm.yyyy 14

15 BigData Analytic ENABLEMENT AREAS Network profile IT System profile Business Process profile User profile Other Physical Network Analytics IT System Analytics Call Center, Store, Online Analytics, Sales, Provisioning, etc. User Contract Household Segment 15

16 USE CASES

NETWORK OPTIMIZATION WITH BIG DATA Best customer experience Benefits Possibility to measure and visualize network traffic, coverage, users, devices; irritations and application usage of Telekom

17 NETWORK OPTIMIZATION WITH BIG DATA Best customer experience Benefits Possibility to measure and visualize network traffic, coverage, users, devices; irritations and application usage of Telekom customers UEFA EURO 2016 Without BInGO is would be impossible to create such insights, understand our customers behavior on our network and define optimization, improvement actions (TU, Marketing- CX)

18 Improve THE user experience (Hard Drop calls or data sessions) 18

CUSTOMER BEHAVIOR ANALYSIS (POKÉMON) Best

data-driven campaign launched in only 1 month

players LEVELUP DATA OPTIONS SOLD (15th August

19 CUSTOMER BEHAVIOR ANALYSIS (POKÉMON) Best customer experience Benefits First real data-driven campaign launched in only 1 month after Pokémon launch that reached more than twice conversion rate. Fast launched campaign to mobilize Pokémon GO players LEVELUP DATA OPTIONS SOLD (15th August 15th September) I am really proud of our first big data driven campaign with excellent results (Marketing) 19

20 CUSTOMER BEHAVIOR ANALYSIS 20

21 IT and Big Data OperationS Analytics 21

22 Q&A

23 Thank You!

BIG DATA AT MAGYAR TELEKOM Márton Kelemen Head of BI Innovation Center

BIG DATA AT MAGYAR TELEKOM Márton Kelemen Head of BI Innovation Center kelemen.marton@telekom.hu Strategic program 2014 2016: Strategic Program IT & Business together, with Top Management Buy-in Outcome: