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Becoming (Big) Data Driven
Geert Van Landeghem
3
▪ Big Data Consultant@DataCrunchers!
▪ geert@datacrunchers.eu
▪ From Vision to Implementation
▪ References
13
The Ubiquity of Data Opportunities
!
Continuous investments in business infrastructure!
!
Virtually every aspect of business can be measured!
!
More and more External data is available!
!
More interest into getting information out of data!
!
Data mining versus data science
13
From Business Problems to Data Mining
13
Data mining tasks
!
Classification!
Regression!
Clustering!
Profiling!
Similarity matching!
Co-occurence grouping!
Data reduction!
Link prediction!
Casual modeling
Data Science Context
7
2
How DDD affects performance
!
!
!
The more data driven a firm is, the more productive it is!!
!
One standard deviation higher on the DDD scale is associated with a !
4%–6% increase in productivity.!
!
DDD also is correlated with higher return on assets, return on equity, asset
utilization, and market value, and the relationship seems to be causal!
!
!
!
!
Source: Study by Economist Erik Brynjolfsson and his colleagues from MIT (2011)
2
Data Driven marketeers outperform their competition
Source: The State of Marketing 2013, IBM’s Global Survey of Marketeers
Many leaders are data-driven
4
What does it mean to be data-driven?
!
The culture and capabilities that!
operationalize capturing, processing, and!
utilizing data to make timely decisions!
that improve products, customer!
experiences, operational efficiency and!
competiveness.
2
Data driven hurdles
!
Disparate Data Sets!
!
Data owned by different silos within the org!
!
Lack of funding!
!
Lack of time/resources!
!
Legacy operating rhythm
2
Embarking on your data driven journey
!
Transformation will not happen overnight!
!
Support of C-Level (top-down)!
!
Requires cross-functional collaboration!
!
Combination of people, process, tools!
!
Cultural champion is needed
2
How does big data fit in?
!
Big data refers to data that is too big to fit !
on a single server, too unstructured to fit !
into a row-and-column database, !
or too continuously flowing !
to fit into a static data warehouse
6
Big Data Technical Drivers
7
Big Data Business Drivers
!
Do More (Analytics) with Less (Costs)
8
Big Data Driven Roadmap
13
14
Source: Bill Schmerzo (EMC)!
How to build the 360° view?
!
!
!
Two-way data integrations across
multi-channels!
!
Data is always updated and !
accessible for use
2
The Enterprise Data Hub
Enterprise Data Hub Definition
!
Contains all enterprise data!
! Internal & External!
!
Processes all data in „near real-time” to generate views!
! Historic & Flowing!
!
Views!
Answers to business questions
2
A Common Start Situation
16
Our Vision #1
Focus on Data not on Derived Data
17
Our Vision #2
Data is immutable!
18
Our Vision #3
Query = function (all data)
19
Conceptually
20
21
Lambda architecture
View generation
!
- Use machine learning!
▪ Recommendations!
▪ Clustering!
▪ Classification!
▪ Regression!
▪ R!
▪ …!
- SQL Like (Hive)!
- Data Flow Programming (Pig)
25
13
References
28

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Becoming (Big) Data Driven presentation at BusinessMeetsIt Big Data seminar March 13th 2013

  • 2. Geert Van Landeghem 3 ▪ Big Data Consultant@DataCrunchers! ▪ geert@datacrunchers.eu ▪ From Vision to Implementation ▪ References
  • 3. 13 The Ubiquity of Data Opportunities ! Continuous investments in business infrastructure! ! Virtually every aspect of business can be measured! ! More and more External data is available! ! More interest into getting information out of data! ! Data mining versus data science
  • 4. 13 From Business Problems to Data Mining
  • 5. 13 Data mining tasks ! Classification! Regression! Clustering! Profiling! Similarity matching! Co-occurence grouping! Data reduction! Link prediction! Casual modeling
  • 7. 2 How DDD affects performance ! ! ! The more data driven a firm is, the more productive it is!! ! One standard deviation higher on the DDD scale is associated with a ! 4%–6% increase in productivity.! ! DDD also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal! ! ! ! ! Source: Study by Economist Erik Brynjolfsson and his colleagues from MIT (2011)
  • 8. 2 Data Driven marketeers outperform their competition Source: The State of Marketing 2013, IBM’s Global Survey of Marketeers
  • 9. Many leaders are data-driven 4
  • 10. What does it mean to be data-driven? ! The culture and capabilities that! operationalize capturing, processing, and! utilizing data to make timely decisions! that improve products, customer! experiences, operational efficiency and! competiveness. 2
  • 11. Data driven hurdles ! Disparate Data Sets! ! Data owned by different silos within the org! ! Lack of funding! ! Lack of time/resources! ! Legacy operating rhythm 2
  • 12. Embarking on your data driven journey ! Transformation will not happen overnight! ! Support of C-Level (top-down)! ! Requires cross-functional collaboration! ! Combination of people, process, tools! ! Cultural champion is needed 2
  • 13. How does big data fit in? ! Big data refers to data that is too big to fit ! on a single server, too unstructured to fit ! into a row-and-column database, ! or too continuously flowing ! to fit into a static data warehouse 6
  • 14. Big Data Technical Drivers 7
  • 15. Big Data Business Drivers ! Do More (Analytics) with Less (Costs) 8
  • 16. Big Data Driven Roadmap 13
  • 18. How to build the 360° view? ! ! ! Two-way data integrations across multi-channels! ! Data is always updated and ! accessible for use 2
  • 20. Enterprise Data Hub Definition ! Contains all enterprise data! ! Internal & External! ! Processes all data in „near real-time” to generate views! ! Historic & Flowing! ! Views! Answers to business questions 2
  • 21. A Common Start Situation 16
  • 22. Our Vision #1 Focus on Data not on Derived Data 17
  • 23. Our Vision #2 Data is immutable! 18
  • 24. Our Vision #3 Query = function (all data) 19
  • 27. View generation ! - Use machine learning! ▪ Recommendations! ▪ Clustering! ▪ Classification! ▪ Regression! ▪ R! ▪ …! - SQL Like (Hive)! - Data Flow Programming (Pig) 25
  • 29. 28