Published on March 15, 2014
Becoming (Big) Data Driven
Geert Van Landeghem 3 ▪ Big Data Consultant@DataCrunchers! ▪ firstname.lastname@example.org ▪ 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
21 Lambda architecture
View generation ! - Use machine learning! ▪ Recommendations! ▪ Clustering! ▪ Classification! ▪ Regression! ▪ R! ▪ …! - SQL Like (Hive)! - Data Flow Programming (Pig) 25
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