Managing explosion of data. Cloudera, Inc. All rights reserved.
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- Lorena Goodman
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1 Managing explosion of data 1
2 Customer experience expectations are converging on the brand, not channel Consistent across all channels and lines of business Contextualized to present location and circumstances Personalized to reflect preferences and aspirations Relevant in the moment to their needs and expectations 2
3 The new pathway is much more fragmented and dynamic These varied shopping channels open up thousands of potential new purchasing pathways, more than five times the number available in the brickand-mortar world. 3
4 Key business challenges Personalization expected Dynamic markets Digital landscape 4
5 Customers across industries Telecommunications Financial services Public sector Retail Healthcare 5
6 Creating true Customer 360 Breaking down data silos Sharing data in accordance with privacy and regulatory policies Becoming iterative, lean and leveraging knowledge across the business 6
7 The challenge: managing customer data Data can come from a variety of siloed sources Massive volumes of customer interaction data Some data from 3rd party data sources Can come in real-time or batches Generated from a variety of data sources Diverse data structures and schemas Some of it may be perishable Combining customer data with contextual data (social media, 3rd party) is the key to customer insight 7
8 Key technology limitations Data silos Data volume New data sources Cost of processing 8
9 Traditional data flow diagram Billing / ordering ETL / stored procedures Workflow Extract data from source ETL / aggregate source data for common schema Load aggregated data into EDW models Serve modeled data via BI tools and reports CRM/ profile Marketing campaigns Other / new data sources mobile, sensors, apps, network logs, files Limited processing power Loss in Fidelity Enterprise Data Warehouse Segmentation & churn analysis Based on sample / limited data BI tools Location Data Marts / Aggregations Social Storage scaling very expensive. Not designed for ELT Marketing offers Clickstream Does not model easily into traditional database schema Limited processing power Manual work. Few automated system feeds. 9
10 Oil doesn t generate more oil [ ] the product of data will generate more data Piero Scaruffi 10
11 Explosion of data and devices (IoT) 30B connected devices $200B total market 1 Transformation of IT infrastructure cloud open source 440x more data The data-driven enterprise machine learning 1 IDC Worldwide Big Data and Business Analytics Market Through
12 We believe data can make what is impossible today, possible tomorrow 12
13 We empower people to transform complex data into clear and actionable insights DRIVE CUSTOMER INSIGHTS CONNECT PRODUCT & SERVICES (IoT) PROTECT BUSINESS 13
14 We deliver the modern platform for machine learning and analytics optimized for the cloud RUNS ANYWHERE Cloud Multi-cloud On-premises SCALABLE Elastic Cost-effective Lower TCO ENTERPRISE GRADE Secure Performant Compliant 14
15 On-demand Big Data analytics for business users Fast For business. Easy To manage Secure Without Compromise Make Big Data Accessible Deliver Big Data In Context Keep Big Data Relevant Faster and higher Big Data ROI 15
16 We empower people to transform complex data into clear and actionable insights In the next 5 years, 42% of CMO s say analytics will be a core competency in marketing DRIVE CUSTOMER INSIGHTS 53% of CMOs have increased pressure to deliver revenue growth this year CONNECT PRODUCT & SERVICES (IoT) PROTECT BUSINESS 16
17 Solution areas for customer insight Acquire & retain Churn analysis Marketing spend analysis Customer lifetime value Cross sell & upsell Next best offer Smart promotions Basket analysis Improve customer experience Omni-channel optimization Sentiment analysis Customer care analytics 17
18 Joint customer examples 18
19 Consolidate your data sources to build a true
20 Customer insight the data value chain Data sources Data ingest & storage Data processing Machine learning Analytics / BI Diverse data sources 3rd party data Batch/ real-time Diverse schema/ formats Real-time data ingest Big-data platform Unlimited storage Low cost / TB In-memory processing Batch vs. real-time Data enrichment Data contextualization Machine learning libraries Unsupervised M/L Supervised M/L Iterative data modeling Data visualization Integration to BI tools SQL queries Search 20
21 Qlik + Cloudera 11 Points of integration Fast & flexible BI & analytics Go beyond SQL Enterprise ready App on demand w/ Impala (In memory user generated data slices ) Data lake browser (Data Concierge for Cloudera) Advanced analytics demo (Integration with Spark/Python/R) Cloudera metrics dashboard (REST API based management console for CM) Direct query w/ Impala (Data stored in Parquet or Kudu) Writeback with Kudu (Interactive analytics) Solr integration (In-memory apps built on Solr data) Security New SSO support (Kerberos delegation / SSO passthrough) Complex data types w/ Impala (Maps, arrays, and structures ) IOT and Kafka integration (Event driven / streaming analytics) Qlik Solr-API app on demand (Search + QAP + D3js) 21
22 22
23 No time like the present All the data is there Proven platform duo Leverage and refine 23
24 Accelerating Big Data ROI + A robust, flexible foundation that can adapt to a changing infrastructure landscape Visual analytics that enables everyone to analyze and explore information, generate insight and make better decisions 24
25 Thank you 25
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