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How to Capitalize on Big Data with Oracle Analytics Cloud

The document discusses leveraging Oracle Analytics Cloud and big data to enhance business intelligence and data-driven decision-making. It covers Perficient's services and examples of use cases for customer engagement, operational efficiency, and predictive analytics, emphasizing the importance of a scalable and integrated data platform. Key elements include Oracle's cloud offerings, data architecture strategies, and the challenges organizations face in managing diverse data types.

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How to Capitalize on Big Data with Oracle Analytics Cloud June, 2018
Shiv Bharti Practice Director, Oracle Business Analytics shiv.bharti@perficient.com Mazen Manasseh Senior Solutions Architect, Oracle Business Analytics Mazen.manasseh@perficient.com
3 Agenda • About Perficient • Opportunity • Technical Use Cases • Business Use Cases • Big Data Architecture • Oracle Big Data Strategy and Cloud • Which Oracle Cloud Services to Choose • Implementation Approach • OAC and Data Lake • Q&A
4 About Perficient Perficient is the leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience, business optimization, and industry solutions.
5 Perficient Profile • Founded in 1997 • Public, NASDAQ: PRFT • 2017 revenue $485 million • Major market locations: Allentown, Atlanta, Ann Arbor, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Phoenix, Seattle, Southern California, St. Louis, Toronto, Washington, D.C. • Global delivery centers in China, India and Mexico • 3,000+ colleagues • Dedicated solution practices • ~95% repeat business rate • Alliance partnerships with major technology vendors • Multiple vendor/industry technology and growth awards
6 Perficient’s Oracle BI Practice Fast Facts • Practice Started: 2004 • Projects Completed: 400+ • Management Team: 14 years • 60% of consultants former Oracle Eng. • Oracle authorized education center • Oracle BI Apps, OBIEE, ODI • Perficient runs it’s business on Oracle BI Solutions Expertise • BI/DW strategy and assessments • OBIEE and Oracle BI Apps • Cloud & on-premises solutions • Custom data warehouse services • Master Data Management • Data integration, discovery, big data • Exadata & Exalytics • Oracle Golden Gate Oracle Specializations
Opportunity
8 Opportunity is Well Known • Data is the fabric of every organization • 10X return on analytics investment • $430B advantage to those who analyze all data and deliver actionable insights Opportunity
9 of companies will be investing in predictive and prescriptive analytics 40% Copyright © 2018, Oracle Source: Forrester • Data growth is driving complexity – Data is doubling every 2 years • Digital transformation is critical to success – Emerging technologies driving need to stay competitive • Data science is key to innovation – Machine learning and AI a new norm Data-Driven Businesses Are Winning Business is Changing
10 Organizations Want To • Access all data – Structured, semi-structured, batch, real-time • Innovate using data science – Machine Learning, AI • Build Data-Driven Solutions – Smart, adaptive • Data to inform every decision, every minute in every moment that matters • Analytic system that can interrupt consumers with relevant insights want scalable platform for Data Science 35% Source: IDC Copyright © 2018, Oracle
Technical Use Cases for a Modern Data Platform
12 Data Integration from Any Source • Data transformation / preparation • Data replication • Data quality Social, IoT, more… SaaS Transactional …make it accessible Data Lake Copyright © 2018, Oracle
13 • Offload data from data warehouse • Process data in the data lake for the data warehouse • Load data products to data warehouse Complete Data Management Data Lake …including data in motion Data Warehouse NoSQL Copyright © 2018, Oracle
14 Streaming Data Data streams Analysis Transformations Data Lake …from any source • Event streams • Stream processing • Stream analytics Copyright © 2018, Oracle
15 Accelerating Data Lake Queries At Scale • 10X to 100X faster • Accelerates any tool • Scale out with increased data or users • Work with the original data, not pre-aggregates Data Lake …in different locations Data Science SQL Analytics In-memory • Indexing • Cache • Query optimization Copyright © 2018, Oracle
16 Data Catalog Catalog Data Lake …and apply data science • What data is available? • Who owns the data? • Where did it come from? • Intelligent recommendations Copyright © 2018, Oracle
Business Use Cases for a Modern Data Platform
18 Use Case: Customer Segmentation & Offers Increase agility and reduce response times to improve customer engagement and satisfaction Marketing teams can dynamically target messaging and adjust outreach frequency based on variable data. For example, retailers can create hyper-targeted campaigns to users based on location and weather conditions. Marketers can quickly adjust these campaign parameters if a new line of apparel is released.
19 Use Case: Customer-Centric Analytics Real-time insights and seamless integration across channels taking lead with loyalty, efficiency, and agility Email Marketing - Targeted discounts at key moments in the lifecycle, and to select customer segments, to deepen brand relationships. OmniChannel Marketing - Single, unified view of the customer -- and seamless user experience -- across channels. Ability to tailor communication on one platform to a user’s engagement on another. Predictive Marketing - Predicting customer lifetime value (CLV) based on a customer’s first purchased product or acquisition channel; identifying which customers are most likely to churn.
20 Use Case: Operational Efficiency - Lower Cost and Drive Growth  Improve performance of existing internal processes across business operations, IT, development & testing  Optimize supply chain including inventory management  Accelerate regulatory compliance reporting, days to minutes
21 Use Case: Predictive Maintenance Machine Learning - Neural Networks Using neural networks to predict attributes of the performance of a pump. This might be part of a predictive maintenance use case, where an organization is looking to prevent mechanical failures, or issues that impact production of some chemical.
What Does a Typical Big Data Architecture Consist of?
23 What are Common Data Lake Technologies? Data Integration Presentation & Machine Learning Storage Access & Processing SQL StreamingBatch ODBC, JDBC ETL, Sqoop, Hive, REST Kafka, Flume RDBMS HDFS (Hadoop File System)NoSQL HBase Data Warehouse Object Storage SQL StreamingBatch ODBC, JDBC Pig, Hive, MapReduce Spark, Impala Mahout Python R Zeppelin Flink Operational & Platform Services Hue Oozie Zookeper YARN Ambari Job Scheduler Sentry
24 Classical BI vs. Big Data Analytics Volume of data and a different type of business need demand a non-conventional design approach. Classic Analytics Schema on Write Big Data Analytics Schema on Read Sources ETL to Schema Store (EDW) Analyze Sources Store (Hadoop) Code to Schema Analyze
25 Classical BI vs. Big Data Analytics Volume of data and a different type of business need demand a non-conventional design approach. Classic Analytics Schema on Write Big Data Analytics Schema on Read Sources ETL to Schema Store (EDW) Analyze Sources Store (Hadoop) Code to Schema Analyze
26 Challenges of Analyzing Diverse Data Organizations need to cope with diverse data  One solution to access all types of data Big Data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower Business Users to self-serve and achieve their goals
27 Challenges of Analyzing Diverse Data Organizations need to cope with diverse data  One solution to access all types of data Big data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower Business Users to self-serve and achieve their goals
28 Challenges of Analyzing Diverse Data Organizations need to cope with diverse data  One solution to access all types of data Big data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower business users to self-serve and achieve their goals
Oracle Big Data Strategy & Cloud Service Offerings
30 Oracle Information Management Services Oracle IDCS Identity & Access Management Oracle VPNaaS Oracle Object Storage Cloud Oracle Cloud Infrastructure text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
31 Oracle Information Management Services Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
32 Oracle Big Data Cloud Service (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
33 Oracle Big Data Cloud Service (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
34 Oracle Big Data Cloud Service (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
35 Oracle Event Hub Cloud Service Use Oracle Event Hub Cloud Service to ingest streaming data. • Based on an Open Source Apache Kafka technology • Extremely low latency and high throughput stream analytics platform • Hosted and managed by Oracle Cloud • Can create dedicated Kafka clusters • Create Kafka Topics and start streaming data using Kafka, REST and Stream Java APIs • Native integration with Oracle Big Data Stack and other Cloud Apps Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
36 Oracle Data Integrator for Big Data ODI for Big Data helps transform and enrich data within the big data lake without developers having to learn the languages necessary to manipulate them. • ODI for Big Data leverages a familiar graphical interface to generate complex Hadoop native code • Transformation and data movement logic is pushed down to run directly in Hadoop. • Optimizes developer productivity and allows ODI users to build business and data mappings without having to learn big data languages like HiveQL, Pig, and Map Reduce. • Easily move data among different platforms: e.g.: from HBase to Oracle Database, HBase to Hive, Hadoop to third-party databases and from third- party databases to HDFS, etc. Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
37 Oracle Big Data Cloud Service Big data platform (PaaS) designed for the enterprise and combines Open Source technologies with Oracle Cloud innovations: • Leverages pre-configured Cloudera’s distribution of Apache Hadoop and Spark • Has the latest advances in the Hadoop ecosystem • Provides the latest big data analytical tools, automatically installed and configured for best performance • Stream processing • Apache Spark for Machine Learning • Cloudera Impala for Ad-hoc querying • Includes Big Data pre-configured components • Spatial for scalable geo-spacial and map-building • Graph for storage and analysis of social and IoT networks • R Advanced Analytics for Apache Hadoop and Spark • Big data connectors to simplify integration Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
38 Oracle Big Data Cloud Service File System (HDFS) NoSQL (HBase) Visualization & Analysis Oracle R, Spatial, Graph, Notebook (Zeppelin) Big data connectors to extract Hadoop data to external systems • SQL Connector • Loader • XQuery • R • ODI Oracle Data Integrator (ODI) for Hadoop Workload Orchestration Big Data Storage Cloudera Hadoop Enterprise Cloudera Manager backup, recovery, Cloudera navigator Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
39 Oracle Big Data SQL Cloud Service Allows joining enterprise data that resides in relational databases and data warehouses with Oracle Big Data. • Seamlessly use SQL query to integrate data from Hadoop, NoSQL, and Oracle Database • Simplifies data science efforts • Makes big data available into the hands of more data consumers • Facilitates the integration of big data into existing applications • Extends Oracle database security features to data in Hadoop Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
40 Oracle Analytics Cloud Single platform to ingest, transform and access all Information Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC Data Access • Visualizations, dashboards, self-service discovery, Day by Day mobile app • Pixel-perfect reporting Transformation & Data Modeling • Data flows to ingest, blend, prepare, and store data • Transformation at scale with Apache Spark or Oracle DB • Single source of truth for enterprise data using a well-governed semantic layer • High-performance, pre-calculated aggregations with Essbase Cubes Big Data Connectors & Built-in Adapters • Out-of-the-box integrations with Oracle Big Data stack • High-performance data movement between relational databases and big data Clusters using Hive, Spark-SQL, Spark and/or Map-Reduce • Various Sources: SaaS Apps, DBs, NoSQL, and generic ODBC/JDBC
41 Oracle Analytics Cloud Editions * This is not the data lake itself, but it has capabilities to create and manage the data lake. The Data Lake itself is another platform either an on-prem data lake or a Big Data Cloud Service. Functionality Standard Edition Data Lake Edition* Enterprise Edition Data Visualization Self-service Data Preparation Essbase Cloud Big Data Sources Data Lake Visual Discovery Data Preparation at Scale in Spark Enterprise Data Modeling Enterprise Reporting (OBIEE Style) Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
Which Oracle Cloud Service to Chose?
43 Oracle Big Data Cloud Service (BDCS) Data Engineering Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
44 Oracle Big Data Cloud Service (BDCS) Data Engineering Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
45 Oracle Big Data Cloud Service (BDCS) Data Science Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist • Ingest & Model • Transform, ML • Analyze Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
46 Oracle Big Data Cloud Service (BDCS) SQL Querying Service Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate Oracle Big Data SQL Cloud Executive VP LOB Manager Business Analyst • Analyze Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
47 Oracle Big Data Cloud Service (BDCS) Self-Services for Business Users Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst • Ingest & Model • Transform, ML • Analyze Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
Big Data Solution Implementation Approach
49 Big Data Best Practices Converge various data engineering/science operations onto a coherent and comprehensive platform. Doing this improves efficiency and effectiveness of a big data solution. Choose Cloud for a Big Data Solution • Avoid the complex installation and configuration of the various Big Data frameworks • Concentrate instead on the business value • Stay up to date with continuously evolving open source technologies • Start small and focused but with scaling out in mind from the start • Infrastructure elasticity at different independent levels: Cluster scale up/down, in/out; Storage, Compute (CPU and Memory) • Pay for processing when needed, as much as needed
50 Big Data Best Practices Big data is not only a technology solution, but a business driver • Data accessible to the right people, not just data scientists and IT • Empower business analysts and end users • Make data lake readily accessible through a variety of tools (seamless access to HDFS, DB through web, alerts and mobile voice recognition) Machine learning is key to success • Aim at automating data preparation; this allows you to easily scale and be more productive • Integration of external data with enterprise data in a secure, scalable and reliable manner is essential to success • Machine learning is key to success when dealing with big data
Oracle Analytics Cloud Makes the Most of a Data Lake
52 Steps to Create & Manage a Data Lake with OAC Discover • Discover & Access Various Sources Prepare • Integrate & Transform Analyze • Interactive Visualization Predict • Machine Learning & Modeling
53 OAC for Data Discovery Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover • Leverage OAC adapters to connect to a variety of data sources • Ingest data into the data lake • Use OAC to replicate data into Oracle big data or Oracle DB
54 OAC for Data Discovery Manage a catalog of data assets • Projects • Connections • Data sets • Data flows and sequences Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
55 OAC for Data Preparation Prepare data sets • Ease of use: Spreadsheet-like transformations • Remove duplicates, replace nulls and standardize inconsistent values • Create custom groups and expressions Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
56 OAC for Data Preparation Create data flows to map to new data sets • Intuitive data transformation flows • Immediate feedback in data preview • Function shipping: Push-down of execution into sources • Native execution in Spark for data lake • Load data into data sets, databases or Essbase cubes Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
57 OAC for Data Analysis Visualize & present • Automatic chart creation based on intelligent data services • Rich palette of built-in visualizations • Single-click trending and forecasting, clustering and outliers detection Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
58 OAC for Data Analysis Support for custom-built external visualization plugins Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
59 OAC for Data Analysis Automatic explanation of data sets • Explore and understand unfamiliar data • Automatic pattern detection • Guide users towards strongest correlated factors and variances from norm Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
60 OAC for Predictions Advanced transforms and scripts in data flows • Time series forecast • Predict possible future trends based on past value patterns • Based on ARIMA model • Sentiment snalysis • Analysis of natural language based on term usage • Custom scripts in Python and R Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
61 OAC for Predictions Machine learning data flows • Use data flows to build machine learning pipelines • Train and score models through data flows Variety of ML algorithms • Numeric prediction • Multi-classifier • Binary classifier • Clustering • Customer algorithms Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
62 Manage and Monitor Leverage OAC to schedule and automate data lake activities • Data replication jobs • Data set refreshes • Setup recurring job schedules • Sequence data flows
63 How to Get Started • Explore Cloud Services • Oracle Big Data Cloud https://cloud.oracle.com/en_US/big-data-cloud • Oracle Analytics Cloud https://cloud.oracle.com/en_US/biz-analytics • Hear from the Experts • Best practices and approach to innovating using big data platform http://blogs.oracle.com/bigdata • Try a Big Data Guided Trial • Step by step guide to building a fully functional data lake http://www.oracle.com/bigdatajourney
Questions Type your question into the chat box
65 Next up: [Guide] The Future of Big Data: Why Traditional Data Warehouses Won’t Solve Your Big Data Challenges Follow Us Online • Perficient.com/Insights • Facebook.com/Perficient • Twitter.com/PRFT_Oracle • Blogs.perficient.com/oracle
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How to Capitalize on Big Data with Oracle Analytics Cloud

  • 1.
    How to Capitalizeon Big Data with Oracle Analytics Cloud June, 2018
  • 2.
    Shiv Bharti Practice Director, OracleBusiness Analytics shiv.bharti@perficient.com Mazen Manasseh Senior Solutions Architect, Oracle Business Analytics Mazen.manasseh@perficient.com
  • 3.
    3 Agenda • About Perficient •Opportunity • Technical Use Cases • Business Use Cases • Big Data Architecture • Oracle Big Data Strategy and Cloud • Which Oracle Cloud Services to Choose • Implementation Approach • OAC and Data Lake • Q&A
  • 4.
    4 About Perficient Perficient isthe leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience, business optimization, and industry solutions.
  • 5.
    5 Perficient Profile • Foundedin 1997 • Public, NASDAQ: PRFT • 2017 revenue $485 million • Major market locations: Allentown, Atlanta, Ann Arbor, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Phoenix, Seattle, Southern California, St. Louis, Toronto, Washington, D.C. • Global delivery centers in China, India and Mexico • 3,000+ colleagues • Dedicated solution practices • ~95% repeat business rate • Alliance partnerships with major technology vendors • Multiple vendor/industry technology and growth awards
  • 6.
    6 Perficient’s Oracle BIPractice Fast Facts • Practice Started: 2004 • Projects Completed: 400+ • Management Team: 14 years • 60% of consultants former Oracle Eng. • Oracle authorized education center • Oracle BI Apps, OBIEE, ODI • Perficient runs it’s business on Oracle BI Solutions Expertise • BI/DW strategy and assessments • OBIEE and Oracle BI Apps • Cloud & on-premises solutions • Custom data warehouse services • Master Data Management • Data integration, discovery, big data • Exadata & Exalytics • Oracle Golden Gate Oracle Specializations
  • 7.
  • 8.
    8 Opportunity is WellKnown • Data is the fabric of every organization • 10X return on analytics investment • $430B advantage to those who analyze all data and deliver actionable insights Opportunity
  • 9.
    9 of companies will beinvesting in predictive and prescriptive analytics 40% Copyright © 2018, Oracle Source: Forrester • Data growth is driving complexity – Data is doubling every 2 years • Digital transformation is critical to success – Emerging technologies driving need to stay competitive • Data science is key to innovation – Machine learning and AI a new norm Data-Driven Businesses Are Winning Business is Changing
  • 10.
    10 Organizations Want To •Access all data – Structured, semi-structured, batch, real-time • Innovate using data science – Machine Learning, AI • Build Data-Driven Solutions – Smart, adaptive • Data to inform every decision, every minute in every moment that matters • Analytic system that can interrupt consumers with relevant insights want scalable platform for Data Science 35% Source: IDC Copyright © 2018, Oracle
  • 11.
    Technical Use Casesfor a Modern Data Platform
  • 12.
    12 Data Integration fromAny Source • Data transformation / preparation • Data replication • Data quality Social, IoT, more… SaaS Transactional …make it accessible Data Lake Copyright © 2018, Oracle
  • 13.
    13 • Offload datafrom data warehouse • Process data in the data lake for the data warehouse • Load data products to data warehouse Complete Data Management Data Lake …including data in motion Data Warehouse NoSQL Copyright © 2018, Oracle
  • 14.
    14 Streaming Data Data streams Analysis Transformations DataLake …from any source • Event streams • Stream processing • Stream analytics Copyright © 2018, Oracle
  • 15.
    15 Accelerating Data LakeQueries At Scale • 10X to 100X faster • Accelerates any tool • Scale out with increased data or users • Work with the original data, not pre-aggregates Data Lake …in different locations Data Science SQL Analytics In-memory • Indexing • Cache • Query optimization Copyright © 2018, Oracle
  • 16.
    16 Data Catalog Catalog Data Lake …andapply data science • What data is available? • Who owns the data? • Where did it come from? • Intelligent recommendations Copyright © 2018, Oracle
  • 17.
    Business Use Casesfor a Modern Data Platform
  • 18.
    18 Use Case: Customer Segmentation &Offers Increase agility and reduce response times to improve customer engagement and satisfaction Marketing teams can dynamically target messaging and adjust outreach frequency based on variable data. For example, retailers can create hyper-targeted campaigns to users based on location and weather conditions. Marketers can quickly adjust these campaign parameters if a new line of apparel is released.
  • 19.
    19 Use Case: Customer-Centric Analytics Real-time insightsand seamless integration across channels taking lead with loyalty, efficiency, and agility Email Marketing - Targeted discounts at key moments in the lifecycle, and to select customer segments, to deepen brand relationships. OmniChannel Marketing - Single, unified view of the customer -- and seamless user experience -- across channels. Ability to tailor communication on one platform to a user’s engagement on another. Predictive Marketing - Predicting customer lifetime value (CLV) based on a customer’s first purchased product or acquisition channel; identifying which customers are most likely to churn.
  • 20.
    20 Use Case: Operational Efficiency- Lower Cost and Drive Growth  Improve performance of existing internal processes across business operations, IT, development & testing  Optimize supply chain including inventory management  Accelerate regulatory compliance reporting, days to minutes
  • 21.
    21 Use Case: Predictive Maintenance MachineLearning - Neural Networks Using neural networks to predict attributes of the performance of a pump. This might be part of a predictive maintenance use case, where an organization is looking to prevent mechanical failures, or issues that impact production of some chemical.
  • 22.
    What Does aTypical Big Data Architecture Consist of?
  • 23.
    23 What are CommonData Lake Technologies? Data Integration Presentation & Machine Learning Storage Access & Processing SQL StreamingBatch ODBC, JDBC ETL, Sqoop, Hive, REST Kafka, Flume RDBMS HDFS (Hadoop File System)NoSQL HBase Data Warehouse Object Storage SQL StreamingBatch ODBC, JDBC Pig, Hive, MapReduce Spark, Impala Mahout Python R Zeppelin Flink Operational & Platform Services Hue Oozie Zookeper YARN Ambari Job Scheduler Sentry
  • 24.
    24 Classical BI vs.Big Data Analytics Volume of data and a different type of business need demand a non-conventional design approach. Classic Analytics Schema on Write Big Data Analytics Schema on Read Sources ETL to Schema Store (EDW) Analyze Sources Store (Hadoop) Code to Schema Analyze
  • 25.
    25 Classical BI vs.Big Data Analytics Volume of data and a different type of business need demand a non-conventional design approach. Classic Analytics Schema on Write Big Data Analytics Schema on Read Sources ETL to Schema Store (EDW) Analyze Sources Store (Hadoop) Code to Schema Analyze
  • 26.
    26 Challenges of AnalyzingDiverse Data Organizations need to cope with diverse data  One solution to access all types of data Big Data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower Business Users to self-serve and achieve their goals
  • 27.
    27 Challenges of AnalyzingDiverse Data Organizations need to cope with diverse data  One solution to access all types of data Big data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower Business Users to self-serve and achieve their goals
  • 28.
    28 Challenges of AnalyzingDiverse Data Organizations need to cope with diverse data  One solution to access all types of data Big data technology is fragmented and complex with many platform  Leverage emerging technology in a single unified platform Specialized skills are in short supply  Empower business users to self-serve and achieve their goals
  • 29.
    Oracle Big DataStrategy & Cloud Service Offerings
  • 30.
    30 Oracle Information ManagementServices Oracle IDCS Identity & Access Management Oracle VPNaaS Oracle Object Storage Cloud Oracle Cloud Infrastructure text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 31.
    31 Oracle Information ManagementServices Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 32.
    32 Oracle Big Data CloudService (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 33.
    33 Oracle Big Data CloudService (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 34.
    34 Oracle Big Data CloudService (BDCS) Oracle Information Management Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 35.
    35 Oracle Event HubCloud Service Use Oracle Event Hub Cloud Service to ingest streaming data. • Based on an Open Source Apache Kafka technology • Extremely low latency and high throughput stream analytics platform • Hosted and managed by Oracle Cloud • Can create dedicated Kafka clusters • Create Kafka Topics and start streaming data using Kafka, REST and Stream Java APIs • Native integration with Oracle Big Data Stack and other Cloud Apps Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 36.
    36 Oracle Data Integratorfor Big Data ODI for Big Data helps transform and enrich data within the big data lake without developers having to learn the languages necessary to manipulate them. • ODI for Big Data leverages a familiar graphical interface to generate complex Hadoop native code • Transformation and data movement logic is pushed down to run directly in Hadoop. • Optimizes developer productivity and allows ODI users to build business and data mappings without having to learn big data languages like HiveQL, Pig, and Map Reduce. • Easily move data among different platforms: e.g.: from HBase to Oracle Database, HBase to Hive, Hadoop to third-party databases and from third- party databases to HDFS, etc. Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 37.
    37 Oracle Big DataCloud Service Big data platform (PaaS) designed for the enterprise and combines Open Source technologies with Oracle Cloud innovations: • Leverages pre-configured Cloudera’s distribution of Apache Hadoop and Spark • Has the latest advances in the Hadoop ecosystem • Provides the latest big data analytical tools, automatically installed and configured for best performance • Stream processing • Apache Spark for Machine Learning • Cloudera Impala for Ad-hoc querying • Includes Big Data pre-configured components • Spatial for scalable geo-spacial and map-building • Graph for storage and analysis of social and IoT networks • R Advanced Analytics for Apache Hadoop and Spark • Big data connectors to simplify integration Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 38.
    38 Oracle Big DataCloud Service File System (HDFS) NoSQL (HBase) Visualization & Analysis Oracle R, Spatial, Graph, Notebook (Zeppelin) Big data connectors to extract Hadoop data to external systems • SQL Connector • Loader • XQuery • R • ODI Oracle Data Integrator (ODI) for Hadoop Workload Orchestration Big Data Storage Cloudera Hadoop Enterprise Cloudera Manager backup, recovery, Cloudera navigator Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 39.
    39 Oracle Big DataSQL Cloud Service Allows joining enterprise data that resides in relational databases and data warehouses with Oracle Big Data. • Seamlessly use SQL query to integrate data from Hadoop, NoSQL, and Oracle Database • Simplifies data science efforts • Makes big data available into the hands of more data consumers • Facilitates the integration of big data into existing applications • Extends Oracle database security features to data in Hadoop Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 40.
    40 Oracle Analytics Cloud Singleplatform to ingest, transform and access all Information Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC Data Access • Visualizations, dashboards, self-service discovery, Day by Day mobile app • Pixel-perfect reporting Transformation & Data Modeling • Data flows to ingest, blend, prepare, and store data • Transformation at scale with Apache Spark or Oracle DB • Single source of truth for enterprise data using a well-governed semantic layer • High-performance, pre-calculated aggregations with Essbase Cubes Big Data Connectors & Built-in Adapters • Out-of-the-box integrations with Oracle Big Data stack • High-performance data movement between relational databases and big data Clusters using Hive, Spark-SQL, Spark and/or Map-Reduce • Various Sources: SaaS Apps, DBs, NoSQL, and generic ODBC/JDBC
  • 41.
    41 Oracle Analytics CloudEditions * This is not the data lake itself, but it has capabilities to create and manage the data lake. The Data Lake itself is another platform either an on-prem data lake or a Big Data Cloud Service. Functionality Standard Edition Data Lake Edition* Enterprise Edition Data Visualization Self-service Data Preparation Essbase Cloud Big Data Sources Data Lake Visual Discovery Data Preparation at Scale in Spark Enterprise Data Modeling Enterprise Reporting (OBIEE Style) Event Hub Cloud ODI Cloud Big Data Cloud Service Big Data SQL OAC
  • 42.
    Which Oracle CloudService to Chose?
  • 43.
    43 Oracle Big Data CloudService (BDCS) Data Engineering Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 44.
    44 Oracle Big Data CloudService (BDCS) Data Engineering Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 45.
    45 Oracle Big Data CloudService (BDCS) Data Science Services Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst Data Scientist • Ingest & Model • Transform, ML • Analyze Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 46.
    46 Oracle Big Data CloudService (BDCS) SQL Querying Service Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate Oracle Big Data SQL Cloud Executive VP LOB Manager Business Analyst • Analyze Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 47.
    47 Oracle Big Data CloudService (BDCS) Self-Services for Business Users Oracle Autonomous Data Warehouse Cloud Service (ADWC) Oracle IDCS Identity & Access Management Oracle Event Hub Cloud Oracle Analytics Cloud (OAC) Oracle VPNaaS Oracle Object Storage Cloud Oracle NoSQL Database Oracle DataSync Oracle Cloud Infrastructure Oracle Data Integrator (ODI) Golden Gate O r a c l e B i g D a t a S Q L C l o u d Executive VP LOB Manager Business Analyst • Ingest & Model • Transform, ML • Analyze Data Scientist Data Engineer IT Role text Structured Data Semi-structured Data Unstructured Data devices logs sensors IoTLinkedInFacebook Google Analytics Twitter Social Media thingsEnterprise data ERP HCMCX POS Planning Transactions videoaudio
  • 48.
  • 49.
    49 Big Data BestPractices Converge various data engineering/science operations onto a coherent and comprehensive platform. Doing this improves efficiency and effectiveness of a big data solution. Choose Cloud for a Big Data Solution • Avoid the complex installation and configuration of the various Big Data frameworks • Concentrate instead on the business value • Stay up to date with continuously evolving open source technologies • Start small and focused but with scaling out in mind from the start • Infrastructure elasticity at different independent levels: Cluster scale up/down, in/out; Storage, Compute (CPU and Memory) • Pay for processing when needed, as much as needed
  • 50.
    50 Big Data BestPractices Big data is not only a technology solution, but a business driver • Data accessible to the right people, not just data scientists and IT • Empower business analysts and end users • Make data lake readily accessible through a variety of tools (seamless access to HDFS, DB through web, alerts and mobile voice recognition) Machine learning is key to success • Aim at automating data preparation; this allows you to easily scale and be more productive • Integration of external data with enterprise data in a secure, scalable and reliable manner is essential to success • Machine learning is key to success when dealing with big data
  • 51.
    Oracle Analytics Cloud Makesthe Most of a Data Lake
  • 52.
    52 Steps to Create& Manage a Data Lake with OAC Discover • Discover & Access Various Sources Prepare • Integrate & Transform Analyze • Interactive Visualization Predict • Machine Learning & Modeling
  • 53.
    53 OAC for DataDiscovery Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover • Leverage OAC adapters to connect to a variety of data sources • Ingest data into the data lake • Use OAC to replicate data into Oracle big data or Oracle DB
  • 54.
    54 OAC for DataDiscovery Manage a catalog of data assets • Projects • Connections • Data sets • Data flows and sequences Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 55.
    55 OAC for DataPreparation Prepare data sets • Ease of use: Spreadsheet-like transformations • Remove duplicates, replace nulls and standardize inconsistent values • Create custom groups and expressions Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 56.
    56 OAC for DataPreparation Create data flows to map to new data sets • Intuitive data transformation flows • Immediate feedback in data preview • Function shipping: Push-down of execution into sources • Native execution in Spark for data lake • Load data into data sets, databases or Essbase cubes Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 57.
    57 OAC for DataAnalysis Visualize & present • Automatic chart creation based on intelligent data services • Rich palette of built-in visualizations • Single-click trending and forecasting, clustering and outliers detection Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 58.
    58 OAC for DataAnalysis Support for custom-built external visualization plugins Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 59.
    59 OAC for DataAnalysis Automatic explanation of data sets • Explore and understand unfamiliar data • Automatic pattern detection • Guide users towards strongest correlated factors and variances from norm Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 60.
    60 OAC for Predictions Advancedtransforms and scripts in data flows • Time series forecast • Predict possible future trends based on past value patterns • Based on ARIMA model • Sentiment snalysis • Analysis of natural language based on term usage • Custom scripts in Python and R Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 61.
    61 OAC for Predictions Machinelearning data flows • Use data flows to build machine learning pipelines • Train and score models through data flows Variety of ML algorithms • Numeric prediction • Multi-classifier • Binary classifier • Clustering • Customer algorithms Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  • 62.
    62 Manage and Monitor LeverageOAC to schedule and automate data lake activities • Data replication jobs • Data set refreshes • Setup recurring job schedules • Sequence data flows
  • 63.
    63 How to GetStarted • Explore Cloud Services • Oracle Big Data Cloud https://cloud.oracle.com/en_US/big-data-cloud • Oracle Analytics Cloud https://cloud.oracle.com/en_US/biz-analytics • Hear from the Experts • Best practices and approach to innovating using big data platform http://blogs.oracle.com/bigdata • Try a Big Data Guided Trial • Step by step guide to building a fully functional data lake http://www.oracle.com/bigdatajourney
  • 64.
  • 65.
    65 Next up: [Guide] TheFuture of Big Data: Why Traditional Data Warehouses Won’t Solve Your Big Data Challenges Follow Us Online • Perficient.com/Insights • Facebook.com/Perficient • Twitter.com/PRFT_Oracle • Blogs.perficient.com/oracle
  • 66.

Editor's Notes

  • #25 Traditionally: “We calculate our Profit Margin in this way, or we derive Employee Transfer Rates in that way.”
  • #26 Organizations: “We know we need to use social data or we need to data mine the logs generated from our devices, and we have an idea of how this may be beneficial for our business, but we don’t know how exactly and in what way specifically. That is something we need to discover.”
  • #31 All Oracle Services benefit from the good things that the Oracle Cloud Infrastructure has to offer. Secure, elastic, and integrated platform Centralized identity and access management, VPNaaS and shared Object Storage
  • #32 All Oracle Services benefit from the good things that the Oracle Cloud Infrastructure has to offer. Secure, elastic, and integrated platform Centralized identity and access management, VPNaaS and shared Object Storage
  • #33 All Oracle Services benefit from the good things that the Oracle Cloud Infrastructure has to offer. Secure, elastic, and integrated platform Centralized identity and access management, VPNaaS and shared Object Storage
  • #34 All Oracle Services benefit from the good things that the Oracle Cloud Infrastructure has to offer. Secure, elastic, and integrated platform Centralized identity and access management, VPNaaS and shared Object Storage
  • #35 All Oracle Services benefit from the good things that the Oracle Cloud Infrastructure has to offer. Secure, elastic, and integrated platform Centralized identity and access management, VPNaaS and shared Object Storage
  • #48 With OAC, complexity of dealing with diverse data sources can be seamless to end users. For example, with OAC query re-direction, an end user looking at a dashboard may be looking at data from HDFS that OAC queries using Hive/Impala/Spark language, or it could be data aggregated in a relational database. The end user wouldn’t need to know. Based on the user’s interactions with the dashboard, OAC will automatically figure out where is the best source of data. Typically, querying HDFS is slower than querying a data warehouse. So OAC goes to the data source that makes most sense for what the user is asking for.
  • #51 Data accessible to end users: First Data Engineers (setup platform, integrations, security, admin), then Data Scientists, but still not getting real value.
  • #54 For example, from Taleo, Fusion Apps, Service Cloud, etc. Apply incremental ingestion
  • #57 For example, if you are analyzing customer buying patterns: Use Data Flow to match Online Sales Transactions from a data warehouse, or an Ecommerce system, with Website or Mobile App User Interaction logs residing in a HDFS. We do this to find out how efficient the Ecom website or the app is. How many clicks does it take for someone to browse through online products and to finally place an order? Another common example is an HR use case. You want to track employees from the time they are candidates for an open position in Taleo, and look at their career progression in your HCM system. Use Data Flow to mash up the different data sets together for reporting.