Making Self-Service BI a Reality in the Enterprise
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For most analysts, the pace of analytics and data science can be frustrating. The common waterfall approach works well for the fixed reports, but it can be a lengthy process to request additional data sets, create new reports, or serve new use cases. So it’s no surprise that organizations are looking to shift towards a self-service model, empowering business users to discover and iterate quickly. However, it’s not just about opening up this access, but also ensuring the results are accurate and trusted. When there are petabytes of data, how does a user know which tables to use and which are most relevant? How do you strike the balance between discovery and agility, while still meeting enterprise governance standards to truly get more value from your data? During this webinar, you’ll learn how to empower end-users to make self-service BI a reality within your organization while fostering governance collaboration between all data stakeholders. We’ll discuss and demo: Strategies of consolidating data across silos for fast, flexible access Enabling easy discovery and exploration, including understanding which data to trust and where to start New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go Collaboration and access outside of just SQL for data science and beyond In addition, we will walk through best practices and considerations when developing your organizational strategy around self-service analytics, and highlight several real-world success stories from a wide range of industries. 3 things to learn: Strategies of consolidating data across silos for fast, flexible access Enabling easy discovery and exploration, including understanding which data to trust and where to start New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
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Making Self-Service BI a Reality in the Enterprise
- 1. MAKING SELF-SERVICE BI A REALITY IN THE ENTERPRISE Alex Gutow | Dir. Product Marketing, Cloudera Romain Rigaux | Engineering Lead, Cloudera
- 2. 2 © Cloudera, Inc. All rights reserved. TRENDS DRIVING ANALYTIC MODERNIZATION Self-Service Flexibility Real-time Analysis Hybrid Cloud Converged Workloads
- 3. 3 © Cloudera, Inc. All rights reserved. MOTIVATIONS FOR SELF-SERVICE Easing the relationship between IT and business Faster access and iteration Support for canned reports and exploration Quickly onboard new users and use cases Use existing skills and tools BUSINESS USERS Empower business and remove bottlenecks Ensure priority SLAs are met Contain resources/costs based on system limitations Meet enterprise requirements INFRASTRUCTURE TEAM
- 4. 4 © Cloudera, Inc. All rights reserved. HOW ARE END-USERS ACCESSING AND ANALYZING DATA TODAY? Select all that apply • Pre-canned reports and dashboards • Self-service analytics through BI tools • Direct SQL queries • Python/R • Other
- 5. 5 © Cloudera, Inc. All rights reserved. TRADITIONAL BI Trustworthy but too rigid for exploration Centralized Distributed Context-free Context-rich Traditional Approach Acquire Curate Use Strong for traditional BI and reporting • Highly curated data • Strict data preparation • Restricted access • Highly governed Constrained self-service agility • Limited to structured data • Limited compute types/SQL-only • Long latencies/freshness from ingest to use
- 6. 6 © Cloudera, Inc. All rights reserved. EXPLORATORY BI Flexible but lacks trust Centralized Distributed Context-free Context-rich Pure Exploration Acquire Use Strong for self-service agility • Data is immediately available • Flexible, iterative wrangling/preparation • Support for multiple compute types • Unrestricted access Constrained for traditional BI and reporting • Difficult to comprehend data semantics (3 V’s) • Limited governance • Uncontrolled curation • Limited trust (tribal knowledge) • Disperse storage with redundant data sets due to distributed wrangling
- 7. 7 © Cloudera, Inc. All rights reserved. CONVERGE THE BEST OF TRADITIONAL & EXPLORATORY Centralized Distributed Context-free Context-rich Acquire Use Curate Governance + Self-Service Leveraging governance artifacts for self- service agility • Immediate discovery • As-it-happens trust and governance • Flexible curation and preparation • Automations for scale
- 8. 8 © Cloudera, Inc. All rights reserved. CLOUDERA DATA WAREHOUSE Decoupled architecture on-prem & cloud-native Analytic Workbench: SQL Developers Preferred BI Tools: Analysts Workload 360: Migrate, Analyze, Optimize, Scale Navigator: Trust & Stewardship Apache Impala Query Engine Hive-on-Spark ETL Processing Apache Kudu | HDFS Local Storage AWS S3 | Azure ADLS Object Storage
- 9. 9 © Cloudera, Inc. All rights reserved. WHAT BI/ANALYTICS TOOLS ARE YOU USING? Select all that apply • Tableau • Qlik • Zoomdata • Arcadia • PowerBI • Microstrategy • Cognos • SAS • Hue • SQL Shell • Other
- 10. 10 © Cloudera, Inc. All rights reserved. BRIDGING THE GAP For collaborative governance IT/OPS STEWARD DBA SQL DEV ANALYST
- 11. 11 © Cloudera, Inc. All rights reserved. BRIDGING THE GAP For collaborative governance IT/OPS STEWARD DBA SQL DEVANALYST
- 12. 12 © Cloudera, Inc. All rights reserved. EMPOWER THE BUSINESS with trust and efficiency • Flexibility: Converge all data from all sources • Trusted discovery: Understand what to trust across cloud and on-prem • Query assistance and recommendations: Based on popular values and best practices • Share and go beyond SQL: Extend to third-party tools or data science teams
- 13. 13 © Cloudera, Inc. All rights reserved. DEMO
- 14. 14 © Cloudera, Inc. All rights reserved. CUSTOMER 360 ANALYSIS Analytic Workbench Demo Discover Query Share • Browse all available data • Search in data catalog (data, queries, documents) • Import new data in minutes • Built in best practices • In-editor popular values, risk alerts, and recommendations • Query builder and dynamic dashboards • Navigate, Copy, Download, Export • Share data, queries, and results • Parameterized presentations • Integrated with BI tools & platform
- 15. 15 © Cloudera, Inc. All rights reserved. CUSTOMER 360 ANALYSIS Understand support costs, product usage, time-to-resolution, marketing channel activities Shared Storage S3 | ADLS | Kudu | HDFS Shared Metadata, Security, Governance Opportunities revenue Cases start end Account History id Usage ts Contact Activities type duration Salesforce Usage Logs Marketing Database
- 16. 16 © Cloudera, Inc. All rights reserved. EPSILON Agility Harmony structured + unstructured Annual operational savings Self-service campaign and segmentation development Gives marketers the freedom and flexibility to use customer data how and when they want without roadblocks • Consolidate cross-enterprise data sources • Complete customer 360 with secured data • Easy-to-use frontend • Build segments and campaigns in minutes not hours Industry-leading SLAs on amount of data and segmentation speed • Processing down from 6hrs to 10min • 4000 lines of SQL against millions of profiles and billions of transactions • Guarantee 10M onboarded profiles/hr and 40- 50M transactions/hr
- 17. 17 © Cloudera, Inc. All rights reserved. GLOBAL PHARMACEUTICAL R&D Information Platform Balance curated usage with agile discovery • Consistent, shared data access through BI tools • Interactive query speeds • OLAP capabilities • HIPAA compliant Results: • Reduced cost and time to identify clinical trial groups • Accelerate new drug development • 1st time metrics and monitoring on compliance data structured + unstructured Analytic Users 70% Execs/Managers 25% Analysts 5% Data Scientists Reduction in silos Use cases
- 18. 18 © Cloudera, Inc. All rights reserved. NOVANTAS Metriscape for customer journey analytics Deposits in cross-bank data set Savings for every $1B in deposits Analyzes 1000s of business metrics for each customer Built in governance for trust in agile analytics • Meet regulatory requirements • Auto-generated and user-driven • Includes unstructured call center data • Single platform for train/test models, interactive queries, and visualizations Results: • 50% decrease in marketing execution costs • Limit retention promotional expenses by 10% with only 3% change in churn • Low TCO with hybrid cloud and transient resources Faster time to monetization with hybrid cloud
- 19. 19 © Cloudera, Inc. All rights reserved. THE DATA MANAGEMENT INFRASTRUCTURE MODEL Source: Solve Your Data Challenges With the Data Management Infrastructure Model, Gartner, 2017
- 20. 20 © Cloudera, Inc. All rights reserved. MULTI-USER, MULTI-FUNCTION Support all skills and curate based on known and new data ANALYTIC DATABASE Discovery (raw) ANALYTIC DATABASE Exploration (curated) DATA ENGINEERING Prep - New Report ANALYTIC DATABASE BI/New Reporting DATA SCIENCE Model Build/Test DATA ENGINEERING Prep – Known ANALYTIC DATABASE Regular Reporting Shared Storage (S3, ADLS, Kudu, HDFS) Shared Metadata, Security, Governance
- 21. 21 © Cloudera, Inc. All rights reserved. CLOUDERA ENTERPRISE The modern platform for machine learning and analytics optimized for the cloud Amazon S3 Microsoft ADLS HDFS KUDU SECURITY GOVERNANCE WORKLOAD MANAGEMENT INGEST & REPLICATION DATA CATALOG Core Services Storage Services ANALYTIC DATABASE DATA SCIENCE EXTENSIBLE SERVICES OPERATIONAL DATABASE DATA ENGINEERING
- 22. 22 © Cloudera, Inc. All rights reserved. QUESTIONS? Try the Free Demo: cloudera.com/products/open-source/apache- hadoop/hue.html#demo Get Started in the Cloud: cloudera.com/products/altus/altus-analytic-db.html
- 23. THANK YOU
Notas do Editor
- Last 6mos 5.6M queries 29k users 47 countries
- Created R&D Information Platform (RDIP) Consolidate >8PB of data (structured and unstructured) from 2100 disparate databases 38 use cases initially and now 200+ across 10 domains Results: First time metrics and monitoring on compliance data; reduce time and cost of identifying diverse groups for clinical trials; accelerate new drug development Platform for self-service discovery, analysis, and trusted reporting, accessible by all 8,700 users 70% managers/execs (dashboards); 25% bench scientists (basic analytic skills); 5% data scientists (deep technical analytic skills) Consistent, shared data via preferred BI tools (Spotfire, Excel, Zoomdata), with advanced analytic capabilities (joins, hierarchies, multi-dimensional views) and interactive query speeds Balance strategic/curated usage with agile discovery HIPAA compliant Notes: This slide was created based on the DIA APPLICATION submitted in 2017. Company Background: GlaxoSmithKline (GSK) is a global pharmaceutical company with commercial operations in more than 150 countries, a network of 87 manufacturing sites, and R&D centers in the United Kingdom, the United States, Belgium, and China. Use Case: It can take from six to 12 years to conduct all the steps necessary—from research and testing to clinical trials and regulatory approvals—to bring a new drug or vaccine to market. Once a new product goes to market, pharmaceutical companies have a small window of opportunity to recoup development costs before their patent expires. Adding to the challenge, the cost to produce drugs has remained static in recent years, leading to a considerable reduction in profitability. To combat these pressures, GSK sought to transform how data is used across Research & Development (R&D). With data in silos, it was difficult to take what researchers learned across the R&D pipeline and build on it. To gain the new levels of efficiency and insight it needed to reduce costs and speed development, GSK had to create a platform that would ingest all unstructured and structured R&D data, and deliver greater analytic capabilities. The GSK R&D Information Platform uses Cloudera, partner technologies, and homegrown tools to deliver a holistic view of all data within R&D and give researchers an immense analytic advantage. For example, It previously could take months to run analysis across a collection of clinical trials. Today, researchers can complete the analysis in minutes. Months to minutes drives significant business value. The platform combines more than five petabytes (PBs) across 10 different data domains, including discovery, clinical, genomics, regulatory, safety, and commercial data, and more than 2,100 silos. With privacy and security of vital importance in the healthcare industry, GSK needed to confirm that the platform addressed rigorous industry and internal standards, including the Health Insurance Portability and Accountability Act (HIPAA). By leveraging the Cloudera SDX capabilities, GSK can manage all the metadata and policy information in a centralized fashion. With its new platform, GSK researchers are gaining insights that help streamline every aspect of the R&D process. For example, Scientists can see exactly how long it takes to design, scale and test a potential molecule. This information is critical in helping accelerate the development of new drugs. GSK researchers can also perform association analysis on public genomic data spanning 500,000 people--work that was impractical on its legacy platform. Clinical trial teams can reduce the time and cost of identifying the optimal mix of participants for clinical trials by harnessing the breadth of data and analytics capabilities. And compliance teams have reduced the time and cost of compiling data for regulatory and compliance activities, with access to all required compliance metrics from across the organization. As GSK achieves greater efficiency and new insights across its many R&D processes, executives expect to ultimately move the needle in terms of time-to-market, bringing new drugs and vaccines to market more quickly and less expensively to help patients. Data sources: ● 2,100 databases spanning discovery, clinical, genomics, regulatory, safety, and commercial data Solution ● Modern Data Platform: Cloudera Enterprise ● Workloads: Analytic Database, Data Science & Engineering ● Key Components: Apache HBase, Apache Impala (incubating), Apache Sentry, Apache Spark, Cloudera Navigator, Cloudera Search, Kerberos ● Data Science Tools: Anaconda Enterprise Notebooks by Continuum Analytics, Anaconda Scale by Continuum Analytics, Anaconda Package Manager by Continuum Analytics, Anaconda ● Accelerate by Continuum Analytics, IPython, JupyterHub, RStudio ● Databases: MongoDB ● BI & Analytics Tools: AtScale, Kinetica, SpotFire, Trifacta, Zoomdata ● Data Acquisition and Curation Tools: StreamSets, Tamr ● Implementation Partner: Cloudwick
- Known Data/Known Business Questions — This represents the foundational core of the business. The business problems are known and the sources of data that support their resolution are well-established. Unknown Data/Unknown Business Questions — This is the realm of exploration and discovery. The business value of the data, and therefore the questions it can answer, has yet to be established. Unknown Data/Known Business Questions — In this sector, the value of data has partially been established — in that there is an accepted understanding of the questions it can answer. However, the final sources of insight to support these questions have not yet been established, including the potential for a wide range of emerging external/exogenous data sources (such as open data, data brokers, social data, personal data, and so on). This is where value is established. Known Data/Unknown Business Questions — This sector represents further exploration of known data — What other uses can this data support?