DATAMEER WHITE PAPER Beyond BI Big Data Analytic Use Cases
This white paper discusses the types and characteristics of big data analytics use cases, how they differ from traditional business intelligence use cases and the business benefits and return-on-investment that users can expect from using a big data analytics application including faster time to insights and better decisions based on all available data. Characteristics of Big Data Analytics Big data analytics is revolutionizing how individuals, businesses and government agencies collect, store and analyze data. Driven by the explosion in the volume, variety and velocity of available data, big data analytics is answering new questions as well as providing more accurate and complete answers to questions that have been around for decades. The world has evolved from a transaction-based society to an interaction-based society as we all interact now via email, text and social media outlets far more than we transact. This has created an explosion of data in volumes and types never imagined just a few years ago. The market has responded to big data with new technologies that store and analyze any volume and type of data. The leading platform technology is Hadoop, an open-source storage and compute platform that leverages low-cost, commodity hardware and can linearly scale to 1000 s of servers. Hadoop, together with big data analytic applications that run natively on Hadoop bring dramatic, cost-effective storage and analytics to big data users. These big data analytic applications are key to end-users since Hadoop by itself has no user interface and requires technical coding to perform any integration or analytic operation. Big data analytics that are easy to use by everyone provides the fastest time to insight and enables better decisions based on all of your data. How Business Intelligence and Big Data Use Cases are Different Business Intelligence (BI) BI is proven technology that gathers transaction and related data from relevant databases (requiring an extract, transform and load solution (ETL) when data is stored in more than one database) and then generates reports on that data. The data is usually put into a pre-built schema or data model that helps to overcome traditional hardware limitations by limiting the queries to a set of known questions. BI solutions are very good at generating historical reports from moderate volumes of structured data. Big Data The advantage of big data analytics is centered on its ability to integrate, analyze and visualize all data that enable users to discover the fastest time to new insights they never had before. Driven by Hadoop s linear scalability, the ability to store and analyze any volume or type of data means that users get broader insights across all available data which results in better decisions, better prediction of behavior and more accurate recommendations of future behavior. Hadoop s cost-effective linear scalability also enables schema on read, which provides the fastest time to insights by freeing users from having to pre-model the data. Data Volume Data Types Data Modeling Big Data Large and small All data types Schema on read BI Small Structured data only Pre-model required Beyond BI: Big Data Analytic Use Cases 1
Big Data Use Cases Big data use cases include existing use cases that are enhanced and broadened through the addition of big data as well as new use cases made possible through the use of new data types and volumes. Use cases can be grouped into four general categories, each with a number of individual use cases: Customer analytics/sales/360 view of customer Security, fraud and regulatory Operational analytics Legacy replacement/modernization Customer analytics/sales/enhanced 360 view of customer Enhanced customer analytics/sales/360 view of customers extends and enhances traditional customer analytics by incorporating and analyzing additional data sources. This enables a deeper and more accurate understanding of customers and prospects by correlating behavior, social sentiment, purchase histories, how they shop and what they might purchase or recommend next. Use cases include: Funnel optimization Behavior analytics Product cohort analytics Pricing optimization Ad optimization Product/services recommendations Example CRM use case brand sentiment While customer analytics has been a staple of BI reporting for years, combining new big data with existing, structured data greatly expands the insights that users gain. Common datasets include purchase transactions and history, weblogs, social media data, clickstream data, pricing, customer support and email logs, product conversions, etc. The return on investment (ROI) for customer analytics/sales/enhanced 360 view of customer is very significant. Datameer customers have seen dramatic results including: 300% increase in product conversions 200% increase in revenue 30% decrease in customer acquisition costs Beyond BI: Big Data Analytic Use Cases 2
B I G DATA US E CAS ES Security/fraud/compliance Security, fraud, regulatory compliance and related use cases are extended and enhanced by big data analytics ability to analyze all relevant data sources. Analyzing very large datasets of credit card transactions correlated with authorization codes over many years reveals more precise fraud patterns than is visible when only analyzing a few months of data. Correlating asset databases with trading system logs makes it more difficult for rogue traders to hide questionable assets. Finally, quickly integrating a number of data sources and financial metrics between databases makes it easier for compliance managers to meet financial reporting accuracy requirements in regulated financial markets. Use cases include: Credit card fraud patterns Rogue trading activity Basel III and SOX compliance Risk management Data accuracy metrics Portfolio analytics Example of security/fraud/compliance use case network intrusion The ROI in security/fraud/compliance use cases is obvious, not only in detection and compliance but also in protecting shareholder value and retaining confidence in public markets. Datameer customers have realized significant benefits including: Predicting worldwide security threats within hours Identifying over $2B in potential fraud. Beyond BI: Big Data Analytic Use Cases 3
Operational analytics Insights across business operations are key to operational excellence. Operational analytics correlates data such as transactions or supply chain information with machine logs, sensor data and other datasets to highlight issues of organizational inefficiencies, customer and user experience, service levels and IT infrastructure health. Operational use cases include: IT infrastructure analytics Device analytics SLA analytics Data center analytics Supply chain management Workforce analytics SCADA Smart meter analytics Utility grid analytics Operational use cases may integrate data center weblogs with sensor data to better manage utilization and reduce electric costs. High tech manufacturers may integrate utilization data with machine logs to proactively monitor service agreement levels and recommend proactive maintenance on their hardware devices. In the electric utility industry, integration of Smart Meter data, utility grid data and SCADA data means that managers now have a complete view of operations from generation to usage. Example of operational analytics use case IT infrastructure analytics Datameer customers have realized benefits including: Dramatic decrease in customer churn through predictive support 30% reduction in end-user network failures Beyond BI: Big Data Analytic Use Cases 4
BI/DW Legacy Replacement Modernization / ROI Big data analytics offers a number of advantages over traditional data warehouse and business intelligence technologies in big data analytic use cases including lower cost of ownership, ROI and ability to analyze larger and/or more disparate datasets. Big data analytics are often complimentary to other BI uses so they often work side-by-side and share data in enterprises. Hadoop s linear scalability cost-effectively accommodates any data size, eliminates the need to pre-model data and can analyze structured, semi-structured or unstructured data. These factors make Hadoop-based analytics ideal for: Modernization of mainframe reporting Enterprise analytics data hub Offloading of new structured and unstructured analytics from existing data warehouses Example of DW modernization use case clickpath analytics ROI for BI/DW modernization is centered on cost-effective, scalable hardware and decreased time to insight versus traditional BI systems. For example, a major national retailer reduced their reporting times from 12 weeks to 3 days with Datameer and Hadoop for market basket analysis and pricing optimization. Conclusion Big data analytics is changing the way enterprises and individuals gain insights from data. These insights are driving increased revenues, lowering costs, detecting fraud and providing a more complete view of prospect and customer behavior. In some areas such as social sentiment, these use cases are new. In other instances, existing use cases have expanded as new data sources become available and can be correlated for broader, more complete insights. The key to maximum benefit from big data analytics is the application of the appropriate technologies to a given use case. Big data analytics is not, in most cases, a replacement for traditional BI. For BI use cases that need to analyze moderate amounts of structured data, BI technologies are mature, well proven and appropriate. However, for use cases that involve analysis of large datasets, and or contain unstructured data or structured and unstructured data together, big data analytics is the clear choice. Beyond BI: Big Data Analytic Use Cases 5
ABOUT DATAMEER Datameer Datameer s big data analytics solution is designed for business users and addresses a wide range of big data use cases. To learn more about Datameer, go to www.datameer.com/solutions. Datameer s big data analytics and discovery solution provides comprehensive functionality to integrate, analyze and visualize any volume and variety of data. To get a first hand look at Datameer, a free trial is available at www.datameer.com/datameer-trial.html. Datameer s Analytic App Market provides pre-built applications that can be used alone or combined to address common big data analytic use cases. The App Market enables anyone to simply browse, download an app, connect to data, and get instant results. Examples of apps applicable to the use cases cited above include: Twitter Brand Sentiment Google AdSense Marketo Metrics Salesforce.com Sales Pipeline Network Intrusion Detection Email Analytics Website Traffic Zendesk Tickets JIRA Tickets Amazon EC2 Monitor To learn more about the Datameer Analytic App Market, go to www.datameer.com/apps. San Francisco 1550 Bryant Street, Suite 490 San Francisco, CA 94103 USA Tel: +1 415 817 9558 Fax: +1 415 814 1243 New York 9 East 19 th Street, 5 th floor New York, NY 10003 USA Tel: +1 646 586 5526 Halle Datameer GmbH Große Ulrichstraße 7-9 06108 Halle (Saale), Germany Tel: +49 345 279 5030 Website www.datameer.com Twitter @Datameer Download free trial at datameer.com/datameer-trial.html 2015 Datameer, Inc. All rights reserved. Datameer is a trademark of Datameer, Inc. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. Other names may be trademarks of their respective owners.