Forrester Newsletter - Solix Common Data Platform Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseSOLIX COMMON DATA PLATFORM:Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseEXECUTIVE SUMMARY“You cannot manage what you cannot measure.”Those prescient words from management guru Peter Drucker more than 30 years ago encapsulated theevolution of enterprise software platforms and have paved the path to the era of the data-driven enterprise.Data is remaking industries and reshaping the global economy. Those who embraced data found growthareas and improved earnings. Organizations that ignore the promise of data can no longer survive in thenew world economy.Today, Drucker’s words are as true as ever. To continue being data-driven, organizations must be able toingest and analyze new forms of data from constantly developing new sources. Those who are ready to minenew data streams, such as social, IoT and more, are primed to transform economies and reap the benefitswith new growth opportunities. Businesses ready to leap into the fray are adopting Big Data. Big Data bringstogether structured, semi-structured and unstructured data. When all forms of data are brought together,it not only multiplies the value of every single piece of data, but it also presents a new set of challengesaround data storage, governance and consumption.In today’s enterprise world, business users want to make real-time, data-driven decisions using the vastamount of data available. Yet, IT departments are faced with the challenge of increasing storage andBusiness Intelligence (BI) costs, complex governance and Information Lifecycle Management (ILM) for data,which is now in the scale of petabytes and beyond. Unfortunately, current enterprise ready technologyofferings are not capable of managing this data tsunami, let alone take advantage of all the possibilities thisdata offers. The tension created within organizations is clear.“ As Forrester also points out in its research report, in the era of Big Data, traditional EDW is failing to meet new business requirements, such as support for real-time and ad hoc customer analytics, new sources of data, and self-service capabilities.1The Solix Common Data Platform (CDP) allows organizations to embrace Big Data, while keeping thechallenges in check. The Solix CDP helps organizations leverage their existing infrastructure and allowsthem to collect, store and analyze massive amounts of data from every source without sacrificinggovernance, security or management. Further, with the Solix CDP all data keeps its original context andstructure, allowing organizations to ask complex questions and gain deep contextual insights from data atany point. The Solix CDP creates a new paradigm fostering a meaningful, frictionless partnership between ITdepartments and business users. IT departments can now become the guardians of data and business userscan become the owners and direct consumers of data.Solix created the CDP to bring ILM to the Data Lake and innovation to the EDW. The Solix CDP is the nextevolution in the new enterprise blueprint, offering Enterprise Data Archiving and Enterprise Data Lake tocreate an Advanced Analytics platform with unprecedented levels of ILM in a Big Data setting.1 Forrester Report on The Next-Generation EDW is the Big Data Warehouse, August 2016 1 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseINTRODUCTION — SOLIX COMMON DATA PLATFORM ARCHIVINGENTERPRISE ENTERPRISE DATA LAKE ----------- ------------------------------------------------------------- SOLIX COMMONINFOR DATA PLATFORM------------------------ MATION GOVERNANCE Solix CDP = Enterprise Archiving + Enterprise Data Lake + Information GovernanceAt the core of the Solix CDP are Enterprise Archive and The Enterprise Data Lake.The Solix CDP utilizes the Solix Big Data Suite to provide comprehensive enterprise data management androbust ILM. With the CDP, organizations can vastly expand the reach of analytics by creating an AdvancedAnalytics platform. For the CIO, Enterprise Archiving offers a quick ROI that will ensure budgetary supportfrom the organization and dissolves the obstacles between Big Data and ILM.The Solix CDP brings enterprise-grade capabilities to the Hadoop framework, addressing all shortcomingsof the Data Lake. Solix CDP provides uniform data collection, metadata management, ILM and secure dataaccess for Advanced Analytics.The Solix CDP does this all while maximizing an organization’s existing infrastructure. With no need toreboot the organization’s enterprise architecture, the Solix CDP harnesses the current architecture todevelop a new enterprise blueprint, capable of evolving with the business requirements of an organization.The Solix CDP is also capable of evolution. As businesses stretch Hadoop to its limits, new Big Datatechnologies will emerge. The Solix CDP is primed to adapt with them. The Solix CDP brings enterprise-grade capabilities to the Hadoop framework, addressing all shortcomings of the Data Lake.2 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseWHY SOLIX COMMON DATA PLATFORM?The need to become data-driven is clear. Transformation has hit every major industry and disruptors havebecome powerhouses in the global economy based on their capabilities to mine data. Any organizationwanting to compete must become data-driven or it is destined to fail.The current enterprise architecture offering, EDW, provides a canonical, top-down view of enterprise data tomeet end user requirements, but those views rarely satisfy the function-specific requirements of data-drivenapplications.“ As per Forrester research, Big Data platforms such as Hadoop have made Big Data architectures more affordable, allowing companies to pursue new business insights for increased data-driven competitive advantage.2The Solix CDP, built on top of Hadoop distributions, enables data-driven organizations to gain more valuefrom their data because now data can be visualized in more specific ways.The cost of relying on the EDW to collect and analyze all of this data would also exceed the budget of mostorganizations. The Solix CDP is a uniform data collection system for structured, unstructured and semi-structured data featuring low-cost data storage and Advanced Analytics. Solix CDP stores data “as-is” toreduce costly Extract, Transform and Load (ETL) operations, as well as transforms data to feed downstreamNoSQL and analytics applications. Solix CDP enables organizations to create a true enterprise Data Lakewith full access to the data, rather than a data swamp where the data gets lost. This enables the CIO to finda better solution than trying to collect and store all of the enterprise data in the expensive Tier 1 storageand existing EDW architectural offerings.The Solix CDP does not require costly infrastructure and offers the scalability and flexibility the Big Dataplatform architecture provides, along with enterprise-grade governance and security. The Solix CDP laysthe foundation for information governance, efficient infrastructure utilization and Advanced Analytics atpetabyte scale. The Solix CDP lays the foundation for information governance, efficient infrastructure utilization and Advanced Analytics at petabyte scale.2 Forrester Report on Big Data Fabric Drives Innovation and Growth, March 2016 3 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseHere is the comparison on how Solix CDP differs from a traditional Data Warehouse and aData Lake: Data DATA WAREHOUSE DATA LAKE SOLIX CDP Schema Processed, Structured Storage Costs Structured, Semi-structured/ Processed, Structured, Semi-structured/ Scalability On write Unstructured Unstructured Agility High Metadata Repository Low On read On read / On write Data Access Query Performance Low, Fixed configuration Low LowSecurity / Governance Centralized MetaData Repository High High Users Query Role based Access High High, Configure & Reconfigure High, Configure & reconfigure Mature ILM Business Users No Centralized MetaData RepositoryRegulatory Retention Yes No Search Query + Search Management No Legal Hold No Medium Medium ROI High Maturing Mature Data Scientists Business Users, Data Analysts, Data No Scientists Yes No Yes No Yes No Yes Low High 4 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterprisePRODUCT/ SOLUTION OVERVIEW OF THE SOLIX COMMON DATA PLATFORMBuilt on top of Hadoop distributions, such as Cloudera CDH or Hortonworks HDP, the Solix CDP provides anintegrated suite of enterprise connectors through its Object Workbench to build a consolidated repository ofenterprise data and metadata. The Solix Analyst Workbench allows multiple teams and users to collaborate,create virtual workspaces and projects to access the data without compromising on compliance andsecurity. Because it runs on top of both of the most popular Hadoop distributions, it eliminates one of thebasic questions behind the creation of a Hadoop stack, and it can bridge the two in environments whereboth are being used.The data-driven enterprise does not wait for the business question to develop and then use the data toanswer it. The data-driven organization uses Advanced Analytics and Business Intelligence to mine the datafor the questions and then the answers. With Solix CDP, business users can create data models and derivethe insights needed to move the organization forward. The self-service model takes IT out of the equation,freeing it to focus on its work, while ensuring security and governance measures are also met. The Solix CDPbrings robust ILM to all data.The Solix CDP ensures all data retains context by retaining its metadata, meaning its value is never lost.This ensures the business questions being raised by the data are truly valid and the answers analysts findare relevant. The Solix CDP ensures all data retains context by retaining its metadata, meaning its value is never lost. 5 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseEnterprise Archiving“ In the era of Big Data, Archiving is a No-Brainer Investment.3Up to 80 percent of production data used by core applications is inactive. Data archiving has emerged asan ILM best practice to meet data growth challenges. Solix CDP ensures that Enterprise Archiving improvesproduction application performance, reduces infrastructure costs and meets the regulatory and complianceneeds.As part of Enterprise Archiving, application data running online is first moved into Tier 2 or Hadoopinfrastructure, and then purged from its source location, according to ILM policies. Data archiving bestpractice requires that MOVE and PURGE processes be coordinated and validated. Enterprise Archivingon Solix CDP ensures proper data governance since enterprise data is ingested and stored based on ILMretention policies and business rules.Archive data is classified for security and compliance requirements, such as legal hold, and universal accessis provided for business users through structured reports and full text search for business objects. Active Data Semi-Active Data InActive Data Reporting / BI ToolsStructured Data (RDBMS) (Hadoop ) Universal Access Solix APM BI Reporting (Repository, Query, Search) Analytics Native SOLIX COMMON DATA PLATFORM Access Custom Apps Solix EDMS Archive Database Solix Big DataSemi/Unstructured Data Database Suite Archiving DB ArchivingVIDEOS EMAIL MACHINE MOVE & COPY DATA Enterprise Business Record Solix Print Stream Capture BigData Search & Query Access Retention Management and Legal Hold SuiteIMAGES FILE XML MOVE, COPY, PRINT SHARE Solix CDP ensures that Enterprise Archiving improves production application performance, reduces infrastructure costs and meets the regulatory and compliance needs.3 Forrester Report on Vendor Landscape: Big Data Archiving, August 2015 6 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseEnterprise Business Record (EBR)An EBR is a de-normalized, point-in-time snapshot of a business transaction, which may include structuredor unstructured elements. The Solix CDP helps model, ingest and manage EBR data into a Hadoop optimizedfile format that is fully accessible for text search or structured query.Data can be ingested to build both a long-term Enterprise Archive and a transient Enterprise Data Lake. Forthe archiving use case, older inactive data is moved from the source application to the Solix CDP. For theData Lake use case, current data can be transformed and then copied from the source application to theSolix CDP. LDAP/Active SAP BO Directory Oracle BI Crystal Reports Role Based Security Dashboards Retention Search Reports IBM Cognos Policies BI Reports 0 – 3 Years Solix Application Portfolio ManagerEnterprise Application Enterprise Business Record 3 – 10 Years AR Invoice Solix Big Data Suite Transactions Master Data Create Ingest After EBR into SBDS Retention Reference Data Attachments Complete business object Search & Query Report Files Denormalized structure Retention Management Point-in-Time snapshot Legal Hold Decoupled from application Scheduler & CLIEDW AugmentationCurrently enterprises are struggling to maintain costs associated with both storage and processingcapabilities around traditional EDW implementations. Offloading storage as well as costly ETL functions to acommodity hardware such as Hadoop enables enterprises to focus on utilizing the existing Data Warehouseinfrastructure to its best ability in doing BI and Advanced Analytics.Migrating warm or cold data from the EDW via archive onto low cost bulk storage system such as Hadoopenables organizations to save millions on storage costs and significantly speeds up the processing power toget more value from the data warehouse by extracting valuable insights at a quicker pace from the collecteddata. Currently enterprises are struggling to maintain costs associated with both storage and processing capabilities around traditional EDW implementations. 7 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseEnterprise Data Lake and Advanced AnalyticsThe Solix CDP based on Apache Hadoop establishes new capabilities for Advanced Analytics applications.It stores data “as-is” eliminating the need for demanding ETL processes during ingestion. It captures andmaintains the metadata connected to each byte of data, which is half or more of the value of the data itself.The Enterprise Data Lake may then be mined for critical business insights using text search, structured queryor further processing by downstream analytical applications. The Solix CDP utilizes either Hive or Sparkquery frameworks dependent on the user requirements.The Solix Enterprise Data Lake reduces the complexity and processing burden of staging EDW and analyticsapplications and provides highly efficient, bulk storage of enterprise data for later use. Once resident withinHDFS, enterprise data may be more easily distilled and better described at petabyte-scale by businessanalytics applications. This allows organizations to develop an enterprise architectural strategy that isresponsive to the business stakeholders without driving up the investment in hardware and software. STRUCTURED DATACLOUD APPS ERP CUSTOM APPS ANALYTICS DATA MINING REPORTINGDATA WAREHOUSE CRMSEMI STRUCTURED DATA DISCOVERY SEARCHJSON XML CSV DATA MART STAGELOGS MACHINE DATA SENSORS TRANSFORMUNSTRUCTURED DATA ARCHIVE DATA LAKEAUDIO VIDEOS SOCIAL MEDIA IMAGESEMAIL DOCUMENTS WORD DOCUMENTSInformation GovernanceAnalysts have warned that applying existing information governance practices to Big Data will result infailure. Comprehensive information governance provided by Solix CDP establishes the control frameworknecessary for proper data access control, data assessment, data discovery, data classification, datavalidation, retention management, legal hold and privilege management. 8 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven Enterprise“ Forrester estimates that the average Hadoop repository doubles in size every year; some implementations double in volume every month. More Hadoop silos are creating data challenges around security, integration, governance and delivery.4To achieve robust ILM, new security and governance measures must be put into place to match the varietyand complexity of the new data assets. The Solix CDP provides a true ILM continuum that addresses thecomplexity of governance in the Big Data world, while ensuring governance for core enterprise applicationsis not sacrificed. The Solix ILM framework manages the data within HDFS and provides an integratedretention-management and legal-hold capabilities.Structured and unstructured data from various data sources are migrated into HDFS with full data-validationand audit reports. These reports provide the necessary defensibility and chain of custody for complianceand data governance. ILM policies and business rules may be pre-configured to meet industry standardcompliance objectives, such as COBIT, or custom designed to meet more specific requirements.INFORMMAAosnTsietIoOsrs, N GOVERNAN CEHold,RetDiasipn,ose CreateClassify CONTROL AUDITEncSreycputre, RECONCILE Archive, RetireAdditionally, ILM also helps to solve the data growth problem by moving less frequently accessed data fromhigh-cost Tier 1 infrastructure to Hadoop, leveraging cheap commodity infrastructure. Relocating inactivedata to low-cost bulk data storage creates enormous infrastructure cost savings. Because governance, riskand compliance concerns grow by the terabyte, the Solix CDP ensures ILM for data throughout its lifecycle.4 Forrester Report on Big Data Fabric Drives Innovation and Growth, March 2016 9 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseCOMPONENTS OF THE SOLIX COMMON DATA PLATFORMSolix Object WorkbenchIntegrated ConnectorsSolix Object Workbench provides integrated connectors that can extract and ingest vast amounts of data“as-is” from an extensive set of enterprise data sources, including structured, semi-structured, unstructuredand streaming data sources. The Object Workbench provides functionality to copy, move, and transform datafrom various data sources into the Solix CDP.Extract, Transform and Load (ETL)The Solix CDP Object Workbench also enables the ETL process to be undertaken as data is moved into theEnterprise Data Lake. This provides the ability to transform complex application data into meaningful data ina ready-to-use format from which the business user can gain immediate insight, with the use of BI tools.Solix Virtual PrinterThe Solix Virtual Printer provides functionality to capture print stream output from any application,transform it into a PDF document, automatically ingest it into Hadoop, index it and make it available forsearch access with full role-based security. 10 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseERP | CRM | HR | Custom Applications Solix Big Data Suite DISCOVERY SEARCH Print Steam STAGE Solix Virtual Printer TRANSFORM ARCHIVE Data LakeThe virtual printer can be used to supplement an archiving project by capturing key report output —including all formatting — from the source application and storing it alongside the structured data.The virtual printer can also be used to support a streamlined archiving approach called “print-and-purge.”Using this approach, key documents, such as invoices or customer documents, are first “printed” by theSolix Virtual printer and ingested into Hadoop, after which the underlying data from the source applicationcan be purged.Real-Time and Streaming Data“ Business users want data that’s integrated in real time from multiple sources, including legacy data, social media, sensor data and weblogs, so they can make better decisions and increase their company’s competitiveness.5Insights from enterprise data is now not restricted to formulated data repositories, which only containdata at the end of its operational life. Huge amounts of data are now collected from both internet enableddevices and also from terminals in real-time and streaming formats.This data can also be captured via the Solix CDP Object Workbench, enabling teams to create views of datato analyze and deliver actionable intelligence. This will enable enterprises across industries to become trulydata-driven.5 Forrester Report on Big Data Fabric Drives Innovation and Growth, March 2016 11 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseSolix Analyst WorkbenchThe Analyst Workbench is designed for business analysts, data scientists, and DBAs to securely access thedata within the Solix CDP and build virtual workspaces to manage analytics projects. All data within theplatform is automatically made searchable and reportable in a secure and governed manner.Functionality included with the analyst workbench includes:Data Lake VisualizerThe Data Lake Visualizer is a graphical inventory of the data contained in the lake. Using the visualizer thedata analyst can quickly find the data sets needed to complete their analytics assignment. Once the datasets are identified they can be selected for inclusion in the analytics project. 12 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseVirtual Projects and WorkspacesFor each analytics assignment a virtual project can be created by the analyst. Within each project one ormore virtual workspaces can be created. The objects identified in the visualizer can then be virtually copiedinto the workspace, eliminating the need to make physical copies of data.Once the virtual workspace has been created, the data analyst can do data mashups by creating newcomposite objects to support the analytics assignment.Data PreparationData preparation is the foundation for becoming a data-driven organization. To properly use data it mustnot only be collected from its ever-increasing variety of sources, it must also be put into a repository wherethose varied forms can be used by the analysts. As more organizations utilize Data Scientists and Advanced Business Analysts to wrangle their data to enable digital transformation, the lack of proper tools hinders this progression. In fact, research shows that Data Scientists spend 80 percent of their time just cleaning data.Every analytics project requires the proper preparation of the data set. The nature of the data — fromsemi-structured data (such as log files), unstructured data (such as social, IoT) and structured data (such asrelational databases) – must be understood, organized and transformed quickly and efficiently. The SolixCDP offers powerful, easy to use self-serve data preparation capabilities, including the ability to parse, clean,join and enrich data, as well as populate missing information and calculate new metrics.The Solix CDP utilizes the Spark framework. Spark runs in-memory within the cluster and provides machinelearning capabilities for faster and more advanced data preparation.Search and Reporting FunctionalitySolix CDP supports universal access to all enterprise data on a petabyte scale via text search, structuredquery or further processing by downstream analytical applications. End users gain improved data-drivenresults because their data is better able to be described. 13 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseInformation GovernanceThe Solix CDP provides the ability to govern all of the data within the Hadoop repository for complianceand security. For example, automatically purge data based on a time-horizon, apply legal holds on filesand transactions, enforce Kerberos/LDAP authorization for user access and more. This level of security andgovernance is able to be maintained because the CDP ensures all data retains its metadata.Information governance establishes the control framework necessary for proper data access control, dataassessment, data discovery, data classification, data validation, retention management, legal hold andprivilege management.Solix Common Data Platform APITo enable development of custom applications and integration with existing BI and Advanced Analyticstools, the platform provides extensive APIs to access the unified repository. The API allows users toseamlessly access data from the Data Lake to enable the data-driven enterprise.Solix App StoreThe Solix App Store makes inductive BI user-friendly. The App Store offers out-of-the-box analytics throughpre-integrated applications and also offers the opportunity to utilize third-party apps.SAeda-rhcohicanrngedpToorotlisng CaAsnha-lflyotiwcs ------------ SAanleasly/ptiicpseline ------------------------ ------------ SMoacirakleAtinngalyatnicds CURRENT ------------------------ SOLIX ANALYTICS APP STORE OFFERSDAanshebxoecaurdtive ------------------------ IsnsttauencghdSraapatrlsiduoTnnBakIbw,tleoiettohaclu.s,14 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseDEPLOYMENT MODELSThe Solix CDP is enabled for a number of deployment models including bare metal infrastructure, datacenter deployment, cloud infrastructure and also hosted multi-tenant deployment.Solix never delivers a one-size-fits-all solution. The Solix team has experts ready to address customer needsfrom IT, business use and financial perspectives. The Solix team will work to understand organizationalneeds and then implement the best solution.SPARK ON SOLIX CDPThe Solix CDP utilizes the Spark programing models. Spark runs in-memory within the cluster and does notdepend on the two-stage Hadoop MapReduce paradigm. Therefore, repeat access to data is much faster.Spark relies on HDFS and runs on Hadoop YARN to be able to analyze the data stream.Solix is committed to adding support for new Big Data tools as they appear in the fast-evolving Big Dataecosystem. This future-proofs Solix CDP installations, an important commitment given the speed with whichthe open source Big Data stack is evolving. It also simplifies the creation and evolution of an enterprise’sBig Data environments.BENEFITS OF THE SOLIX CDP The benefits of the Solix CDP include: • Combining the advantages of Hadoop with the ability to preserve the full metadata. • Providing advanced ILM capabilities, including the ability to copy data from the data warehouse and to archive older data. • Supporting advanced data security, as well as third party analysis packages, including machine learning and cognitive computing analysis of the data. • Preserving all data in its original format and with full metadata and supporting established open standard interfaces. It future-proofs the Data Lake, ensuring the data will be usable by the new technologies and for new use cases that are as yet undefined. • Providing a unified data governance layer from the time of data ingestion to use of data by business users for operational insights and Advanced Analytics. • Ability to utilize either Hive or Spark query frameworks dependent on the user requirements. • Cloud, on-premise and hybrid deployment models. • Working with all Hadoop distributions such as Cloudera and Hortonworks. 15 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven EnterpriseThe Solix CDP is the first solution to address all the data needs of an organization. From governanceto analytics, the Solix CDP works with an organization’s existing infrastructure to create a true ILMcontinuum that ensures the onslaught of data can be an asset and not a hindrance to business growth anddevelopment. The Solix CDP brings together the Enterprise Archiving, EDW and the Enterprise Data Lakewhile preserving metadata, allowing for schema on read, analytics opportunities, low cost implementationand maintenance as well as offering incredible scalability.CONCLUSIONThe era of game-changing digital disruption is here, and to thrive in this competitive environment,organizations need leadership that can effectively leverage all the data to derive actionable insights to fuelgrowth.Reducing infrastructure costs, attaining operational efficiencies and deriving insights from BI and AdvancedAnalytics is the desire of many organizations. Solix CDP maximizes the insights that can be achieved, whilereducing risk, ensuring compliance and governance to create a true ILM framework to lead organizationsinto the future. The Solix CDP gives organizations all the tools necessary to lower the total cost ofownership and satisfy the desire for return on investment. 16 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

Empowering the Data-driven Enterprise Empowering the Data-driven EnterpriseSolix Technologies, Inc.4701 Patrick Henry Dr., Bldg 20Santa Clara, CA 95054Toll Free: +1.888.GO.SOLIX (+1.888.467.6549)Telephone: +1.408.654.6400Fax: +1.408.562.0048URL: http://www.solix.comCopyright ©2016, Solix Technologies and/or its affiliates. All rights reserved.This document is provided for information purposes only and the contents hereof are subject to changewithout notice.This document is not warranted to be error-free, nor subject to any other warranties or conditions, whetherexpressed orally or implied in law, including implied warranties and conditions of merchant- ability orfitness for a particular purpose.We specially disclaim any liability with respect to this document and no contractual obligations are formedeither directly or indirectly by this document. This document may not be reproduced or transmitted in anyform or by any means, electronic or mechanical, for any purpose, without our prior written permission.Solix is a registered trademark of Solix Technologies and/or its affiliates. Othernames may be trademarks of their respectively. 17 Solix Common Data Platform: Advanced Analytics and the Data-Driven Enterprise

For Enterprise Architecture ProfessionalsThe Next-Generation EDW Is The Big DataWarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analyticsby Noel YuhannaAugust 29, 2016Why Read This Report Key TakeawaysEDW is not dead; it’s evolving! Enterprise data Without Modernizing Your Current EDWwarehouses have come a long way in delivering Platform, You Will Likely Failvalue by predicting trends, minimizing churn, Business users are demanding faster, more real-and identifying new business opportunities. time, and integrated customer analytics fromHowever, in the era of big data, traditional EDW is multiple sources, so they can make better decisionsfailing to meet new business requirements, such and increase their company’s competitiveness.as support for real-time and ad hoc customer Current EDW platforms have gaps and limitationsanalytics, new sources of data, and self-service that fail to meet these new requirements.capabilities. Enterprise architects should read thisreport to learn how the new big data warehouse Forrester’s Big Data Warehouse Strategyaddresses these gaps by delivering timely and Extends The Existing EDW Frameworkactionable insights to gain competitive edge and Based on interviews of customers and vendors,enable innovation and growth. Forrester has laid out an architecture to guide enterprise architects in creating a big data warehouse framework tailored to their firm’s requirements to support both existing and new actionable business insights. You Need A Big Data Warehouse Strategy To Succeed Big data warehouse is a modern data warehouse architecture that leverages traditional and new data repositories, in-memory, cloud, and other technologies.forrester.com

For Enterprise Architecture ProfessionalsThe Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics by Noel Yuhanna with Gene Leganza and Shreyas Warrier August 29, 2016Table Of Contents Notes & Resources 2 EDW Has Been The Analytics Platform King Forrester interviewed various customers in the For Decades financial, oil and gas, retail, and healthcare sectors. But New Business Requirements Are Changing EDW Requirements Related Research Documents EDW Technology Gaps Are Making Big Data Fabric Drives Innovation And Growth Enterprises Look Elsewhere The Forrester Wave™: Enterprise Data 6 The Big Data Warehouse Extends The EDW Warehouse, Q4 2015 Platform TechRadar™: Big Data, Q1 2016 Big Data Fabric Connects The Superset Of Your Data Sources — Including Your BDWs The BDW Provides A Comprehensive View And Integrated Analytics10 The Major EDW Vendors Provide BDW Components BDW Use Cases Go Beyond Traditional AnalyticsRecommendations12 Extend Your Current EDW Platforms Toward A BDW Strategy13 Supplemental MaterialForrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA 02140 USA+1 617-613-6000 | Fax: +1 617-613-5000 | forrester.com© 2016 Forrester Research, Inc. Opinions reflect judgment at the time and are subject to change. Forrester®,Technographics®, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of ForresterResearch, Inc. All other trademarks are the property of their respective companies. Unauthorized copying ordistributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsEDW Has Been The Analytics Platform King For DecadesThe enterprise data warehouse is an architecture, not a technology. The traditional EDW platform hasserved and continues to serve a broad range of business users, including enterprise architecture (EA)pros, feeding both analytical and operational systems. EDWs: ›› Organize and aggregate historical analytical data from functional domains. EDWs house information from data subject areas such as customer, manufacturing, finance, and human resources that align with key processes, applications, and roles. Most of the traditional EDW platform has been built using relational database management system (DBMS) and columnar database platforms using extract-transform-load (ETL), change data capture (CDC), and replication technology (see Figure 1). ›› Offer a strong decision support framework. EDWs provide in-database analytics, predictive models, and embedded business algorithms to drive business decisions. ›› Are central to a firm’s data ecosystem. The EDW is a proven ecosystem that supports integration with data models and security frameworks, automation, and a broad range of business intelligence (BI) and visualization tools.1 ›› Provide the foundation for BI. EDWs support timely reports, ad hoc queries, and dashboards and supply other analytics applications with trusted and integrated data. Many use the EDW to deliver operational intelligence — in the form of query responses, reports, dashboards, charts, and other analytic views — in support of various decision scenarios.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsFIGURE 1 The Traditional Enterprise Data Warehouse PlatformSource Storage/persistence Compute/processingOLTP Relational • Modeling Business intelligence CRM Columnar • Data quality Operational ERP • Security governance reportingSocial SaaS transformation Analytics • Integration Predictive analytics ETL/CDC/replication On-premises Hybrid CloudBut New Business Requirements Are Changing EDW RequirementsToday, business users are demanding real-time analytics that’s integrated from legacy, social, andcloud sources, while business execs want self-service and autonomous access to fit-for-purposecustomer data insights. In our 2016 global survey, 59% of respondents stated that leveraging big dataand analytics was a critical or high priority (see Figure 2). But increasing data volume and dealing withmultimodel customer data are slowing down timely analytics and putting constraints on traditionalwarehouse platforms, causing firms to revisit their EDW architectures. Businesses are reporting thatcurrent EDW platforms: ›› Can’t share current data quickly enough for timely business decisions. With increasing big data comes a major challenge for any enterprise: knowing what to look for and where, and then making sense of it. In our survey, 30% of businesses reported growth of data volume and variety affecting their BI strategy (see Figure 3). Firms are realizing that traditional data warehouses fall short when it comes to real-time analytics.2 “With data explosion and increasing demand for real-time analytics by the business, we are finding it challenging to support our LOB users. While we already use Hadoop, our traditional data warehouses still are important for analytics, but we are now looking at modernizing that architecture.” (Enterprise architect, oil and gas, North America) ›› Don’t support ad hoc and dynamic analytics for new customer trends. EDWs were built for a limited set of uses, providing answers to known questions. But 27% of enterprises report that fast- changing analytics and reporting requirements are one of the biggest challenges when orchestrating© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics their BI strategy, while 30% cite the growth and variety of their data. Processes using traditional EDWs don’t scale well when you introduce ambiguity or add new and dynamic questions. EDWs need to ingest, process, and curate data continuously and support dynamic insights. “We are now looking to [build] a modern data warehouse that can provide insights to all kinds of tough questions critical for our business to succeed. Including identifying business risks and opportunity.” (Business analyst, financial services, Europe)›› Don’t provide a self-service platform for strategic and operational decision-making. When executives need to determine why something is happening or what the best course of action is, they can’t wait for a data processing cycle to make data available. Analysts need to be able to aggregate and prepare data sets without technology management’s involvement. Twenty-seven percent of companies reported lack of end user self-service capabilities as one of the biggest challenges in executing their BI strategy. Self-service customer analytics has become critical for organizations to succeed. “Self-service for all data is our long-term strategic direction, and we know it’ll take us some time to get there, but we have to start somewhere. We have started to integrate our current EDW appliances to Hadoop and in-memory to create [a] unified and integrated analytical platform.” (Enterprise architect, financial services, North America)FIGURE 2 Big Data And Analytics Have Become A Priority “Which of the following initiatives are likely to be your organization’s top business priorities?” (Better leverage big data and analytics in business decision-making) Don’t know 1%Critical priority 19%High priority 40%Moderate priority 28%Low priority 9% Not on our agenda 0% 4 Base: 3,343 data and analytics decision-makersSource: Forrester’s Global Business Technographics® Data and Analytics Survey, 2016 © 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsFIGURE 3 Data Growth And Variety Are Affecting Business Intelligence And Analytics Strategy“What are the biggest challenges your firm faces when orchestrating its business intelligence strategy?”Data security and privacy 35%Growth of data volume/variety 30%Fast-changing analytic and reporting requirements 27%Lack of alignment between IT and business 25%Lack of adequate user training 25% Poor data quality 25%Inadequate or missing relevant internal skills 22% 22% Legal and regulatory compliance 21% Lack of data standards 20% Lack of end user self-service capabilities Lack of access to data and insights 19% 17%Inadequate change management programs 17% (communications, incentives, etc.) 16% Widespread utilization of insights for decision-making and planningLack of business C-level executive supportDon’t know/does not apply 4% Base: 3,343 data and analytics decision-makersSource: Forrester’s Global Business Technographics® Data and Analytics Survey, 2016EDW Technology Gaps Are Making Enterprises Look ElsewhereWhile traditional data warehouses often took years to build, deploy, and reap benefits from, today’sorganizations want more simplified, agile, integrated, cost-effective, and automated solutions. Firmsare revisiting their EDW strategies, as they spend too much time loading, unloading, transforming,securing, integrating, and curating customer data. Enterprises face:© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics ›› A data volume explosion that’s affecting customer analytics. Traditional structured data continues to grow rapidly, slowing down legacy data warehouse systems and affecting analytics and timely insights. Regulatory requirements now mandate storing compliant data for several years, and business growth is generating more data at a faster pace than ever before. “We are experiencing tremendous data explosion for traditional data sets that’s impacting our data warehouses. While we are still looking at improving the performance of existing data warehouses for the short term, we are now starting to look at alternatives, both supplementary and replacement as longer-term strategy.” (Enterprise architect, oil and gas, North America) ›› Data variety that’s making it harder to support using traditional warehouses. Business users can’t easily spot patterns and trends in content such as documents, email, images, audio, and social media. In addition, storing, processing, and accessing unstructured data in data warehouses pushes the limits of traditional technologies and architectures, which were not designed to handle such data types.3 ›› Data speed that’s making it harder to keep up. New sources of data are coming in a lot faster, such as sensor and machine data, log and clickstream data, cloud and software-as-a-service (SaaS) data, and other streaming data. Storing, transforming, and processing such data requires new technologies and systems to support new customer analytics, real-time analytics, and operational intelligence reporting.4 “For us, real-time data sharing is critical internally among business users but also with various partners that we engage with. Currently, not all of our data is available to everyone, but we are looking at ways of expanding to support a more self-service real-time big data platform.” (Data scientist, biotechnology company, North America)The Big Data Warehouse Extends The EDW PlatformFirms are already using a variety of technologies in their big data strategy to support new, next-generation analytics (see Figure 4). The big data warehouse (BDW) is a modern data warehousearchitecture that leverages traditional data warehouse architectures as well as modern big datatechnologies (see Figure 5). Forrester defines the big data warehouse as: A specialized, cohesive set of data repositories and platforms used to support a broad variety of analytics running on-premises, in the cloud, or in a hybrid environment. BDW leverages both traditional and new technologies such as Hadoop, columnar and row-based data warehouses, ETL and streaming, and elastic in-memory and storage frameworks.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsFIGURE 4 Cloud, Streaming, And Distributed In-Memory Are Already Part Of Firms’ Big Data Strategy“Which of the following are included in your plans for big data?” Public cloud big data services 40% 36% Large-scale predictive modelling, data mining, 33% or other advanced analytics 30% 28% Streaming analytics/computing 27% 26% Distributed in-memory databases, grids, 26% analytics tools Unstructured data mining/analytics Packaged analytics technologies that brand themselves as big dataMarketing or digital data management platforms and service providers that brand their offerings as . . . Creating or building out a data lakeData anonymization or de-identi cation 23% Hadoop (including Hbase or Accumulo) 23% 22%Semantic technologies (ontology building, 18% search, autocuration, graph, etc.) 16% A massively parallel processing (MPP) data warehouse NoSQL other than HadoopDon’t know 8%Base: 2,094 data and analytics decision-makersSource: Forrester’s Global Business Technographics® Data and Analytics Survey, 2016© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsFIGURE 5 Big Data Warehouse ArchitectureSources Storage/compute Management Interaction Use cases processing Business OLTP Relational Integration In-memory/ intelligence CRM Data quality Apache Spark ERP Columnar Operational Social Security Self-service reporting SaaS Apache Hadoop Transformation Ad hoc Devices AnalyticsSensors Governance interactions Machine learning Modeling Predictive analytics Real-time analytics ETL/CDC/replication On-premises Streaming Cloud HybridBig Data Fabric Connects The Superset Of Your Data Sources — Including Your BDWsThe big data warehouse is part of a larger big data fabric architecture, which embodies data frommultiple — potentially distributed — data sources, including BDWs and data lakes. The big datafabric architecture enables integration, data quality, security, governance, data curation, datapreparation, and data management to support an end-to-end, real-time big data platform (see Figure6).5 The two architectures: ›› Can exist separately but work best as complements. Multiple traditional EDWs, BDWs, and data lakes have become the new norm to support the variety of analytical workloads. While both BDWs and big data fabric architectures can exist independent of each other, typically firms leverage both to deliver a blend of real-time and batch across various distributed enterprise data sets to support broader use cases. For example, some financial services organizations use the BDW to support mostly financial data analytics — leveraging columnar data warehouses, Hadoop, and ETL technologies. The BDW also acts as a source within the big data fabric architecture that delivers real-time customer analytics across BDW, Twitter, Salesforce, and clickstream data. © 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. 8 [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics ›› Vary significantly in the amount of data transformation required. We often see big data fabric used for real-time analytical use cases that integrate data across many disparate sources, including BDW, with the BDW used mostly for batch and near-real-time analytics for data stored in a data warehouse and Hadoop clusters that require aggregation, transformation, and further processing before becoming available to BI users or analytical processes. Exploration occurs within the fabric, with transformations captured within the BDW.FIGURE 6 Big Data Fabric Architecture Integrated With Big Data Warehouse Big data fabricHadoop BDW Processing and Spark persistenceNew York Data ingestion (streaming/replication/batch) Hadoop Spark EDW SingaporeOn-premises sources Cloud sourcesThe BDW Provides A Comprehensive View And Integrated AnalyticsA key component of the BDW architecture is the ability to leverage various specialized datarepositories such as traditional relational data warehouses, columnar data warehouses, and Hadoop.Unlike traditional data warehouses, the BDW minimizes complexity and hides heterogeneity byembodying a trusted model, supports all kinds of data types including unstructured data, and adaptsto changing business requirements more rapidly through a self-service platform. The BDW centralizesadministration of distributed data repositories, in-memory compute resources, metadata, storage,access, and processing functions. It leverages new technologies such as: ›› Hadoop to support diverse data sets and distributed computing. By leveraging Hadoop, the BDW enables organizations to deal with a wider variety of data structures than traditional EDWs. Hadoop can also deal with extremely large data sets that are inappropriate for traditional EDW© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics platforms. Enterprise architects can choose to store data in relational, columnar, wide columns, or Hadoop based on business needs. For example, a retailer leverages legacy structured data stored in a traditional data warehouse, and Hadoop for clickstream data, and integrates them to deliver a 360-degree view of the customer for recommendations and churn analysis. ›› In-memory to enable faster customer analytical capabilities. A key component of the BDW is the ability to use in-memory to deliver performance and faster access to business data. We are heading toward having large memory platforms that will store petabytes in DRAM and Flash/SSD in the coming years. For example, several retailers are using BDW to leverage customer-related data to determine product discounting strategy, optimize product distribution across stores, and enable personalized customer experiences. ›› Streaming engines to support new data channels for ingestion and processing. Market data, clickstream, mobile devices, and sensors are new sources for analytical information that are not in your existing data warehouse. Streaming technology boosts integrating, transforming, and curating data on diverse data streams in real time.6 Integrating streaming technology with data platforms such as Hadoop and Spark — as well as traditional data warehouses — has become critical. For example, we see oil and gas industry firms leveraging streaming technology for insights into new business opportunities, such as predicting staffing and resource requirements for various drilling sites and performing machine failure analysis.The Major EDW Vendors Provide BDW ComponentsFrom an implementation viewpoint, most enterprises are currently building BDW platforms themselvesby integrating their traditional data warehouses with Apache Spark, Hadoop, Storm, and in-memorytechnologies. Forrester sees many enterprises already using an extract-Hadoop-load (EHL) approach to:1. Extract data from various source systems such as traditional databases and flat files.2. Load data into Hadoop to perform aggregation and transformation using Apache Hadoop ecosystem tools.3. Finally load the result into the EDW platform.7BDW Use Cases Go Beyond Traditional AnalyticsAdoption of BDW architectures will accelerate as enterprises run into existing EDW challenges. Butbuilding a BDW platform internally will require more time and effort, which will likely put pressure onthe overall business technology (BT) agenda. The good news is that solutions are starting to emergefrom vendors such as IBM, Microsoft, Oracle, SAP, Snowflake, and Teradata that provide some or all ofthe components to build and deploy a BDW strategy.8 Enterprises are already using BDWs to supportsocial analytics, risk analysis, campaign analysis, fraud assessment, and pricing trends. The top BDWuse cases include:© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service Analytics›› Integrated analytics. A key challenge in the traditional EDW approach was that if data didn’t exist in the warehouse, you couldn’t do any analytics — full stop. With BDW architecture, you can perform integrated analytics across data warehouse and Hadoop clusters. Hadoop can store and process large sets of semistructured and unstructured data, log files, and streaming data with ease. For example, health research often requires looking at complex patient data and determining how effective a treatment is likely to be based on factors like age, sex, and health status. The BDW enables gathering and storing millions of data points in Hadoop and performing complex navigation and modeling using traditional data warehouse and in-memory technology.›› Internet-of-things (IoT) analytics. Traditional data warehouses don’t deal with IoT data. However, the BDW offers the ability to store, process, and access large volumes of IoT data from sensors and devices in Hadoop repositories efficiently through automation and machine learning technologies. Manufacturers deal with highly sophisticated machinery to support their plants, whether they’re building a car, airplane, or tire or bottling wine or soda. Every minute of machine downtime can cost a manufacturer dearly. IoT analytics on BDW platforms enables manufacturers to predict machine failures based on sensor data, minimizing or eliminating production slowdown.›› Right-time business analytics. Traditional EDW architectures were based on mostly batch processing, with ETL doing the heavy lifting of data from traditional systems to operational systems to data warehouses. As a result, by the time data arrived in data warehouses, it was already 12 to 48 hours old. BDWs enable right-time analytics by leveraging streaming and replication with direct access to data sources, whether on-premises or cloud, bypassing traditional ETL approaches. The financial services industry has been an early adopter of BDW to support right-time analytics for portfolio management, fraud detection, and asset management.›› Adaptive, self-service analytics. Most EDWs use predefined data sources to deliver predictive analytics, trends, and insights. The BDW enables organizations to dynamically leverage new data sources quickly to deliver new insights. It enables self-service capabilities for business users to ask complex and new questions so they can make more accurate decisions. The BDW adapts to the new sources and can help correlate data using machine learning and adaptive intelligence. For example, a major European bank recently built a BDW framework that business units now use to support self-service for making better decisions on investments and risks. The platform represents a major shift from the static reports the bank used previously.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsRecommendationsExtend Your Current EDW Platforms Toward A BDW StrategyDon’t throw away your existing EDW platform! The investments you have already made in EDWs will formthe foundation of the next-generation BDW strategy. However, attaining this demands that you rearchitectyour existing EDW platform and invest in new technologies to deliver on a new vision of right-timeanalytics, self-service, and intelligent and contextualized customer analytics. Forrester recommends thatenterprise architects extend existing EDW platforms toward a BDW strategy by leveraging: ›› Hadoop for low-cost storage and processing of big data. Let Hadoop be the first stop for your big data that has no other home in your data warehouse. Hadoop offers the ability to store very large volumes of data (including unstructured data) more efficiently than traditional warehouses — and at a fraction of cost. In addition, Hadoop helps you offload data from traditional warehouses and leverage a distributed computing framework to perform transformation, aggregation, and curation quickly. ›› In-memory technology to support right-time analytics. Without in-memory technology, customer analytics, personalization, and right-time analytics will run slowly. This could cause you to miss key trends like customer churn or miss the opportunity to offer new products and services or identify weak markets. You can also use data from the BDW as part of the bigger big data fabric framework that leverages distributed in-memory computing to deliver a broader enterprise information fabric. ›› Hybrid platforms to support on-demand and scalable BDWs. Storing all of your data on- premises need no longer be the default. Cloud platforms like those from Amazon Web Services, Google, IBM, Microsoft (Azure), Oracle, and Rackspace offer pay-as-you-go facilities to store, process, and access any amount of data.9 Hybrid is the new norm — look at utilizing both on- premises and cloud data warehouse platforms as part of your BDW architecture, with a common administration facility. ›› Vendor solutions that help achieve faster time-to-value. Data warehouse, Hadoop, and other big data solutions from vendors such as Cloudera, Hortonworks, IBM, MapR Technologies, Microsoft, Oracle, SAP, and Teradata can reduce time-to-value by automating and simplifying various BDW functions and implementation steps. Look at vendors that support broader solutions and can support your business data. Ask your vendor how it plans to provide the BDW vision. Review the various components that the vendor has integrated and ask how it plans to fill any gaps.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsEngage With An AnalystGain greater confidence in your decisions by working with Forrester thought leaders to applyour research to your specific business and technology initiatives.Analyst Inquiry Analyst Advisory WebinarTo help you put research Translate research into Join our online sessionsinto practice, connect action by working with on the latest researchwith an analyst to discuss an analyst on a specific affecting your business.your questions in a engagement in the form Each call includes analyst30-minute phone session of custom strategy Q&A and slides and is— or opt for a response sessions, workshops, available on-demand.via email. or speeches. Learn more.Learn more. Learn more.Forrester’s research apps for iPhone® and iPad®Stay ahead of your competition no matter where you are.Supplemental MaterialForrester’s Global Business Technographics® Data And Analytics Survey, 2016 was fielded in March2016. This online survey included 3,343 respondents in Australia, Brazil, Canada, China, France,Germany, India, New Zealand, the UK, and the US from companies with 100 or more employees.Forrester’s Business Technographics ensures that the final survey population contains only those withsignificant involvement in the planning, funding, and purchasing of business and technology productsand services. Research Now fielded this survey on behalf of Forrester. Survey respondent incentivesinclude points redeemable for gift certificates.Please note that the brand questions included in this survey should not be used to measure marketshare. The purpose of Forrester’s Business Technographics brand questions is to show usage of abrand by a specific target audience at one point in time.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

For Enterprise Architecture Professionals August 29, 2016The Next-Generation EDW Is The Big Data WarehouseBig Data Warehouses Drive Faster, Integrated, Self-Service AnalyticsEndnotes1 Today, organizations still rely on EDW platforms to deliver actionable, timely, and trustworthy intelligence. EDW technology organizes and aggregates analytical data from various functional domains and serves as a critical repository for organizations’ operations. See the “The Forrester Wave™: Enterprise Data Warehouse, Q4 2015” Forrester report.2 It takes a long time to measure a business process. Enterprise data hubs need to accommodate more data and an infinite set of queries. See the “Create A Road Map For A Real-Time, Agile, Self-Service Data Platform” Forrester report.3 Data consumers — from casual data analysts to data scientists to your customers — are looking across a broad variety of data today to find answers to their questions. See the “Compose Digital Data To Create A Symphony Of Insight” Forrester report.4 Data bottlenecks create business bottlenecks. The days of provisioning data to simply meet the requirements of systems of record are over. Business stakeholders at the executive and line-of-business levels need data faster to keep up with customers, competitors, and partners. See the “Create A Road Map For A Real-Time, Agile, Self-Service Data Platform” Forrester report.5 Forrester defines big data fabric as “bringing together disparate big data sources automatically, intelligently, and securely, and processing them in a big data platform technology, such as Hadoop and Apache Spark, to deliver a unified, trusted, and comprehensive view of customer and business data.” See the “Big Data Fabric Drives Innovation And Growth” Forrester report.6 Streaming technology helps integrating, transforming, and curating data on diverse data streams in real time. See the “The Forrester Wave™: Big Data Streaming Analytics, Q1 2016” Forrester report.7 Forrester sees many enterprises already using an extract-Hadoop-load approach to extract data from various source systems, such as IoT devices and cloud and traditional platforms, then load it into Hadoop, perform aggregation and transformation, and finally load it into the EDW to support business analytics. See the “The Forrester Wave™: Enterprise Data Warehouse, Q4 2015” Forrester report.8 Most big data integration vendors focus on making classic processes faster with tools for moving data into a lake and working with it there. Three innovative vendors — Looker Data Sciences, SnapLogic, and Snowflake Computing — offer alternative approaches. See the “Breakout Vendors: Big Data Integration” Forrester report.9 According to Forrester customer feedback, such cloud-based storage is typically over 20% less expensive than on- premises deployment.© 2016 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

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