Data Analytics as a Service

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1 Data Analytics as a Service unleashing the power of Cloud and Big Data

2 Big Data in a Cloud

3 DAaaS: Data Analytics as a Service

4 DAaaS: Data Analytics as a Service Introducing Data Analytics as a Service (DAaaS) Providing advanced analytical capabilities, like anomaly detection, predictive analytics and advanced pattern recognition Deployed in a cost effective SaaS model. Customer value proposition Data as a service Functionality - Access to raw individual data - Access to pre-aggregated data Common interfaces ODBC & JDBC Analytics as a Service (analytics workbench) Functionality - Run predefined queries and analytics - Define and run custom queries and analytics Common interfaces HTTP, REST and Proprietary Why Atos Unique offer with a full Big Data Analytics environment in the cloud on Canopy and Partnered by Siemens First set of anomaly detection algorithms especially made for the oil and gas industry Scalable on-demand Participation of third parties possible to add own apps Analytics Marketplace Based on open source and best of breed COTS Customer benefits Low entry hurdle into Business Analytics Allows to put the focus on insight discovery without worrying about the technology Quicker feedback loops during Business Analytics strategy definition phases.

5 Elements of a DAaas

6 The Challenges of a DAaas Information Lifecycle Management: the complete analytical workflow can get very complex with lots of important steps: data acquisition (data access, setting parameters, transformation, data cleansing, data quality ) data modeling (definition of logical model, linking with other data ), data mining (variable identification, algorithm selection, validation ) and visualization (customized reporting, advanced graphics ). Data model diversity: a diversity of potential types of data models exists for specific business needs - and these data models are tightly coupled to specific types of analytics. Analytic knowledge: although not really new, many of the advanced techniques related to advanced analytics (like Machine Learning) are quite complex and demand people with very specific knowledge,

7 The Challenges of a DAaas Data volume: even when technology exists for processing huge volumes of data, it is not easy. Moving big volumes of data to a cloud solution can be difficult, and sometimes, it is much easier to bring computation to where the data is. Real-time analytics: more and more, the value of analytics demands quicker insights, progressing towards the concept of real-time analytics. Security: like in any other cloud solution, security is a very complex issue. Some companies, due to the data criticality or regulatory constraints, may be reluctant to move data to the cloud, but could benefit of the analytical capabilities offered in a private cloud. Privacy: for some specific types of data, privacy considerations may impact the potential of cloud analytics - not only due to the data in itself, but also due to the potential that data will not remain anonymous after analysis.

8 Benefits of DAaaS The main benefit of the DAaaS is to lower the barrier of entry to advanced analytical capabilities, without demanding that the user commits to large internal infrastructures and human resources to the project. Instead of a complex custom project the customer follows simpler steps: Data Scientists working for the organization explore the AppStore for an Analytical App that fits the problem. They rent the Analytical App for a specific time or quantity of data. They configure the Analytical App to its needs including, for example, the usage of external data sources provided by the DAaaS. Then the data is fed from the internal systems to the Analytical App. The SMEs in the company validate the results and even enhance them with some customization. Outcomes are available for all other uses.

9 DAaaS in a Real Life Value Proposition: Unique offer with a full Big Data Analytics environment in the cloud on Canopy and powered through Helix Nebula and Pivotal. Partnered by Siemens First set of anomaly detection algorithms especially made for the oil and gas industry Scalable on-demand Participation of third parties possible to add own apps Analytics Marketplace Based on open source and best of breed COTS Strategic Partner(s): Siemens / XQH References (WIP): Vitens, BPCL and Shell

10 DAaaS in a Real Life XHQ Lite Option Corporate Technologies Analytical App Store Source Systems Analytic Apps Analytic Algorithms Analytic Algorithms XHQ Connector & Cloud Gateway Cloud Data Analytics Cloud Data Management Cloud Data Sources DAaaS Cloud Platform External Data Sources

11 Data Analytics Hidden Information in 4 Domains Production Quality The Solution might be hidden in the massive amounts of data that is streaming through the plant every day on all domains. Maintenance Inventory

12 Future Trends Big Data Will Transition From Hype to Actionable Insights Visualization / Big Data (Analytics) Tools Will Become Essential Enterprise IT Investments More Companies Will Implement Machine Learning and Predictive Analytics The rise of the Industrial Internet / analytics everywhere

13 Thanks For more information please contact: T F M Atos, the Atos logo, Atos Consulting, Atos Sphere, Atos Cloud and Atos Worldgrid, Worldline, bluekiwi are registered trademarks of Atos Group. November Atos. Confidential information owned by Atos, to be used by the recipient only. This document, or any part of it, may not be reproduced, copied, circulated and/or distributed nor quoted without prior written approval from Atos. For internal use

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