Transformation with aFinancial Services Next Generation Data WarehouseThe unparalleled growth in the volume, variety and velocity of data within and outside the enterprise is both a challenge and an opportunity for today’s companies. The ability to leverage Big Data for actionable business insights is a key to sustainable competitive advantage in today’s marketplace. Leading financial services institutions need to rethink their Data Warehousing strategy for improved business outcomes.

A true Digital Enterprise engages customers through rich, personalized, meaningful and proactive interactions. This requires the ability to collate, organize and analyze structured and unstructured customer information from a variety of data sources in real or near-real time.

Data Warehouse Modernization with HadoopFor banks and financial institutions, the limitations of traditional Data Warehouses often make it difficult to leverage data for data-driven decision making. Increasing volumes lead to high costs and poor performance with these traditional systems.

Atos Syntel recommends augmenting existing Data Warehouses with a Hadoop-based Data Lake to enable maximum scalabilityand flexibility at the lowest possible cost. Data Lakes are built to handle higher volumes and greater varieties of data, and have several key benefits.

LowCost

More Insights

High Scalability

Data Agnostic

Data lake comes with all the benefits of Big Data systems built on commodity servers.

Analytics Tools can pull more insights from Raw Data and high performance of Big Data system.

Hadoop based Data Lakes are highly scalable. Commodity servers can be added to manage increased load.

Can store both structured and unstructured data in raw formats. Also supports multiple programming languages and frameworks.

• Greater variety and volume of data – Ability to analyze all data stored in the lake

• Support for multiple tools and products – The cross-engine integration requires multiple technology stacks that support structured, semi-structured, and unstructured data types

• Domain focus – The Data Lake must be tailored to the client’s specific industry and must have a business-aware data locating capability

• Automated metadata management – Captures a robust set of attributes for every piece of content within the lake (e.g. data lineage, data quality, and usage history)

• Configurable workflows – A configurable ingestion workflow can provide a high level of reuse, enabling easy, secure, and traceable content ingestion from new sources

• Easy integration – The Data Lake needs to meld into and support the existing enterprise environment, including data management paradigms, tools, and methods

In Atos Syntel’s view, the characteristics of a Data Lake are:

• Step 1 employs classic ETL processing to deliver structured and repeatable analysis.

• The “Big Data Refinery” highlighted in Step 2 is a new system capable of aggregating and transforming a wide range of multi-structured raw data sources into usable formats.

• Step 3 takes the model further, as the Big Data Refinery interacts with systems powering Business Transactions & Interactions and Business Intelligence & Analytics.

• Since the Data Lake excels at retaining large volumes of data for long periods of time, the model is completed with the feedback loops illustrated in Steps 4 and 5.

Atos Syntel’s Big Data Offerings Atos Syntel combines its expertise with a wide range of tools and industry best practices to deliver Big Data solution and servicesthat provide a 360° view of the business through structured, consistent and clean customer information, while ensuring that costs are kept low.

Our Big Data and Analytics practice has expertise conceptualizing and designing Big Data strategies, and defining roadmaps for end-to-end Big Data implementations in several industries. Our Big Data services include:

These solutions are enabled by a Big Data Innovation lab dedicated to capability and solution building, a 350+ member team of professionals, and partnerships with leading Big Data package developers like Cloudera.

CONSULTINGNEW DATA

PLATFORMSDATA SCIENCE

• Assessment, Strategy &Roadmap

• Infrastructure Set-up• Tool Evaluation• Architecture – Planning

& Execution

• Agile Big Data• Big Data on Cloud• Big Data Security

• Hadoop as a Service• Augmented Data

Warehouse • Legacy Modernisation• Archival and Content

Management

• Data Mining• Statistical Modeling• Predictive Analytics• Text Analytics• Machine Learning

E2E IMPLEMENTATION

Typical Data Lake Architecture

Audio, Video, Images

Docs, Text, XML

Web logs, Clicks

Social, Graph, Feeds

Sensors, Devices, RFID

Spatial, GPS

Events, Other

2

Data Lake

Classic ETL processing

Store, aggregate and transform multi-structured data to unlock value

4Retain runtime models and historical

data for ongoing refinement & analysis

1

Share refineddata and runtimemodels

3

5Retain historical data to unlock additional value

Web, mobile, CRM,ERP, SCM, …

Dashboards, Reports,Visualization, …

BusinessTransactions

&Interactions

Big DataRefinery

Business Intelligence& Analytics

Company Name Global Cards & Payments Major

Challenges• Inability to positively influence in-flight marketing campaigns run by merchants • Existing Merchant Campaign Performance Management process takes 30 days • Existing Netezza Platform was unable to process existing volume of campaign data, transactions, demographics and

customer behavior attributes

Solution• Integrated a Hadoop based Data Lake with the existing Netezza DW to efficiently store 21+ billion records and 5+ TB of

input data• Delivered near real-time view of campaigns using advanced data visualization• Built an analytics solution to generate near-real time business insights and enable course correction for in-flight campaigns

Benefits Delivered• 15% reduction in campaign cost• Reduced TAT from 30 days to a few hours• Increased ROI through timely course correction

Case Study 2

© 2018, SYNTEL INC.

Company Name Leading Payment Services Provider

Challenges• Inability to generate real-time or near real-time business insights from payment data• Lengthy, 10+ hour nightly payment data batch process on Teradata platform• High cost of maintaining scripts in Teradata

Solution• Implemented a Hadoop-Spark Augmented Data Warehouse solution in conjunction with the existing Teradata

Data Warehouse• Migrated 6 PB of data and 50,000+ scripts from Teradata to Spark on a Hadoop platform

Benefits Delivered• Saved 50% on migration efforts with a reusable framework to automatically convert Teradata scripts to Spark SQL• 30% reduction in TCO

Case Study 1

Success Stories

To summarize, we want to highlight that we have deep domain experience in both conceptualizing and designing Big Data strategies, and our end-to-end services encompass everything from defining a roadmap to a full-scale Big Data implementation.

Analytics solutions will enable you to understand your customers, proactively offer relevant Our Big Data & products and services, and personalize customer interactions to maximize loyalty.

About Us

Atos Syntel is a leading global provider of integrated information technology and knowledge process services. Atos Syntel helps global enterprises evolve the core by leveraging automation, scaled agile and cloud platforms to build efficient application development and management, testing and infrastructure solutions. Our digital services enable companies to engage customers, discover new insights through analytics, and create a more connected enterprise through the internet of things. Our "Customer for Life" philosophy builds collaborative partnerships and creates long-term client value by investing in IP, solutions and industry-focused delivery teams with deep domain knowledge.

To learn more, visit us at: www.atos-syntel.net

For more information, visit us at www.atos-syntel.net