Three steps to becoming a data and analytics driven enterprise

Transcription

1 Three steps to becoming a data and analytics driven enterprise Unlocking innovation with data and analytics Big data & Business Analytics KEY TAKE-A-WAY Tapping into the potential of digitization requires organizations to; first, implement new big data and advanced analytics technologies. Second, develop and adopt agile business models that respond faster to changing market and economic conditions. Third, drive innovation while addressing governance, security, and the delivery of personalized customer centric solutions. This paper describes HCL s approach to delivering business value quickly as the organization transforms into an Agile Analytics Enterprise. WHITEPAPER April 2016

2 TABLE OF CONTENTS INTRODUCTION 3 STEP ONE: INDUSTRIALIZE DATA ACQUISITION 3 STEP TWO: HARMONIZE AND UNIFY DATA STORAGE. 4 STEP THREE: PERSONALIZING DATA DELIVERY AND ANALYTICS 5 CONCLUSION 7 THE HCL APPROACH 7 ABOUT HCL s BAS PRACTICE , HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 2

3 INTRODUCTION Digitalization is changing the business climate Digitalization is fundamentally changing the business climate. To be successful organizations must develop and adopt agile business models that respond faster to changing market and economic conditions. They must also develop the capability and agility to power innovation while addressing governance, security, and the delivery of personalized customer centric solutions. However, successfully implementing big data and analytics solutions introduces unique challenges. Current technologies, such as operational data stores, enterprise data warehouses, data marts, and appliances are complex, rigid, and expensive to maintain. Data analysis beyond the original design is almost impossible, changes require redesign, and performance is a problem. As a result, business users are frustrated with the lack of agility to rapidly interrogate and explore data intuitively, the way they want to. And IT has the unenviable task of becoming the gatekeeper that balances capacity and cost. Delivering on the promise of big data and analytics requires an organizational shift to The Agile Analytics Enterprise. The Agile Analytic Enterprise delivers high performance data and analytics results that convert data into just-in-time insights. It enables organizations to deliver personalized digital services through: y The ability to ingest and process any data necessary to understand the customers preferences y Advanced analytics to automate connections, on any device, to their preferred products and services. In this white paper we examine three key steps organizations can take to unlock the potential of big data and analytics. We also explore how to incrementally deploy data and analytics services across a unified platform that delivers agility to business users and customers, improves operational performance, and reduces the total cost of ownership demanded by IT. STEP ONE: INDUSTRIALIZE DATA ACQUISITION Digitally centric applications used in devices and communications have exploded exponentially, massively increasing the volume, velocity, variety, and complexity of data organizations have at their disposal Applications like these will continue to grow as companies exploit the potential of the Internet of Things (IoT). Insurers are already fitting devices to cars to monitor driving behavior. Telecom companies are using call and network log data to microtarget customers, and utilities companies are introducing smart meters that provide readings every few minutes. Plus, personalized health devices are enabling people to collaborate with a network of like-minded people and track their goals. Social media applications such as Facebook, Twitter, LinkedIn, Pinterest, Google+, and WeChat are not only changing how people interact, but also how we engage with companies and share information. Big data and analytics offer companies a platform to industrialize the acquisition of 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 3

4 data by automating the ingestion of any data, any time. There are no longer limits. Data from sources such as enterprise applications, sensors, the web, and consumer and/or employee devices are all part of the platform s capabilities. And the data sources can provide structured, semi structured, or unstructured data, images, and video. Case study: Leading US telecom operator Challenge Integrate and correlate data from social media with legacy systems to build a nextgeneration business intelligence system. Solution A reliable, extensible, and future-ready service for ingesting data, together with an Agile Analytics operating model and as a service delivery for enabling applications. Benefits An end-to-end process for capturing, storing, and processing multiple varieties of high speed, high volume data, which is then pushed to the desired systems, allowing advanced analytics to be carried out in a secure manner. STEP TWO: HARMONIZE AND UNIFY DATA STORAGE. The next step to becoming a data driven enterprise is to put in place a data lake, a massive, easily accessible data repository built on relatively inexpensive computer hardware. Data lakes enable organizations to capture any data at any time. The key to exploiting a data lake is automating the processes that enforce governance and security, analyzing data patterns for storage, and then distributing correlated data sets based on user requirements. Unlike data marts, with rigid designs and schemas based on pre-defined attributes, a data lake is designed to retain all the data attributes. Keeping all the data attributes means that decisions do not have to be taken about the scope of the data or what its use will be. As a result, the data is future-proof, in the sense that it is available for use by any type of analysis in the future. Data lakes provide rapid access to a growing array of data sources and are built on four key concepts: 1. Overarching governance for the data and metadata, providing integration of any data, any time, and in any volume 2. The security to deliver data to any user on any device 3. Standards for staging the raw data needed by all other data platforms 4. Standards for the analysis and transformation environments. Case study: Fortune 500 financial services group Challenge Existing analytics reports could not determine the reason for sales of online auto insurance products declining despite traffic to the web site increasing. 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 4

5 Solution Migration from traditional analytics to a big data analytics solution that explores patterns of consumer behavior. Benefits Improved funnel and behavior analysis that resulted in a increase in conversion rates, an improvement in the quality of leads, and the ability to create targeted marketing, upsell and, cross-sell offers. STEP THREE: PERSONALIZING DATA DELIVERY AND ANALYTICS The third and final step is to make relevant data and analytics available to business users as a service providing data built and delivered on regular cycles or built to order for specialized and one-off needs. Delivering personalized data and analytics services requires a framework for governance and security that enables data to be published based on a user s access permissions. Personalized data and analytics services enable organizations to deliver pre-defined or customized data sets, analytical methods, models, algorithms, as well as enterprise metrics, and KPIs. End-users have access to easy-to-use methods for selecting the data, self-service BI and analytics tools, and pre-built, interactive visualizations based on enterprise standards. Personalized data and analytics services deliver the flexibility and agility analytic producers and consumers need to quickly analyze data from any source, including legacy systems, social media, the IoT, machine-to-machine communications, and business events and transactions. Case study: Global aerospace manufacturer Challenge Acquire, process, and analyze aircraft sensor data in near real-time and reduce the time lag of manually analyzing unstructured text. Solution A data acquisition, data lake, and analytics solution for structured, semi-structured, and unstructured data that provides self-service analysis, uses interactive dashboards, and allows cross-functional teams to work together. Benefits Replacement of manual text analysis with text mining and advanced analytics. Integration of service data and text mining with in-flight sensor data to develop predictive models that are improving service reliability guidance and maintenance scheduling, resulting in higher service reliability. 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 5

6 JOURNEY TO A DATA AND ANALYTICS DRIVEN ENTERPRISE Following the steps above allows organizations to incrementally move to a unified, agile, and flexible data and analytics services delivery platform. We recommend organizations plan, launch, and deliver incrementally and avoid a big-bang approach. Planning correctly will progressively deliver business value quickly. We suggest organizations use one or both of the approaches outlined below. BOTTOM-UP APPROACH The bottom-up approach is a Total Cost of Ownership (TCO) approach that identifies the path forward based on replacing existing legacy systems and enterprise data warehouses that no longer provide the capabilities and performance needed to drive business needs. They may also have become prohibitively expensive to run and/ or are not meeting service level agreements. In this situation, the resulting big data lake may co-exist with existing enterprise data warehouses; extract, transform, load platforms; and existing analytics reporting environments. This approach dramatically reduces the TCO and provides the advantages of being able to publish data sets for specific analytical needs and reduce time to insight. Case study: Bottom-up implementation Challenge A leading financial services organization wanted to decrease the cost and increase the scalability of its legacy analytics solutions. Solution Legacy processes were offloaded to a new big data lake solution. Benefits 40% reduction in total cost of ownership and no misses on its service level agreements. TOP-DOWN APPROACH The top-down approach is business value-driven and delivers benefits quickly by focusing on specific business use cases. For example, applying predictive analytics to deliver next best offer, or improving maintenance by servicing expensive assets at the right-time and right-location. The business case is identified and developed based on the value of the insights delivered to the organization and its customers. Each business case drives incremental delivery. As projects are completed and deployed business users and IT realize the benefits - and the capabilities of the unified data and analytics platform are realized and enhanced. Case study: Top-down implementation Challenge At the largest investment bank in Germany changes to risk rules were taking 4-6 months, simulation was limited to a sample data set, and the manual processes were leading to errors. 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 6

7 Solution A new big data lake solution. Benefits Improved time-to-market of implementing risk rule changes and the ability to test using large data sets, which resulted in more accurate results. CONCLUSION Surviving in today s dynamic business environment means being able to innovate and operate at the speed of digital commerce. In turn that means becoming a data and analytics driven enterprise delivering Agile Analytics services to business users and customers. This requires the: y Agility to ingest process, and analyse any data, any time, at any level of complexity y Flexibility to correlate, combine, and consolidate all data for rapid delivery y Ability to easily and quickly publish data for end user reporting and analytics applications y Facilities to gain the insights needed to grow market share, improve operations, and increase customer satisfaction. A unified data and analytics platform, based on a big data lake, provides these capabilities. At HCL we have the experience and expertise to help you on your journey to becoming a data driven enterprise. To start the discussion please get in touch using one of the methods below: integrated.applications@hcl.com THE HCL APPROACH At HCL we ve developed the expertise, methodologies, and a wide range of tools that support the three steps to successfully implementing big data and analytics. With our Agile Analytics approach we help customers accelerate their journey to becoming a data and analytics driven enterprise. Industrializing data acquisition Using our Data Movement Framework we ingest data from multiple source systems, internal and external, in varied formats, to a data lake that hosts, processes, and publishes the data to different downstream systems. Data is delivered in context, in that datasets are created based on an on-demand approach from business users for self-service analytics and reporting. In addition all the data in the data lake is made available for delivery from varied, multiple, and concurrent channels and mobile devices. 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 7

8 Industrializing data acquisition Bulk job creation Data Cleansing and Data Transformation Libraries Security for PII/PCI data using AES encryption. This is very relevant to Citi context Audit Service Metadata Enabled data discovery End to End Data Lineage Extensible to Real Time Data Sources Data Movement Easy to Use Configurable UI to capture source an destination details Pattern based libraries to capture data from different data sources Logging mechanism to record execution details of ingestion routines Operational Dashboard Alerting mechanism for exceptions Scheduling Services to initiate ingestion automatically HARMONIZING AND UNIFYING DATA STORAGE As part of the process of creating the data lake, our approach also provides an intuitive method for users to shop for the data and analytics they need for their business application, including: y A metering process to keep track of the data delivery - volume of data transfer, frequencies, CPU usage, disk space usage, etc y Charge back policies y Data delivery using different channels, such as file system, database, messaging system, REST APIs, JDBC connections, ODBC connections y Publishing and governing the APIs through which data is served y Searching data sets by different means, such as names, relations, or data values y Capturing requests for new data sets along with the approval/refer back process y Capturing and tracking orders placed for different data sets by users SEARCH JOB GENERATOR EXPLORATORY DATA ANALYSIS HARMONIZING AND UNIFYING DATA STORAGE ERROR CAPTURING & VISUALIZATION SCD 1 & SCD 2 PERFORMANCE DATA CAPTURING 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 8

9 Personalize data delivery and analytics At HCL we believe that focusing on delivering specific business use cases is a powerful way to drive business value. Our exploratory workshops enable a tripartite collaboration between our experts and client business and IT users and have been proven to be successful in multiple engagements. For example, at one of the leading investment banks in Europe we created a unique standalone co-innovation lab where business stakeholders, partners, and end users are able to directly engage on business problems through research, ideation, and rapid prototyping. The result has been a substantial reduction in the time from business idea to business value. Personalize data delivery and analytics HCL Funds Lab Data Quality and Preparation Working Model Lab Location Strategy Data Undertaking Modelling Service Catalogue Operating Model Inputs Process Outputs Outcomes Product Partnership POCs Product Specialist Team Composition EXIN Selection Framework Socialization ABOUT HCL s BAS PRACTICE HCL s Business Analytics Services practice provides deep capabilities and solutions to help companies move to the next generation of big data solutions and become a truly data and analytics driven enterprise. Our clients benefit from: y A global team that seamlessly draws on business and IT cross-functional services, and our co-innovation labs in Noida and Bangalore (India), London (UK), Cary (NC), and Guadalajara (Mexico) to create innovative solutions that deliver real business value y An Agile Analytics approach for rapidly designing, building, and deploying data lakes y A Global Center of Excellence with high standards of practice and competency building for accelerating deliveries y A talent pool with deep and comprehensive big data and analytics skills y A vast range of next generation services for unified information architectures that include EDW, MPP, In-memory processing, big data and analytics, business intelligence, and interactive visualizations 2016, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED. 9

10 About HCL Technologies HCL Technologies is a leading global IT services company working with clients in the areas that impact and redefine the core of their businesses. Since its emergence on the global landscape, and after its IPO in 1999, HCL has focused on transformational outsourcing, underlined by innovation and value creation, offering an integrated portfolio of services including software-led IT solutions, remote infrastructure management, engineering and R&D services and business services. HCL leverages its extensive global offshore infrastructure and network of offices in 31 countries to provide holistic, multi-service delivery in key industry verticals including Financial Services, Manufacturing, Consumer Services, Public Services and Healthcare & Life sciences. HCL takes pride in its philosophy of Employees First, Customers Second which empowers its 100,000+ transformers to create real value for customers. HCL Technologies, along with its subsidiaries, had consolidated revenues of US$ 6.1 billion, for the Financial Year ended as on 30th December 2015 (on LTM basis). For more information, please visit About HCL Enterprise HCL is a $7 billion leading global technology and IT enterprise comprising two companies listed in India HCL Technologies and HCL Infosystems. Founded in 1976, HCL is one of India s original IT garage start-ups. A pioneer of modern computing, HCL is a global transformational enterprise today. Its range of offerings includes product engineering, custom & package applications, BPO, IT infrastructure services, IT hardware, systems integration, and distribution of information and communications technology (ICT) products across a wide range of focused industry verticals. The HCL team consists of over 105,000 professionals of diverse nationalities, who operate from 31 countries including over 505 points of presence in India. HCL has partnerships with several leading global 1000 firms, including leading IT and technology firms. For more information, please visit Hello there! I am an Ideapreneur. I believe that sustainable business outcomes are driven by relationships nurtured through values like trust, transparency and flexibility. I respect the contract, but believe in going beyond through collaboration, applied innovation and new generation partnership models that put your interest above everything else. Right now 105,000 Ideapreneurs are in a Relationship Beyond the Contract with 500 customers in 31 countries. How can I help you?