The New Enterprise Data Platform

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Emergence of the analytics appliance and big data - Gartner Business Intelligence Conference 2012. IBM and Fox Filmed Entertainment session

Sarath Ramaswami, VP of IT Krishnan Parasuraman, CTO, Digital Media
Fox Filmed Entertainment Netezza and Big Data, IBM

The New enterprise data platform :
Emergence of the analytics appliance and big data

Gartner Business Intelligence Summit, April 2-4, 2012

Talking points

The Changing Enterprise Data Landscape : Key Drivers

How are enterprises adapting to the changing landscape?
Fox Filmed Entertainment’s Perspective

IBM’s integrated data platform solution

The Changing Enterprise Data Landscape
Key Drivers

1. Analytics gives you an advantage

Organizations that lead in
analytics outperform those Top Performers are more
who are just beginning to likely to use an analytic
adopt analytics approach over intuition

Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study.
Copyright © Massachusetts Institute of Technology 2010.

2. Big Data is Here

12 TB of tweets being
created each day.

Unstructured data will grow by
More than 30M networked
sensors, growing at a rate
>30% per year.
650% in 5 years
Every day, the NYSE captures
1 TB of trade information.

3. Complexity is out. Simplicity is in
Emergence of Data Warehousing Appliances

• Dedicated devices
• Optimized for purpose
• Complete solution
• Fast installation
• Very easy operation
• Standard interfaces
• Low cost

4. Parallel Data Processing is commoditized

The Changing Enterprise Data Landscape

Workloads
Big Data Advanced Analytics

Deployment
Models
Appliances Cloud

SQL
Data Access
Real Time Map Reduce

How are Enterprises Adapting to the
changing landscape?
Fox Filmed Entertainment’s Perspective

Who are we?
Fox Filmed Entertainment is a global leader in movie production and distribution

Existing Solution
Traditionally, We have focused our efforts on solutions within our Business Units
leading to Data Silos across the enterprise

Industry Challenges
Explosion in format and channels through which content is viewed and delivered

Digital Media

Changing Release windows

Enterprise Information Framework
Establish an Information framework to support the changing landscape of the business

Key Considerations

Technology
Data Management

BI Platform Business Intelligence capabilities

Business Analytic capabilities

Agile Methodology

Process
Information Governance

Program Management

People BI competency Center

Enterprise Data Warehouse Platform
You have many choices. What is right platform for your organization?

Cloud Appliance
Software Hardware

How can we leverage Social Data?
How can we transform the Enterprise? 8
How can we transform the Enterprise? 8

Last Updated: 2-25-12 Proprietary and Confidential to FFE

Enterprise Data platform – Key Considerations

Analyze Extreme Volumes of Data

Analyze Wide Variety of Data

Enterprise Data
Analyze Data in Real Time
Platform

Discover & Experiment

Governance

Smart Consolidation for Smart Analytics

Netezza
• Extreme Performance
• In-Database Analytics
• Scalable Appliance

Streams
IBM’s Big Data • Act on Data “In-Motion”
Platform • Real time analytics
• Alerts/Actions

Big Insights
• Hadoop
• Unstructured Data
• Complex Analytics

IBM Netezza – Powering the Age of Analytics

Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning. In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others. Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long. With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.

Platforming the Major Analytic Use Cases for Modern Engineering

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We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management. In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX). Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure. Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes. Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.

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Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success. Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.

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This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenge today of how to prepare for AI in the organization and how to plan AI applications. The foundation for AI is data. You must have enough data to analyze and build models. Your data determines the depth of AI you can achieve — for example, statistical modeling, machine learning, or deep learning — and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.

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The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.

This is the age of analytics—information resulting from the systematic analysis of data. Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment. The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations. Success measures of a data initiative may include: • Creating a competitive advantage by fulfilling unmet needs, • Driving adoption and engagement of the digital experience platform (DXP), • Delivering industry standard data and metrics, and • Reducing the lift on service teams. This green paper lays out the framework for building and customizing an effective data and analytics operating model.

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Data catalogs, business glossaries, and data dictionaries house the metadata that builds organizational confidence in your data. First and foremost, the people in your organization need to be engaged in leveraging the tools, understanding the data that is available and who is responsible for the data, and knowing how to get their hands on the data they need to perform their job function. This metadata will not govern itself. Join Bob Seiner for the April RWDG webinar, where he will discuss how to govern the metadata in a data catalog, business glossary, and data dictionary. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must be governed. Learn how to govern that metadata in this webinar. Bob will discuss the following subjects in this webinar: • Successful Data Governance relies on value from very important tools • What it means to govern your data catalog, business glossary, and data dictionary • Why governing the metadata in these tools is so important • The roles necessary to govern these tools • Value expected from governing the catalog, glossary, and dictionary

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While BI dashboards are great at democratizing analytics in organizations, the architecture that traditionally powers them has hidden consequences that have serious impacts on the business. This architecture is based on a 30-year-old paradigm that requires many different systems, ETL jobs, and copies of data in data marts, data warehouses, and BI extracts. One downside of many is that it takes many days if not weeks to answer a different business question with this architecture. The negative consequences are further multiplied by the tens, hundreds, or even thousands of dashboards needed to run a data-driven organization. Now, there’s a straightforward way to overcome these challenges that many organizations are already taking advantage of, an open cloud data lake architecture and Dremio Join Jason Hughes, Technical Director at Dremio, for this webinar to learn how you can migrate BI dashboards to Dremio to quickly provide interactive dashboards to data consumers without the issues of the traditional architecture — and finally deliver the benefits always promised by BI. What you’ll learn: • Why BI dashboards’ traditional architecture implemented at scale causes many issues, which hinder the very insights it promises. • How a Dremio-powered cloud data lake architecture eliminates or mitigates the negative consequences of the traditional approach. • Step-by-step instructions for migrating a BI dashboard to run directly on a cloud data lake, both a self-contained example and your own dashboards.

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Learn about the business demands driving modernization, the benefits of doing so, and how to get started. Can your data and analytics solutions handle today’s challenges? To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data. In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company. We cover: -The business demands and industry shifts that are impacting the need to modernize -The benefits of data and analytics modernization -How to approach data and analytics modernization- steps you need to take and how to get it right -The pillars of modern data management -Tips for migrating from legacy analytics tools to modern, next-gen platforms -Lessons learned from companies that have gone through the modernization process

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ADV Slides: Modern Analytic Data Architecture Maturity Modeling

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Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program. Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity. This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.

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At Fifth Third Bank, about 450 people use data every day. They all start with Alation. But this wasn't always the case. In fact, getting hundreds of folks working in sync has been a monumental task. Just ask Greg Swygart, VP of enterprise data at Fifth Third Bank. Greg has led data consumption and interaction efforts since adopting Alation. Currently he’s scaling out data literacy for Fifth Third, replicating data capabilities to all roles across the company. Join Greg to learn how Fifth Third Bank moved from a command-and-control governance approach to non-invasive — and reaped the benefits. Greg will be followed by Bob Seiner, creator of Non-Invasive Data Governance, who will speak to data governance’s evolution, with an eye to what’s next. In this webinar, you'll learn: • About Fifth Third’s transition away from command-and-control governance • How Fifth Third leverages Alation as its data marketplace for curation & consumption • Why words matter when driving adoption • About the data catalog — and its role in human management

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To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents. In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.

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The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation. 1. Program Management 2. Data Quality 3. Data Architecture 4. Metadata Management 5. Privacy

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År 2013 kommer nästan 70 % av företagens driftkostnader att läggas på existerande IT. Endaste en av fem organisationer lägger idag mer än 50 % av IT-budgeten på nya projekt. Med vetskap om den digitala tillväxt vi har framför oss, handlar ödesfrågan enligt Don Boulia, Vice President Strategy, IBM Software Group, därför om hur väl ett företags infrastruktur klarar förändringarna. Nya lanseringen i familjen IBM PureSystems, PureData, adresserar utmaningarna med stora datamängder. Talare: Don Boulia, Vice President IBM WebSphere Cloud, Per Fredriksson, IBM PureSystems Executive Architect Besök http://smarterbusiness.se för mer information.

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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application. In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics. Length: 30 minutes Session Overview ------------------------------------------- During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana: - What out-of-the-box solutions are available for real-time monitoring JMeter tests? - What are the benefits of integrating InfluxDB and Grafana into the load testing stack? - Which features are provided by Grafana? - Demonstration of InfluxDB and Grafana using a practice web application To view the webinar recording, go to: https://www.rttsweb.com/jmeter-integration-webinar

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The New Enterprise Data Platform

1. Sarath Ramaswami, VP of IT Krishnan Parasuraman, CTO, Digital Media Fox Filmed Entertainment Netezza and Big Data, IBM The New enterprise data platform : Emergence of the analytics appliance and big data Gartner Business Intelligence Summit, April 2-4, 2012

2. Talking points The Changing Enterprise Data Landscape : Key Drivers How are enterprises adapting to the changing landscape? Fox Filmed Entertainment’s Perspective IBM’s integrated data platform solution

3. The Changing Enterprise Data Landscape Key Drivers

4. 1. Analytics gives you an advantage

5. 1. Analytics gives you an advantage Organizations that lead in analytics outperform those Top Performers are more who are just beginning to likely to use an analytic adopt analytics approach over intuition Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts Institute of Technology 2010.

6. 2. Big Data is Here 12 TB of tweets being created each day. Unstructured data will grow by More than 30M networked sensors, growing at a rate >30% per year. 650% in 5 years Every day, the NYSE captures 1 TB of trade information.

7. 3. Complexity is out. Simplicity is in Emergence of Data Warehousing Appliances • Dedicated devices • Optimized for purpose • Complete solution • Fast installation • Very easy operation • Standard interfaces • Low cost

8. 4. Parallel Data Processing is commoditized

9. 5. Latency is out. Real Time is in

10. The Changing Enterprise Data Landscape Workloads Big Data Advanced Analytics Deployment Models Appliances Cloud SQL Data Access Real Time Map Reduce

11. How are Enterprises Adapting to the changing landscape? Fox Filmed Entertainment’s Perspective

12. Who are we? Fox Filmed Entertainment is a global leader in movie production and distribution

13. Existing Solution Traditionally, We have focused our efforts on solutions within our Business Units leading to Data Silos across the enterprise

14. Industry Challenges Explosion in format and channels through which content is viewed and delivered Digital Media Changing Release windows

15. Enterprise Information Framework Establish an Information framework to support the changing landscape of the business Key Considerations Technology Data Management BI Platform Business Intelligence capabilities Business Analytic capabilities Agile Methodology Process Information Governance Program Management People BI competency Center

16. Enterprise Data Warehouse Platform You have many choices. What is right platform for your organization? Cloud Appliance Software Hardware

17. How can we leverage Social Data? How can we transform the Enterprise? 8 How can we transform the Enterprise? 8 Last Updated: 2-25-12 Proprietary and Confidential to FFE

18. IBM’s integrated data platform offering

19. Enterprise Data platform – Key Considerations Analyze Extreme Volumes of Data Analyze Wide Variety of Data Enterprise Data Analyze Data in Real Time Platform Discover & Experiment Governance

20. Smart Consolidation for Smart Analytics Netezza • Extreme Performance • In-Database Analytics • Scalable Appliance Streams IBM’s Big Data • Act on Data “In-Motion” Platform • Real time analytics • Alerts/Actions Big Insights • Hadoop • Unstructured Data • Complex Analytics

21. IBM Netezza – Powering the Age of Analytics

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