In this webinar, we hear from John Myers of Enterprise Management Associates about the drivers associated with big data implementations, evolving technical requirements for big data environments such as the Hybrid Data Ecosystem and how a robust information management layer is important to big data projects.
We also discuss how big data is evolving as a practice and we are quickly approaching a point at which data will be treated as a single source rather than divided between analytic and operational, big data and traditional enterprise data types, and multi-structured data stores and relational databases.
To learn more, visit: www.snaplogic.com/big-data
Exploring the Future Potential of AI-Enabled Smartphone Processors
Webinar: The Hybrid Data Ecosystem: Are You Battling an Illogical Data Warehouse?
1. The Hybrid Data Ecosystem:
AreYou Battling an Illogical
Data Warehouse?
Live Webinar
Wednesday
April 24, 2015
2. Today’s Featured Presenter
John L Myers
Managing Research Director
Enterprise Management Associates
JMyers@EMAusa.com
@johnlmyers44
John has over 10 years of experience working in areas
related to business analytics in professional services
consulting and product development roles.Additionally,
John helps organizations solve their business analytics
problems whether they relate to operational platforms,
such as customer care or billing, or applied analytical
applications, such as revenue assurance or fraud
management.
3. Topics
• What’s Driving Data Management Evolution?
• Internet ofThings
• Data Lakes
• Speed of Deployment
• Brief Introduction to SnapLogic
• Where to Go From Here
• Question and Answer
4. Poll Question #1
Where are you on your big data journey?
A. Live in Production………...13%
B. Test and Development…….7%
C. Still Evaluating…………….27%
D. Just Researching…………..53%
10. Poll Question #2
What are your primary big data use cases?
A. Customer analytics………………………….…14%
B. Operational analytics (machine, devices, etc.)…14%
C. Data-driven products and services (IoT)……….7%
D. Fraud and compliance…………………………...0%
E. Archiving data…………………………………...0%
F. Other…………………………………………....7%
G. Don’t know yet/researching…………………...57%
16. Poll Question #3
How are you thinking about big data management/ Hadoop
relative to your current enterprise data warehouse?
A. We want to replace current
approaches if possible…………14%
B. It must complement current
approaches…………………….57%
C. Too soon to say………………..29%
17. Where to Put This Information:
Data Lakes
Slide 17
26. Today’s Agenda
• What’s Driving Data Management Evolution?
• Internet ofThings
• Data Lakes
• Speed of Deployment
• Brief Introduction to SnapLogic
• Where to Go From Here
• Question and Answer
27. • Experienced Team: Leadership from Informatica,
Salesforce, Sybase, Cognos, CA
• Headquarters: San Mateo, California
• Investors: Andreessen Horowitz & Ignition
• Advisory Board: AstraZeneca, HP, Symantec,
Yahoo
• Customers: Adobe,Acxiom,AstraZeneca,
Blackberry, Bloomin’ Brands, CapitalOne, Cisco,
Cognizant, Danone, Fox Sports, GamesStop, GE,
HP, IDG, iRobot, RocketFuel,Target,Yelp, Uber, USF,
Xactly
SnapLogic: Unified Platform to Connect Faster
28. Why SnapLogic Elastic Integration?
Modern ConnectedProductiveUnified
We can do more in two hours with SnapLogic than we
could in two days with traditional solutions.
31. Big Data Integration in a Snap
@SnapLogic
Facebook.com/SnapLogic
Plus.google.com/+SnapLogic
Learn more at!
www.SnapLogic.com!
!
Notas do Editor
The buzz factor generated by the business press as well as the IT pundits has eclipsed most any coverage in the past 10 years. The challenge with this coverage is to cut through the buzz words and get down to whether big data can drive change and provide a greater level of insight. How many of you have an executive who wants you to run down to the store and pick up a couple of those Hadoop things? Big data is not about Hadoop—it is about analytics and business insights.
Internet of Things Adoption
It is currently adopted and an essential part of our business 17.0%
It is currently adopted and an important part of our business 29.3%
It is currently adopted and mostly supplemental 16.8%
It is planned for adoption in the near future (3-6 months) 10.5%
It is being researched in next year (6-12 months) 15.6%
It is not planned for research or adoption 10.5%
Connected Devices
Geo-location devices (e.g., information from smartphones) 14.712918660%
Corporate infrastructure devices (e.g., office building thermostats; HVAC control) 13.875598086%
Manufacturing and production devices (e.g., assembly line; restaurant food prep) 10.167464115%
Connected fleet vehicles (e.g., fleet trucks; planes) 9.449760766%
Consumer infrastructure devices (e.g., home thermostats; heating and cooling) 8.612440191%
Consumer durable goods (e.g., television; washing machine; refrigerator) 8.373205742%
Utilities infrastructure (e.g., electrical, water) 8.133971292%
Consumer connected vehicles (e.g., personal automobile) 7.177033493%
Government connected fleet vehicles (e.g., city buses; state road maintenance) 6.578947368%
Bio-medical devices (e.g., Fitbit; heart monitor) 6.459330144%
Government infrastructure (e.g., roadways and bridges) 5.861244019%
Challenges to Implementing a Data Lake
Lack of information management/data integration to link existing platforms to data lake 22.8%
Lack of executive backing for data lake concept 21.9%
Existing data management environment will not support scaling 21.1%
Freshness (or lack thereof) of data in the data lake (i.e., fear of data swamp) 17.2%
Unclear business case for data lake implementation 16.8%
Preferred Big Data Project Implementation Method
Customizable applications from external provider 20.3%
Hand-rolled development by internal resource(s) 18.0%
Custom development by external consultant(s) 17.3%
Productized development by external provider 15.6%
Stand-alone products from external provider(s) 14.2%
Bespoke development by internal staff contractor(s) 13.8%
Implementing Big Data Integration
Internal design patterns for data processing 22.6%
Software vendor schemes for multi-structured data parsing/processing 20.9%
Industry data models for target data source configuration 20.2%
Software vendor designed blueprints for data integration processing 18.2%
SnapLogic is the industry’s first unified data and application integration platform as a service (iPaaS). Our vision is to help enterprise IT organizations and lines of business to connect faster and we're working with enterprise IT organizations who are re-thinking their data, application and API integration layer. Customers like Adobe, Cisco, Target, GE are finding that they are able to do these things as much as 5x faster by using a modern integration platform that is built to handle both streaming and batch-oriented big data integration.
Founded by Gaurav Dhillon, who also founded and ran Informatica for 12 years, taking the company through a successful IPO. SnapLogic is backed by Ignition Partners and Andreeson Horowitz and our management team is world class. We’re based in San Mateo, California and we have offices in Colorado, Boston and New York with plans to expand to Europe later in 2015.
There are 4 primary reasons companies choose SnapLogic to connect faster.
We’re a unified platform for data and application integration
Our productive user experience is designed to enable so-called “citizen integrators” and advanced users to accelerate time to value.
Our modern architecture is built for web scale, with a hybrid architecture that respects data gravity.
We have 300+ connectors, called Snaps, to small and big data sources, cloud and on-prem business applications, virtually any data, app, API technology you can think of.
Before we discuss use cases and dive into a custom demonstration based on your requirements, I’ll review each of these in a little more detail.
Ovum quote – SnapLogic SWOT assessment March 2015: http://campaigns.snaplogic.com/rs/snaplogic/images/assessment-snaplogic-elastic-integration-platform.pdf