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Twitter Tag: #briefr The Briefing Room
Reveal the essential characteristics of enterprise software, good and bad
Provide a forum for detailed analysis of today’s innovative technologies
Give vendors a chance to explain their product to savvy analysts
Allow audience members to pose serious questions... and get answers!
Mission
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Topics
October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS
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Parallel Universe
Ø Crossing the chasm
Ø Reinvent data movement
Ø Recast data transformation
Ø Refresh your team and vision!
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Analyst: Robin Bloor
Robin Bloor is Chief Analyst at The Bloor Group
[email protected] @robinbloor
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RedPoint Global
RedPoint Global is a data management and integrated marketing technology company
RedPoint Data Management offers solutions designed for master data management (MDM), collaboration and architecture integration
RedPoint’s Hadoop-powered, YARN-compliant application provides a scalable and cloud-friendly solution for big data
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Guest: George Corugedo
George Corugedo is Chief Technology Officer & Co-Founder at RedPoint Global Inc. A mathematician and seasoned technology executive, George has over 20 years of business and technical expertise. As co-founder and CTO of RedPoint Global, George is responsible for leading the development of the RedPoint Convergent Marketing Platform™. A former math professor, George left academia to co-found Accenture’s Customer Insight Practice, which specialized in strategic data utilization, analytics and customer strategy. Previous positions include director of client delivery at ClarityBlue, Inc., a provider of hosted customer intelligence solutions to enterprise commercial entities, and COO/CIO of Riscuity, a receivables management company specializing in the utilization of analytics to drive collections.
4 © RedPoint Global Inc. 2015 Confidential
Overview of the Big Data Journey
Skills are s3ll a scarce resource because ecosystem s3ll immature Technologies being held cap3ve by the “coder” lifestyle Technology is certainly less expensive than other EDW technologies but the total TOC is not as compelling as a simple hardware to hardware comparison Descent into trough not a measure of the technology but a reac3on to the hype No amount of marke3ng hype can violate the laws of physics
6 © RedPoint Global Inc. 2015 Confidential
Big Data Arriving Faster Than Predicted Because Facilitators Are
7 © RedPoint Global Inc. 2015 Confidential
Why the Synergies
The Cloud makes data capture easy Simple to subscribe to PaaS services that manage Big Data Real 3me event hubs make real 3me capture and u3liza3on a subscrip3on
Elas3c Compu3ng allows the infrastructure to expand and contract as needed
Data is available in both batch and real 3me for analysis Results can be stored for ac3on or propagated across a service bus for downstream consump3on
Plethora of unaLended algorithms in the cloud to use for discovery analy3cs More data = more machine learning
More precise ac3ons are taken that generate addi3onal, reinforcing data Automated tes3ng allows the machine learning to further refine classifica3ons No need for absolute truth, can learn from a mere data stream. Analy3cs are prescrip3ve rather than just predic3ve if deployed correctly
9 © RedPoint Global Inc. 2015 Confidential
What’s the Difference Between Supervised Learning and Machine Learning?
Machine Learning with Op0miza0on
10 © RedPoint Global Inc. 2015 Confidential
Machine Learning - Deep Learning Neural Net
No absolute truth NN breakdown data at its lowest form then learn to recombine it These algorithms are very fast and distributable
When used with op3miza3on thousands or millions of itera3ons can be tested Used for op3mizing a metric This is why strategy is so important; pick your metric carefully
11 © RedPoint Global Inc. 2015 Confidential
What Does This All Mean?
Synergy between Cloud Compu3ng (PaaS), Machine Learning will accelerate the adop3on and benefits of Big Data Time horizon for Big Data is not 5-‐10 years but 1-‐3 years Category leaders are already implemen3ng business solu3ons Mainstream enterprises will start implemen3ng next year Depending on where your organiza3on aspires to play, serious considera3on needs to be given to these types of combined solu3ons immediately. Strategy becomes more important than logis3cs and execu3on
12 © RedPoint Global Inc. 2015 Confidential
So Where are You Going to Land?
Are you awash with data and cant seem to get it under control? You know there is value there but you just wish your business processes could take advantage of the insights? Are you limited by the avenues of execu3on that can be empowered by Big Data insights? Is your strategy ar3culated sufficiently to point the technology in the right direc3on? Do you lack the corporate will to undertake this program of change? Can you afford to wait?
13 © RedPoint Global Inc. 2015 Confidential
What to do When you Get back to your desk - #1
“Start small—look for low-hanging fruit and trumpet any early success. This will help recruit grassroots support and reinforce the changes in individual behavior and the employee buy-in that ultimately determine whether an organization can apply machine learning effectively. Finally, evaluate the results in the light of clearly identified criteria for success.” McKinsey Quarterly – June 2015 | by Dorian Pyle and Cristina San Jose http://www.mckinsey.com/insights/high_tech_telecoms_internet/an_executives_guide_to_machine_learning?cid=other-eml-ttn-mip-mck-oth-1509
14 © RedPoint Global Inc. 2015 Confidential
What to do When you Get back to your desk - #2
These resources make the difference between success and failure Keep project going and don’t allow small challenges to prevent progress Focus on strategy and a well defined outcome Be rigorous both in process and analysis Interpret and trumpet early success
15 © RedPoint Global Inc. 2015 Confidential
What to do When you Get back to your desk - #3
Leverage Technologies that are designed to take advantage of Big Data
16 © RedPoint Global Inc. 2015 Confidential
RedPoint Cloud Implementations - Azure
5 x smallA-‐SQL RPI Databases
(S1, Scalable)
IIS
Azure cache
IIS
Azure load balancer
Access and Endpoint Control
Scalable SQL DW(Client Marketing DB)
DevOps Support
AD DCOr
AD FS
CampaignOrchestration
Virtual Network
DataManagement
Event Hub
CampaignOrchestration
Data Management
Availability set
Infinitely ExpandingData Lake
Affinity group
Single Master, Multiple Tenant NodeScaled through telemetry and scripting
Machine Learning
Session Data, Real Time Decision Support
17 © RedPoint Global Inc. 2015 Confidential
Forrester CCCM Wave Report - Leader
Debut in leader wave
#1 in Customer Sa3sfac3on
#1 in Cross Channel Integra3on
#1 in product strategy/roadmap
#1 User Interface
18 © RedPoint Global Inc. 2015 Confidential
RedPoint Ranked First for Data Quality and Data Integration
19 © RedPoint Global Inc. 2015 Confidential
Summary for Success
Recognize this is the 3me. Category leaders are already there. Mainstream enterprises are moving in next. The enabling technologies have come together to make this accessible to anyone. Step back from the logis3cs and focus on strategy. Its how you point the machine to align to your objec3ves. Start small, think big, scale fast and trumpet results. Find the right resources that can work across disciplines. Be rigorous. There has been too much hype in this space, be the counter-‐hype. Don’t fear failure; just do it quickly and move on
20 © RedPoint Global Inc. 2015 Confidential
Keep Calm and Hadoop On!
www.redpoint.net [email protected]
Why the “Big Data Hype Cycle” is Misleading
u Big Data is an ecosystem, not a technology – which distorts the picture
u Some analytics applications have experienced “absurd acceleration”
u Hadoop is, in many instances, the laggard
u Nevertheless, Hadoop is growing like bamboo in spring
The Necessity
Forbes: Recent academic research found that companies that have incorporated data and analytics into their operations show productivity rates 5 to 6 percent
higher than those of their peers.
What Is a Data Scientist?
u Project manager u Qualified statistician u Domain business
expert u Experienced data
architect u Software engineer
(It’s a TEAM)
The Corporate Culture Issue u Some companies have an
established analytics culture, but most do not
u Establishing one is not a simple task
u A corporate analytics structure can disturb the corporate hierarchy
u Even where one exists, great technology opportunities/pitfalls exist
u Analytics is, or has become, disruptive
The Technology Issue u Technology maturity varies u In particular, the Hadoop
stack varies and needs careful consideration in respect of components and distros
u By comparison the analytics S/W is mature
u Streaming architectures are less mature (lambda architectures are relatively new)
u It all needs to support an end-to-end business process
u How easy is your technology to implement? Describe the process for a typical customer.
u What do you regard as the normal priorities for establishing an enterprise level analytics capabilities?
u Is there an ROI calculation of any kind that applies?
u There’s clearly a trend to low latency analytics. How do you see this developing?
u How does this relate to the usual BI applications, or doesn’t it?
u How much integration work is necessary?
u Is there any Hadoop distribution that you prefer? If so, why?
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October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS