fit for purpose: preventing a big data letdown

Grab some coffee and enjoy the pre-show banter

before the top of the

hour!

The Briefing Room

Down to Business: How to Ensure Success with Big Data

Twitter Tag: #briefr The Briefing Room

Welcome

Host: Eric Kavanagh

[email protected] @eric_kavanagh


  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


Topics

October: DATA MANAGEMENT

November: ANALYTICS

December: INNOVATORS


Parallel Universe

Ø  Crossing the chasm

Ø  Reinvent data movement

Ø  Recast data transformation

Ø  Refresh your team and vision!


Analyst: Robin Bloor

Robin Bloor is Chief Analyst at The Bloor Group

[email protected] @robinbloor


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


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.

Fit for Purpose: Preventing a Big Data Letdown October 2015

2 © RedPoint Global Inc. 2015 Confidential

The Gartner Hype Cycle


Overview of the Big Data Journey


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


Looking Deeper into the Reports


Big Data Arriving Faster Than Predicted Because Facilitators Are


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


How the Cloud and PaaS Facilitates Big Data Utilization


What’s the Difference Between Supervised Learning and Machine Learning?

Machine Learning with Op0miza0on


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


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


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?


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



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



Leverage Technologies that are designed to take advantage of Big Data


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


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


RedPoint Ranked First for Data Quality and Data Integration


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


Keep Calm and Hadoop On!

www.redpoint.net [email protected]


Perceptions & Questions

Analyst: Robin Bloor

Hadoop: The Agony and The Ecstacy

Robin Bloor, PhD

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

The Reality

The value is only ever delivered by ACTIONING the analytical discoveries

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?


Upcoming Topics

www.insideanalysis.com

October: DATA MANAGEMENT

November: ANALYTICS

December: INNOVATORS


THANK YOU for your

ATTENTION!

Some images provided courtesy of Wikimedia Commons and http://thespiritscience.net/2014/11/02/scientists-propose-parallel-universes-really-exist/