BigDataNYC

how often are you calling people for data? and then not knowing how it's been transformed [tampered] and whether it's fit for purpose

Jeff Frick Can you expand on this?

Dan Hushon how many people would reuse a piece of binary code?... for me a derived data set that has been massaged for one purpose may not fit your purpose

I agree that we will see the rise of what I call "graph software" which will create the "next Google" bc search is the big problem..clutter is a discovery and navigation problem to "data"

Are enterprises viewing the challenge this way? Really changing the construct.

And the elevation of the second order info, the meta data. interesting

John Furrier10

Topic: Data Science? What is the role and who are the people and skills needed?

Crowd Captain

Data science should be everyone in the organization according to Florian Zettelmeyer who spoke at #GE event last week

Jeff Frick I heard Florian talking about learning some basic data science methods and vocab, POV, but aren't we trying to democratize data, make it actionable, in the hands of the people?

Crowd Captain

@kelloggschool posted https://twitter.com/KelloggSchool/status/388364388266037250

KelloggSchool

How can you derive tangible business value from #BigData? Florian Zettelmeyer explains [VIDEO]: http://t.co/7RWC0POhwF #IndustrialInternet

7 days ago

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Crowd Captain @gesoftware https://twitter.com/GEsoftware/status/387965277800906752

GEsoftware

Most important skills of analytics R not technical; they R thinking skills. -Prof. Florian Zettelmeyer #IndustrialInternet @KelloggSchool

IMHO Data scientists combine technical know-how & curiosity to analyze massive amts of data to deliver insights/answers to help solve real business problems.

Sylvie Otten (Sollod

They’re part technologist, scientist, researcher, business analyst, mathematician, statistician, economist and engineer!

Jeff Frick Difficult job description for the new hire

Sylvie Otten (Sollod @JeffFrick ;-) They just need to possess the skills - not necessarily the job description. ;-) I think many folks today already have many of these skills - doesn't mean you have to be expert in all of them - just passionate about them.

AnalystOne

I always loved the @josh_wills data scientist definition: Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

role should be to identify insights that can be productionized and rolled out to business users

Jeff Kelly14

What are the data governance and security implications for self-service analytics? Should business users be let loose on any data source they like? Where does IT fit in this equation?

AnalystOne

Wow more great questions. Very thought provoking. Need more than Twitter to deal with this one...

Data Governance, Data Architecure are extremely important. IT has to provide security and governance for #BigData Analytics to suceed

IT drives this imho bc cloud opens up a can of worms wrt security bc it differs from on-prem vs cloud.. very important that SLAs on the front end meet the cloud security SLA

AnalystOne

What if an enterprise user generates conclusion based on new self service BI and mis-uses info? There are endless scenarios where that could happen. And I can't think of an auditing system that would stop that.

Jeff Kelly if decisions being made are strategic "big picture" decisions, users should have to justify their decisions to others - self-serve analytics is a tool, not the final arbiter of decisions

why isn't there a whitelist / blacklist registry

and I also believe that tools like #chorus may provide for peer review of analytics through visible analytic process

Jeff Kelly great point - analytics should be a collaborative process

#SelfServiceAnalytics does not equal chaos. It too, can be governed

it's funny that we talk about governance, and yet don't recognize the importance of peer review in science ... proofs versus theorems

Suzanne3

Decisions made at rt contextual level. No need to include excess granularity. Limit data to relevance, but provide rich view with diverse sources.

seems like putting a measuring stick to these could be a bit tricky. When do you know you're getting into excess granularity? How does the LOB person know when to limit (no more value) vs when to add (data diversity for richness)

Can this be automated for the Non-Data Scientist?

AnalystOne13

Question: is the age of Big Data infrastructure on decline, to be replaced by rise of end user tools? #DataAnalytics

Is a data pool, or data ocean, additive, or subtractive to Big Data Infrastructure as you've defined it here?

John Furrier Thank God you didn't say "data lake" - i hate that term :-)

no brainer on the rise.. the market for big data apps hasn't come to life yet bc the killer app is visualization which is being paced by analytics which is waiting on infrastructure #DWBI #ConvergedInfrastructure retooling

AnalystOne Love that reply but thought infrastructure was coming along very well. No?

Seabourne

Big Data is only getting bigger, and more disparate :)

John Furrier both volume and diversity from folks that I talk to.. at @sap @sapphirenow 4 yrs ago we talked about fast data..now #industrialinternet #iot highlights machine to machine data small and fast but very important

the underlying #BigData infrastructure is the enabler of effective and game-changing #analytics - can't have one without the other IMO

also AWS is showing the model for integrating stacks for rapid developer productivity and their next step will be big data apps to be followed by @rackspace @pivotal @ibm @hp etc

Jeff Frick2

How has the evolution of visualization techniques and technologies changed the game? Hard to see a pattern in a Billion of anything. What's the next big step here?

intelligent visualization that adapts to the best way to display insights in large data sets

Jeff Frick But how much does the visualization choice shape the perceived answer? Age old trick, to show high growth, big spaces between points on Y axis. To show low growth, small space between points Y axis. Adjust the place where X axis crosses.

Jeff Frick is there a corollary with the more complex visualization tools?

Actually easier to pattern in a Billion than wade through a BI report

important to ahve flexibity of user choice in #Viz created for analysis. Each to their own #Tableau

Suzanne1

Just bcuz the data is available does'nt mean it should be used pervasively. Right Data for rt process still an imperative. Variety & source for richer decisions, not only more quantity

Jeff Kelly3

Yes, companies like Piedmont Healthcare, Paychex and even Apple are moving to self-service BI tools without needing to make large infrastructure investments > I am quoting @seabourneinc http://twitter.com/seabourneinc/status/390918507007070208

seabourneinc

@jeffreyfkelly Have you seen good self service approaches? Seems that proposing new infrastructur... #bigdatanyc via https://t.co/I5iMzEleEC

2 minutes ago

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Suzanne4

#BI defines a framework for decision validation. #DataAnalytics represents interactive process of understanding decision process. 3G of #BI with #Tableau

4 Votes Vote

Interactive, seems like a key point, might have a partial hypothesis, but those change as new patterns emerge, check a different path

Jeff Kelly Analytics is fundamentally an iterative process, must be interactive to ask questions, get an answer, ask another question

Crowd Captain

the crowd call agree that Tableau really shines the light on the value of getting at data fast and in many different ways..kills any argument for old model of BI and DW

good distnction - with traditional BI, you model the underlying data to answer predefined questions - interactive analytics allows for iterative investigation of data

Suzanne1

Hammer & Nail scenario. All HDFS needs to look like SQL today for tools to be able to provide value. Extracts circumvent the problem, but wastes the computing pwr.

some say compute will be unlimited so not an issue