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Thanks Doug and All Analytics for this e-chat !

 

Prospector

Thanks, Doug, and everybody for participating. Look for a wrap up post and link to the archived chat here on AllAnalytics.com tomorrow.

Blogger

Thank you! Whew! A real chat on fire!

Prospector

Thanks all.  Very interesting.

Prospector

Doug, I would like to thank you for clearing up alot of questions I had about DS - I hope you can come back again, since I am sure we have only skimmed the surface of a really interesting field !

Blogger

Some plugs:  Find me @doug_laney on Twitter. Join our #GartnerChat tweet-up on analytics, big data, collaborative BI tomorrow at 1pm ET. And dont forget about our BI/Analytics summit in LA Apr2-4!  See you there! Cheers!!

Prospector

You're on, Doug! Thanks loads

Blogger

Doug. Did you havean upcoming chat you wanted to plug?

Blogger

To find and cultivate a DS, get the person with the best aptitude in methods and stats.  The rest comes with time.

Data Doctor

On behalf of Gartner, I really appreciated the opportunity to chat with an learn from you all. Thanks to Shawn & co at All Analytics. Let's do it again real soon! 

Prospector

And you probably better get yourself some before they're all taken! :-)

Prospector

Correct - it is the IT knowledge and science/analysis knowledge that would be harder to teach.

Prospector

Yes, the "data scientist" title may be in vogue and upset some folk's sensibilities. But the reality is that companies actually are hiring and deploying talent to take them beyond basic BI. Call it what you will.

Prospector

I guess we're getting to the end of our hour. Final thoughts?

Blogger

Absolutely Sam. But that road is shorter than teaching someone with even nominal stats background what's needed to be a good data scientist. Agreed?

Prospector

Doug - I can understand the question but field knowledge and company knowledge help me understand the data.  I work a lot with federal data sets on the science education and science workforce and you have to understand how the data was collected to know the strength/weaknesses of a variable even over time.  It takes time and many conversations to get there. 

Prospector

So to sum up, Doug, data scientist is certainly more than just a title but a critical role desperately needed by any organization in need of intelligently leveraging data.

Blogger

BK's got it right. It's certainly easier to train a good DS on the business than train a good business person on advanced analytic techniques and tools.

Prospector

In general, the best answer for how to define and develop the role of the data scientist is - 'it depends'.  Whether the organization is private sector, public sector or academia; large firm or small firm, long time in the industry or a brand new competitor; whether the investigative results will impact profits or public policy, etc. - all these factors impact how the DS role is defined and deployed.

Data Doctor

The benefits of both problem solving and also exploring and innovation are clear, then.

Blogger

Sam - Agreed.  An ability to absorb new concepts quickly and to incorporate them into your actual efforts is paramount.  Really exciting and certainly not for the faint at heart ! :) 

Blogger

As to where to sit a DS-- If working on optimizing a particular business problem, then very close to the business (with access to IT/data).  And if working more on broad innovation/discovery, then perhaps in a specialized analytics group. My first summer job in IT was as a summer SAS programmer at Abbott Labs in the Biometrics Dept--servicing the deep analytic needs of the entire company. It's a good org model to think about for any kind of business today.

Prospector

Polya's heuristics for making a problem more acessible include analogy, generalization, induction, variation, auxiliary problems, decomposing, etc.

 

Prospector

That is where training in methods comes in - whether to approach an investigative opportunity analytically (disaggregating the data) or synthetically (aggregating the data).  It is the difference between hypothesis testing and data mining, but it all relates to the approach and which statistical tools are best to do the job. Subject matter expertise is great, but if you are just starting out, you can pick up the subject matter knowledge as you go along.

Data Doctor

Along the lines of sam.held's point, I'm really wondering how much the data scientist is exploring and how much they are just seeking answers posed by the enterprise.

Blogger

Louis - I think you are correct and that then brings up the issue of knowledge of the business/field.  Would I be effective if I was not familiar with local customs - standard data pieces, views, etc.  But I think what makes a good DS (and BA in that case) is learning those quickly and adapting.

 

Prospector

I'm sure there are Shawn. But one great scientific skill is the ability to draw analogies. A fresh set of eyes and perspective can do wonders.

Prospector

Isn't it also part of the data scientists job to advise on data to collect?  In program evaluation, it is the evaluability assessment step -- are you ready to be evaluated - is the correct data points in place.  If not, do we step in and tell the business folks what to do?  So far, this all sounds post-hoc to me.

Prospector

There's some thinking among my Gartner colleagues embroiled in this debate that the DS perhaps focuses more on causation and the BI analyst more on correlation. I think that's a fair simplified argument.

Prospector

I wonder if there are limits to that, Doug.

Blogger

Good Point Beth , What good is being able to uncover details of data mining ...etc  if you can 't apply it to Business objectives ?

Blogger

I agree with Sam.  The smaller the company the more hats one has to wear.

Master Analyst

Good Q Shawn. How industry/function knowledgable does a DS have to be?  Are the principles and therefore skills transferable across domains?  To some degree, yes, I think.  A good DS could probably be a good DS in any industry and with almost any problem.

Prospector

I do think the skill set the data scientist has to have depends on the size of the company - bigger companies = more specialized role/smaller company = more generalized role

Prospector

Beth, Briefly data modeling is about logically organizing and integrating data to optimize access. Business modeling is about understanding the way processes work, are affected/enabled by data to optimized performance. But this crowd probably already gets that.

Prospector

Doug, here on AllAnalytics we often circle around the data vs. intuition aspect of building a fact-based decision-making business culture -- and I think we're at it again when you talk CFO vs. marketing. The message being, you can have data scientists galore, but we still need the business savvy to succeeed

Blogger

Doesn't the source of talent also depend upon the kind of enterprise? Is data science really all the same regardless of the data being analyzed?

Blogger

And finance orgs really don't deal much with the big challenge of "big data" the way other parts of the org do.

Prospector

Good point Pierre, often University focus more on theory, real world application has to be stressed but yes the skills they garner will be for the most part outdated by time they enter the field.  But I guess it is a start and we have to start somewhere.

Blogger

Gary, Yes my friend and former colleague Tom Davenport is right on the...er...money.  CFOs have typically led when it comes to having influence and certainly basic quant skills. But I think those that come from marketing can (and often are) sharper quantitatively. The issues are more complex there.

Prospector

Doug, any thought's on Gary's question, "Doug ... my question about if the CFO function might drive the adoption rate of analytics is due in part because Tom Davenport gave a keynote presentation at a major CFO conference speculating that the CFO function has both influence plus quantitative skills. Your thoughts?"

Blogger

@Gary - in the federal sector there is a similar discussion among CFOs and CIOs of the agencies about their evolving roles.

 

Prospector

I'm not sure it isn't. Just wondering.

Blogger

I agree with Beverly.  I am a nuclear physicist by training but worked on the computing analysis side.  I needed a little formal computer science training but that comes easier than training the science/analysis/statistics skills.

 

Prospector

Unfortunately, I don't think there's a silver bullet for cultivating the skills. Certain BI analysts will rise to develop real stats & business modeling skills.  And certain business people will rise to develop the data-management part of the equation. But implementing a corp culture that values data, facts, analysis is a fertile ground.

Prospector

doug -- can you explain the difference between modeling business problems and data?

 

Blogger

Good point, Pierre, and welcome.

Blogger

Which dovetails into Shawn's good question

Blogger

I have to agree with Shawn.  I don't know if academia is the best place to look.

Master Analyst

Doug ... my question about if the CFO function might drive the adoption rate of analytics is due in part because Tom Davenport gave a keynote presentation at a major CFO conference speculating that the CFO function has both influence plus quantitative skills. Your thoughts?

Blogger

Doug, I liked that you framed "business model problems" instead of the data focus - it raises the question of how academia keeps up with changing technology - when one person enters a program, the needs may change by graduation

 

Blogger

Doug. Is academia the best source?

Blogger

I see what are the options for those of us no longer in school officially ?

Blogger

It would be nice to translate this discussion into organizational design - the definitions are interesting, but what does it mean from a practical standpoint. Any best practice in terms of how organizations are structured to most effectively address everything that has to happen for successful analytics?

Blogger

Yes Sam, I think you've nailed it.

Prospector

The professional skills, knowledge and abilities are often derivaties of college training in social, behavioral and physical sciences.  The college environment provides a foundation for systematically deploying intellectual curiousity in terms of methods and statistics. From there, industry-specific experience, mentoring and post-graduate studies do the rest.  Folks of this ilk are just like camera film - they just need time and exposure to develop.

Data Doctor

Doug - it sounds like analysts are practiconers and data scientists are the theory people making sure the information/data is there in a format for them to use. 

This reminds me of the medical field and translational science - application of biological science results in clinical settings. 

Sounds like IT setting it up and business units enacting it.

Prospector

The biggest issue from these universities is teaching students to model business problems, not data.

Prospector

From working with leading universities on their BI/analytics curricula (some of you may know of the BI Congress I advise) that there's a huge push in academia to churn out these folks.

Prospector

Yes Doug  How do we cultivate these skills?  Since the numbers show we are sorely lacking in them.

Blogger

@Doug -- sounds like R&D.  I get a lot of strange looks when I mention R&D to my peers since I'm in marketing, but reallly business needs R&D no matter what industry.

Master Analyst

Good questions Doug. And your thoughts?

Blogger

I'm wondering whether the analysts thinks more about the applications of the science particularly for business purposes. Thoughts?

Blogger

OK, so now we've spent some time discussing/differentiating the role.  How about where we find and how we cultivate these skills?  And where in the org should they sit?

Prospector

smkinoshita -- I like the vet analogy. 

Blogger

 

 

 

 

 

 

 

 

 

 

 

 

So business analyst use the product of data scientists? which doesn't sound so different than the historical quant/analyst division

Blogger

I'm just trying to use parallels from other professions that use science as their base.

Master Analyst

Now whether the typical data scientist is actually a scientist—creating & testing hypotheses, following scientific method etc—that’s still debatable.

Prospector

Yeah, I think the data scientist does a lot more with social science/economics theories and analysis in order to present the information.  I see BI as more of the accounting information. 

Prospector

Maybe it's sort of like:

Vet = Data management, keeps the subject healthy.

Trainer = Analyst, knows how to put the subject to work.

Biologist = Data scientist, knows the ins and out of the subject.  The other two professions use the scientist's expertise to accomplish their missions.  "Vets" don't need to know how the animal's organs developed or what other uses there may be for them, they just need to know how to keep them working.  "Trainers" care more about HOW a subject will behave given a certain stimulous over WHY it does.

How does that sound?

Master Analyst

So my conclusion is that there *is* a difference.  There is something going on in organizations trying to raise their analytic game...and move beyond basic BI.  This is great!

Prospector

Arguably, there is a difference between articulating findings and interpreting results.

Blogger

Words that show significantly more in each type of job description:

Data scientist: design, knowledge, research, complex, learning, machine, models, problems, performance,

BI analyst: reporting/reports, company, technical, industry, user, sql, applications, metrics

Prospector

OK, i don't get "disease," but most other terms seem in there

Blogger

I guess that is what Doug and Beth are saying, that it does not sound so specialized from a data manager/miner

Prospector

So I ran a little "data scientist" experiment myself. I sucked several dozen job descriptions of "data scientist" and "bi analyst" into a wordcloud generator and did some analysis. Have a look: https://docs.google.com/present/edit?id=0Aa7E6TDaLOQNZGRzejJ6ZHhfMTg1ZGo3N3YzY2M

Prospector

but doug, " and persuasively articulate findings" -- doesn't that fall more squarely under Gartner's definition of a business analyst? 

Blogger

But there is, in our definition, a distinction between this and an analyst, no?

 

Blogger

SO the data scientist does not develop the research question or hypothesis?  Just finds the data to answer the question - no analysis, nothing else?

 

Prospector

Anyone who can model business problems, gather and prep data, develop and test hypothesis, drive SAS, SPSS, R, etc. and persuasively articulate findings should feel free to call him/herself a data scientist.

Prospector

Yes Computer Science seems to be a necessary evil.....

Blogger

so doug, data scientist = quant jock?

Blogger

smkinoshita -- I'm on the same page with you. But I agree too with those who call for more of a collaborative view of data science, too. yes, have your formal -- ie titled -- data scientist, but that person shoudl be workign closely with the data managers, IT, the business, etc. 

Blogger

A pretty firm definition, Doug. Nothing vague about that...

Blogger

Here we go. Some Gartner role defs. Feel free to use them (or not).

Information/Data Scientist: Responsible for mining, modeling, interpreting, blending and extracting information from large datasets and then present something of use to non-data experts. These experts combine expertise in mathematics-based semantics in computer science with knowledge of the physics of digital systems.

Business Analyst:  Interpreter of business processes, workflows and requirements into functional specifications for development or application evaluation that will be used by various development teams, process owners and practice areas (such as business intelligence, business applications and information management).

Prospector

For me, the role of a Data Scientist is beyond the typical role of a BI analyst. A Data Scientist not only must have knowledge about the common methods that we can find for doing data mining but also he or she must be capable to create new ones to tackle new kind of data whether they come from the business area or some other radical area

 

Prospector

I think there is a statistical analysis component to it that transcends the tech knowledge.  you have know about data analysis techniques and statistical rules to interpret data correctly.  No data set is complete.

Prospector

Not sure what the problem was, Doug. Just keep rolling.

 

Blogger

From an epistemological perspective, a scientist is a knowledge seeker who uses methods and tools to make an objective assessment. Is this consistent with your concept of a data scientist? And if so, do you see the data scientist as being a describer of data only or should s/he seek to establish normative expectations -what the data should be?

Data Doctor

I'm trying to cut-paste some Gartner role definitions, but when I post they go into a black hole. Trying to figure it out....

Prospector

Which brings up the question of is this really science ?  Worthy of a scientist label ?

Blogger

@Beth:  Yes, because a scientist studies and proves or disproves theories.  Like there's a difference between someone who cares for an animal and one who studies its species specificly. 

Master Analyst

Hi, Betsey. Hi mtroester. Hi Gary. I'm thinking the title also suggests a use of methodology beyond just a tech role.

Blogger

@mtroester   I agree, IT has many of the technical skills set for this, yet companies seem not realize that.

Blogger

Testing...my last post didn't show....

Prospector

so smkinoshita -- so you see the role evolving out of the analytics discipline rather than the data management discipline? (by data management, i mean knowing data flow, database architecture, etc.) 

Blogger

Doug, my question is about organizational change management. Which function might drive the adoption rate of business analytics? Is it possible the CFO's function will as they grow stronger in being a strategic advisor ... KPIs, customer profitability analysis, etc.?

Blogger

I think it's also interesting to consider the other roles that support the data scientist - since data scientist expertise is in short supply, I think we really need to promote the work that IT and business can do to offload the data scientist work - things like IT really understanding the data requirements for analytics so they can help more effectively with the data preparation. What do you guys think about that?

 

Blogger

Different from the BI as the Data Scientist is more devoted to the data and is less business-specific.

Master Analyst

To me, "Data scientist" sounds like someone experienced in measuring and interpretting massive quantities of data as well as knowing how to devise ways to measure things previously thought to be too difficult to measure.

Master Analyst

And following up on my last question doug, since you mention a debate within Gartner, what are the positions?  

Blogger

Hi Everyone! Apologies for being a bit late!

Prospector

@smkinoshita    Yes, I do like the title as well....

Blogger

So tell us then, Doug, what is your definition of a data scientist? What responsibilities? What capabilities? And how does a data scientist differ from a BI analyst, if at all

Blogger

Here's how you described it Doug. "The role of the data scientist -- Defining the role.  Is it hype? How is it different than a BI tool jock? Where to find and how to cultivate talent? How industry knowledgable do they need to be?  Where to place them in the org, How to get the org to understand what they can bring? etc. "

Blogger

We're having on onging debate @ Gartner on this. (And you thought we were all one collective conscious!)

Prospector

John, unfortunately it was a while ago, I would have to look for it ...I am sure it is still out there though ....

Blogger

That said, I think a "Data Scientist" sounds legit and straight-forward.

Master Analyst

Not sure it's "dangerous" per se Beth, but certainly could add confusion and/or set expectations out of whack.

Prospector

I think it's funny how people like to create titles which will baffle others into what exactly they do.  I mean, from a marketing standpoint it can be helpful if people are interested enough to inquire what it is and thus allow for a pitch, but other people will dismiss the person as a charlatan.

Master Analyst

Louis--I'd/we'd like to see that vid later. Have a link?

Prospector

Come on Shawn, stop playing around !  : ) 

Blogger

Hi Doug -- I think that there is definitely a danger in the data scientist become a trendy erudite term, if we're not careful

Blogger

Go for it Shawn.

Prospector

Doug, I don't mean to get too far ahead of the discussion, but  I recently saw a u-tube clip on what it took to become a data scientist, and this person who was working in the field as one, basically lucked into an opportunity to consult a company in a technical compacity and it eventually evolved into a Data Science position.  Do you see that being the case for many until some formal structure is in place ?

Blogger

I'm reminded of once when I had the title "knowledge engineer" on my business card. Not so much in vogue anymore eh?

Prospector

Doug. I was thinking of getting the ball rolling with a brief e-mail discussion we had a few days ago about this tropic. Mind if I share how you framed the topic? I think it's right on target

Blogger

Ok, well let's get going. First there's the question of whether the data scientist role is really different than a BI analysts, or whether its just a trendy erudite term.  Anyone?

Prospector

Ping!  Hello all!

Master Analyst

And how analyst can transition into this role ?   Within the organization  and Freelance Opportunities ?

Blogger

Sorry guys -- server seems a bit slow!

 

Blogger

Well, I'm not sure what happened to Shawn!

Blogger

Shawn--were you going to kick it off with a topic intro, rules of the road, etc?

Prospector

Also, maybe a brief definition of what this role encompasses?

Blogger

As I am, yours. Thanks Louis.

Prospector

Hi Doug I am really excited to hear your thoughts of the state of the Data Scientist.

 

Blogger

Hi everybody. OK let's get started.

 

Blogger

Hi Carlos, and Doug!

 

Blogger

Hello everyone. My name is Carlos Campos. Thank you for inviting me to this chat with Doug Laney.

Prospector

Hi, Doug Laney w Gartner here. Thanks for having me!

Prospector

Yep, if you see your comment, you're in the right place! Just about time to get started.

Blogger

jerryc -- you're in. this is a text-only chat. we haven't started yet, though

 

Blogger

i would like to join you,but I keep going around in circles trying to get in. Can you help? thanks. ..JerryCohen

Prospector

Please join us for a conversation with Doug Laney, VP of Research for Business Analytics and Performance Management at Gartner, as we discuss defining the above role. How does this role differ from others in the business intelligence field? Where do companies find such individuals, how much do they need to know about the industry as a whole, and where do they fit into a company's organizational chart? Doug will be guiding our discussion, posing some questions and sharing some research. Hope you can all join us!  

Blogger


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Analytic Myths: True or False?

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At the recent 2014 SAS Global Forum Executive Conference, five analytics experts give us their perspective on whether four common myths about IT and analytics are true or false.
Beth Schultz
We'll Be Your Eyes & Ears

3|20|14   |   2:15   |   (0) comments


We'll be on the scene at SAS Global Forum events in Washington, D.C., March 23 to March 25, glad to share what we learn with our community members.
Beth Schultz
7 Tips for Deploying Visualization

3|7|14   |   33:15   |   (0) comments


We chat with Analise Polsky, a data visualization thought leader on the SAS Best Practices team, about what you need to know before you deploy data visualization.
Michael Steinhart
Choosing a Big-Data Analytics Platform

2|19|14   |   31:53   |   (3) comments


The big-data analytics market can be a confusing place. Among the vendors vying for your dollars are traditional database management providers, Hadoop startup services, and IT giants. In this video, All Analytics editors Beth Schultz and Michael Steinhart sit down in a Google+ Hangout on Air with Doug Henschen, executive editor of InformationWeek. Henschen discusses use cases for big-data analytics, purchase considerations, and his recent roundup of the top 16 big-data analytics platforms.

Related posts:

— Michael Steinhart, Circle me on Google+ Follow me on TwitterVisit my LinkedIn pageFriend me on Facebook, Executive Editor, AllAnalytics.com

Michael Steinhart
Keeping a Close Eye on Shoppers

2|13|14   |   02:14   |   (16) comments


At the National Retail Federation BIG Show last month, All Analytics executive editor Michael Steinhart noted a host of solutions for tracking and analyzing customer activity in retail stores. From Bluetooth beacons to RFID tags to NFC connections to video analytics, retailers must find the right combination of tools to help optimize the shopper experience, streamline operations, and boost revenues.

Related posts:

— Michael Steinhart, Circle me on Google+ Follow me on TwitterVisit my LinkedIn pageFriend me on Facebook, Executive Editor, AllAnalytics.com

Michael Steinhart
Real-Time Demand Drives Forecasting

2|11|14   |   02:22   |   (1) comment


The days when historical shipment trends and gut feelings were enough to forecast retail demand accurately are long over. SAS chief industry consultant Charles Chase outlines the benefits of pulling real-time sales information from point-of-sale and product scanner systems, then flowing that data into dynamic forecasting tools from SAS.

— Michael Steinhart, Circle me on Google+ Follow me on TwitterVisit my LinkedIn pageFriend me on Facebook, Executive Editor, AllAnalytics.com

Videos
Intro to Visual Analytics

6|5|13   |   1:58   |   (0) comments


With today's advanced visual analytics tools, you can stream data into memory for real-time processing, provide users the ability to explore and manipulate the data, and bring your data to life for the business.
Videos
Visual Analytics, Instant Insight

5|16|13   |   2:06   |   (5) comments


Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
Videos
Big Data, Fast Infrastructure

2|14|12   |   3:35   |   (6) comments


No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Videos
Red Hot Analytics

1|10|12   |   3:51   |   (7) comments


Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
Videos
Hotelier Checks In With Analytics

12|14|11   |   06:55   |   (11) comments


InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.