Crowdsourcing vs. Analytics/Big Data #data-science #analytics


Thomas Blumer
 

Hello,
 
I had an interesting discussion with a colleague of mine regarding the difference between crowdsourcing and Analytics/Big Data. I am interested to hear what your take is on this:
 
Situation: A company hosts hundreds of  ERP/CRM systems for its customers. If the company wants to learn from the aggregated data, for example to find correlations across certain geographies or industrial vertical, would such an activity fall into crowdsourcing or analytics/Big Data?
 
My take was that crowdsourcing actively involves the crowd, for example by asking for inputs for specific questions or problems, or by letting the crowd solve a problem. On the other hand, there is (normally) only one party involved when it comes to analytics/Big Data, even if this party is leverage data across many customers and data sources.
 
Please let me know how you distinct the two.
 
Thank you and I look forward to hearing from you.
 
Thomas


Douglas Weidner
 

Dear Thomas,

 

Folks in your customer Crowd may have a serious analytic bent, and be able to tell you how to apply algorithmic, evidence-based methods to your big (aggregated) data, but that’s probably not the best route or likely outcome.

You’ll probably need quite accomplished analytical folks on your team ‘one party involved’ that can analyze the information which resides in your big data collection.

 

True, ‘crowds’ can solve problems, if you tap into a huge crowd of hobbyists that dabble in a particular field of interest. There are some good case studies for that outcome.

More likely, the diversity of the crowd can provide insights (diverse opinions) your marketing experts may yet be unaware of.

        

DOUGLAS WEIDNER  |  Chairman, Chief CKM Instructor

Home of KM Transformation Solution

O: 703-757-1395  

KM INSTITUTE – 2014 EVENT SCHEDULE

                

 

 

 

From: sikmleaders@... [mailto:sikmleaders@...]
Sent: Monday, May 19, 2014 12:13 PM
To: sikmleaders@...
Subject: [sikmleaders] Crowdsourcing vs. Analytics/Big Data

 

 

Hello,

 

I had an interesting discussion with a colleague of mine regarding the difference between crowdsourcing and Analytics/Big Data. I am interested to hear what your take is on this:

 

Situation: A company hosts hundreds of  ERP/CRM systems for its customers. If the company wants to learn from the aggregated data, for example to find correlations across certain geographies or industrial vertical, would such an activity fall into crowdsourcing or analytics/Big Data?

 

My take was that crowdsourcing actively involves the crowd, for example by asking for inputs for specific questions or problems, or by letting the crowd solve a problem.

On the other hand, there is (normally) only one party involved when it comes to analytics/Big Data, even if this party is leverage data across many customers and data sources.

 

Please let me know how you distinct the two.

 

Thank you and I look forward to hearing from you.

 

Thomas


tman9999@...
 

The example you cited above sounds to me like an example of data analytics, which can involve looking across the data sets that result from the activity of various and disparate customers or others. Crowdsourcing on the other hand involves tapping into the so-called wisdom of the crowd. Here you are actively soliciting input from a widely dispersed group of individuals by posing a question or situation in such a way that they feel inclined to respond. Their collective response will likely warrant further analysis, although probably not data analytics per se. The resulting synthesys or summary of their input becomes the basis for a crowd sourced solution or answer.


Stan Garfield
 

Thomas, I agree with your view.  Here is the definition I have used: Crowdsourcing: the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers




Karla Phlypo
 

Hi Tom,
I don't disagree with you it can also be used to collect so-called collective wisdom but the actual original purpose was to leverage said crowds to perform tasks, provide input to for marketing purposes for cash and prizes.  The word has broad usage.  Chaordix did exactly that and perhaps I did express that clearly enough but they did have crowds of people that were individuals with a common perspective or experience that shared their knowledge and wisdom.  But that is only one form.  Thank you for the comment.  I hope this helps to clarify.


Karla Phlypo
 

Hi Thomas,
Very interesting question.  I feel that its too easy to lump everything into crowdsourcing.  I am trying to be a bit more pure in the definition.   You can have challenges,  which ask a crowd of unrelated folks to give there ideas, solutions to a problem. 

Then you have organizations that want to understand something about let say, their frequent flyer and they solicit them to participate in improving a service.  This is a forum that over the time of the project begins to develop a community. 

Crowdsourcing involves the recruitment or invitation of individuals that have interest in the subject to share what they know or have experienced.   For market research it is targeted.  For civic questions "like crowdsourcing a new bus stand design" it is open to all with the interest. 

To me Big data is not crowdsourcing, while it may be the means by which more is understood about the crowd that was used to participate, in one of the aforementioned functions.  To me it is data analysis of a crowd sourced data.  Not sure if that helps?

Karla


Karla Phlypo
 

Hi Stan,
I dont disagree it is a form of crowdsourcing (Chaordix uses this definition too).  As I stated above it is easy to lump everything under crowdsourcing like with KM.  I added open communities because the are organized and come together for longer periods of time. Whereas in crowdsourcing the interactions is generally short and most do not get to know one another over an extended period of time.  This was fun!  Thanks again for having me present.