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Top 10 List – The V's of Big Data

Recently I wrote about the "Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s", which summarizes some of the big issues associated with the deployment of big data projects. The use of the letter V may seem forced and contrived, but it is used primarily as a mnemonic device to label and recall these critical challenges, in much the same way the original 3 V's of Big Data have been invoked many times to identify the three defining characteristics of big data.

The main point of the V-based characterization is to highlight big data's most serious challenges: the capture, cleaning, curation, integration, storage, processing, indexing, search, sharing, transfer, mining, analysis, and visualization of large volumes of fast-moving highly complex data.  Many businesses have been excited by the opportunities presented by big data analytics (for marketing innovation, customer engagement, building the bottom line, and many more business goals) but the majority of them have fallen short in achieving meaningful ROI from it. In fact, it has been said that more than 55% of big data projects end in failure.  In some sense, the 3 V’s are to blame – the businesses see these enormous challenges and wonder if there is any way to overcome them.  The good news is that there are many big data solutions and success solutions to help out.  The Big Data Innovation Summit 2014 in Santa Clara provided many excellent examples from vendors and end-users.

Here is my Top 10 List of the V's of Big Data:

  1. Volume
  2. Variety
  3. Velocity
  4. Veracity
  5. Validity
  6. Value
  7. Variability
  8. Venue
  9. Vocabulary
  10. Vagueness

Read the full article to get more detail and explanation at: http://bit.ly/1hH6sB9

Follow Kirk Borne on Twitter at @KirkDBorne.

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Comment by Atif Farid Mohammad on April 21, 2014 at 11:27am

Wonderful...

Comment by Kirk Borne on April 18, 2014 at 11:18am

Good!  

My favorite ROI is "Return On Innovation"!

Comment by Merritte Stidston on April 18, 2014 at 11:11am

Kirk

Base on a question I received, I've modified the ROI descriptor. "ROI - (Data as an Asset) Effective management & analysis of the data enables better business decisions, thereby maximizing the return on investment (ROI) of your data processing system"

Comment by Kirk Borne on April 18, 2014 at 10:41am

got it! thank you.

Comment by Merritte Stidston on April 18, 2014 at 10:40am

Yes, sir

That was a few years ago and I've changed roles since.

Comment by Kirk Borne on April 18, 2014 at 10:39am

Great!  Thanks!  I presume that this is you here:  http://h10124.www1.hp.com/campaigns/enterprise/converged-cloud/us/e... 

Comment by Merritte Stidston on April 18, 2014 at 9:17am

Kirk,

Yes you may. Thak you for asking I am Director, Senior Enterprise Architect for Data & Analytics at McKesson.

Comment by Kirk Borne on April 18, 2014 at 9:09am

@Merritte, thanks for your contributions.  I like that "R" business perspective!  Can I borrow those R's for use in another blog article (with proper credit to you, of course)?

Comment by Merritte Stidston on April 18, 2014 at 9:03am

Kirk,

I enjoyed your comments. The V's are mainly from an infrastructure perspective of the world. I took a different view looking at it from a business perspective and created the R's.

Relevant - (Data Fit) Disambiguation of the incoming data with existing enterprise data. Define what is potentially relevant and useful to the business outcome.

Real-time - (Data on Time) Accelerating time-to-value from data creation to data usage

Realistic - (Data Outcomes) Data acquisition, analytics processing and appropriate data skill sets support the defined business use case

Reliable - (Data Quality) Data quality is critical to the reliability & efficacy of the result sets. Strong data quality measures correlate to good results

ROI - (Data as an Asset) Managed & analyze data to make the best business decisions to maximize return on investment (ROI)

All to often we discuss the V's with business leaders and fail to translate the technical to business imperatives and constraints that help them make critical business decisions. Just a different way to view/present the world of Big Data.

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