HiPPOs and Veracity, Gartner, IBM, “V”s

by workforceplanner

Workforce Locator™ is a data source that’s more reliable than the highest paid person’s opinion…

I came across a well written piece from ImagingBiz dot com that cites a report by Andrew McAfee and Erik Brynjolfsson, authors of Race Against The Machine:

> The managerial challenges of using big data are greater than the technical challenges, the authors believe. One of the most critical is silencing the highest-paid people’s opinions, or HiPPOs. When data were expensive and hard to get, relying on the intuition of upper-level managers made sense, but times have changed.


Here’s a link to Academia dot edu with a reprint of that report McAfee and Brynjolfsson wrote for Harvard Business Review:

> Exploiting vast new flows of information can radically improve your company’s performance. But first you’ll have to change your decision-making culture.

About Gartner’s definition of Big Data:

> Gartner’s Big Data Definition Consists of Three Parts, Not to Be Confused with Three “V”s:


This NetworkWorld slideshow ranks IBM as the leader among “15 most powerful Big Data companies”


IBM added “veracity” – another “V” to Gartner’s, volume velocity and variety, and IBM does a great job promoting IBM’s leadership position with virtual events and animations such as this one:

> Cultivating Big Data Adoption in Banking


IMO this article from TheServerSide dot com is worth reading as the writer expresses his POV about the four ‘V’s:

> Handling the four ‘V’s of big data: volume, velocity, variety, and veracity

> Veracity is probably the toughest nut to crack. If you can’t trust the data itself, the source of the data, or the processes you are using to identify which data points are important, you have a veracity problem. One of the biggest problems with big data is the tendency for errors to snowball. User entry errors, redundancy and corruption all affect the value of data. Your consulting firm needs to help you clean your existing data and put processes in place to reduce the accumulation of dirty data going forward.


See also:

> Leon Guzenda, founder of Objectivity discusses the 5 V’s of Big Data

> Let’s start with Value: Yes, there is a lot of big data out there, e.g. the many types of logs (Splunk) from M2M systems, location data, photo/video data, etc. At Objectivity we believe that inside your data there are relationships, either explicitly or implicitly hidden within data. And in those relationships lies the true Value of your data. Examples include telephone call detail records (CDRs, from/to subscriber #), network logs (TCP/IP logs, source and destination IP addresses), and web logs (clickstream data). Extracting this set of columns data can build a very nice graph. The question then is how to utilize this information to get commercial value out of it. The point about value is that there are lots of people collecting and storing big data, but what’s the point if you don’t know or have a plan how to use it. What’s its commercial value? How do you manage it? How do you know what you’ve got and where it is? Do you keep it forever, or delete it, or something in-between?