dataeconomy

Agreed. Video surveillance data provides different analytics resource than POS transactions

I'm not sure different data types is what I would measure by. It's more data usage and purpose

there are so many - more to come perhaps - vizualization, traditional BI, Excel, machine data systems

Agree, most, if not all, business data has value. That value changes over time and in relation to business mission, goals and operations

So you could postulate an analytics system that used (say) BOTH video and POS data for some reason. Driven by usage of the data and not specifically because those happen to be different data types

In my view, there cannot be one set of analytics that will cover analyzing all questions and all types of data - there will always be a wide set of potential solutions

Agree. Before you can get value, you need to first identify and define the business goal you are trying to achieve. Build your strategy around your business need, and then seek for the right analytics approach.

Gov't shutdown defunds all statistics branches, so possible no data is collected & creates a missing period

Defaulting on the debt (Oct 17-31) would be far worse and could easily send economy into recession

For starters, in our IT-centric economy, imagine all those open tickets leaving gov't people trying to work today helpless...

Gov't shutdown also creates business uncertainty. I'm old enough to recall certain Congress members telling us buz uncertainty is a terrible event

Australia had government shutdown once. In the end, the Queen fired everyone in Parliament

Which data are we talking about? Not all data is or should be treated the same.

and who owns the data and who is willing to share what is often considered proprietary data.

Prioritizing the "right" data has become a bigger challenge since storing data has become cheaper, we tend to keep everything

prob lots of data that won't be shared..but..there's a lot to be learned from data on social media about any company...a whole new world IMHO

cloud could help here but the issue will be privacy and data protection for the companies involved..b2b system might be API based to allow this seamlessly in the future

Definitely Cloud has been a big door opener, but organizations keep holding "vital" or valuable info on their own

When you mention vertical what's the context? Industry?

financial services is the lowest hanging fruit then ecommerce with recommendation engines then other verticals

How does one ensure the data is all valid and not manipulated (it's all digital)?

This in my opinion will be a new job that will be created more about compliance and API mgt but my concern is does this stunt innovation organically

agree that it needs a focus but with ownership and governance

Why would the solution be just one source is better than a shared validation method?

My belief is that would create additional layers rather than enabling existing entities to expand and evolve...

Why should the "Data Czar" be independent of the CIO?

Do we need a Data Czar role for quality? Quality need to permeate throughout the layers that touch data. Otherwise, there is little/ no context.

that would increase costs

If the organization can have czar, that's great. In many companies, we've seen a central group (e.g. center of excellence) agree on the specific roles and processes to ensure data quality, governance etc

CDO and CIO are both required to be at the table along with Legal, RIM, Business, Privacy, and Security..

care to share some examples/proof points of practical solutions that you've seen?

I think this is a good point Tim-- So how can organizations focus on the practical and add value - vs spinning the wheels?

I agree. Practical use cases include: customer analytics, operational analytics, fraud and compliance and EDW optimization

how do you see that impact data value?

meaning what cross over volume?

I don't think there is an absolute number. ESG talks about how "big data" is a LOT more data than you are accustomed to

So, for a small company that typically has about 100TB, "big data" might be 300TB

Whereas for a large corporation, "big data" might be in the PB range

On a theoretical note; just finished a good book on Big Data called "Big Data A Revolution", in which Big Data is described as the environment where the sample set for is "All".

Translation: big data applies to everyone

I don't think the hardware is relevant. It changes the economics of what is doable. But that's about it

IDC is defining it as "deployments where the data collected (not stored) is over 100 TBs". Data sets that are not very large but are experiencing rapid growth rates of 60% or more annually are also considered Big Data under their definition.

That IDC definition seems very weak. Lots of companies have >100TB but are not doing "big data"