Daniel D. Gutierrez

You and the 10 Stages of Data Readiness for Analytics

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magneticnorth0
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Data Doctor
Re: Let's start at #6!
magneticnorth0   9/28/2016 4:17:16 AM
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@rbenci I think it may be the term, "analytics" that leads your clients to think this. Getting into analytics for them is jumping straight into the analysis. I do remember clients giving us a huge pile of data expecting it to be culled, though there was not much effort to consult us about the first five steps.

kq4ym
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Data Doctor
Re: Let's start at #6!
kq4ym   9/16/2016 7:51:38 AM
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Yes, as indicated the named stages all are very important for getting to the results. It's always tempting though to skip over one or more to get to something seemingly "more important" or at least easier to acheive.

Zimana
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Re: Let's start at #6!
Zimana   9/15/2016 7:08:39 AM
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Good point on the vale of process, and what it should mean. It's telling that a client's data visualization was managed by the IT group.

rbenci
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Let's start at #6!
rbenci   9/14/2016 1:13:38 PM
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Most of my clients want to jump right to Stage 6.  Perhaps they already bought a visualization tool, and had someone from IT "play with it" to create dashboards.  Then they wonder why they didn't get game-changing insights!  This is a great list to show that it takes a process --and QUALIFIED analysts/data scientists-- to achieve credible, reliable insights.  Bravo!

Zimana
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Re: 10 Stages
Zimana   9/9/2016 9:07:45 AM
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I agree Seth. I like this list too, particularly with governance positioned before scaling as mentioned in Stage 9.  Thnking about governance for analytics involves understanding how data is consumed within an organization and viewing how data is combined - identifying where PII is inadvertently used is a good example.

There's always a temptation in business to scale something up - increase production on a product, raise prices when demand is high. But analytics forces a reasoning on how resources are used.  This description of stages is a great demonstration of why that reasoning is crucial for long term success.

SethBreedlove
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Re: 10 Stages
SethBreedlove   9/8/2016 4:05:38 PM
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Thank you Jamescon.  I'll have to remember that.  "Good enough for government work."  

I looked up a quick history on that. "Originated in World War II. When something was "good enough for Government work" it meant it could pass the most rigorous of standards. Over the years it took on an ironic meaning that is now the primary sense, referring to poorly executed work."

Jamescon
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Editor
Re: 10 Stages
Jamescon   9/8/2016 3:07:22 PM
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@Seth. Your tale about just getting the project done in time brings to mind the old line that probably is still popular around construction sites. "Good enough for government work." The wall doesn't have to be perfectly plumb, the paint can be a bit streaky, the lawn a bit weedy. Good enough, let's get lunch!

SethBreedlove
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Re: 10 Stages
SethBreedlove   9/8/2016 1:39:50 PM
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I over heard someone on transit today saying all that matter was that the project was completed on time, not that it was good. 

I heard the same thing from a friend a few years back working on a project that everyone expected to fail.  

Often just getting it done and worrying if it works later is the definition of success.  

Daniel Gutierrez
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Re: 10 Stages
Daniel Gutierrez   9/8/2016 1:30:42 PM
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Absolutely, data injest is often the most Helter-Skelter stage in the organizations I consult with. The reason? The data sources often are the result of years of evolution, not always the most objective process in many enterprises. It is difficult to tell thought-leaders that their prized data assets aren't all they should/could be. This makes injest not the most straight forward process.

SethBreedlove
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Data Doctor
10 Stages
SethBreedlove   9/8/2016 12:45:53 PM
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These 10 stages could be applied to a variety of other projects as well.   I particullary like the ingest phase and think it is overlooked most of the time.  Often projects fail so thinking what needs to be done if it is a success probally doesn't enter into the minds of many.  If they succeed and get all this great data are they ready to process it? 

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