Metadata Lifecycle Management Considerations

An interesting diagrammatic look at the lifecycle of metadata.  Everything has a lifecycle, but the question is always: To what degree do we have to pay close attention to it?  The diagram is quite complex.  Its easy to say always, but attention always has a cost.  So is this really a portfolio of attention, and do we manage it that way?  More to follow as an exploration continues under the lifecycle tag.

On Data Flow

Agree with the points made here, the topic of lifecycle management is rarely considered seriously.  It also goes beyond lifecycle, to better understand the context of the data and decisions that are influenced by that data.

What Is Data Flow and Why Should You Care?  by Eric Kavanagh
 What goes around surely comes back around, which in the world of data is often called lifecycle management. To be blunt, very few organizations have ever formalized and implemented such a grandiose practice, but that's not a pejorative statement, for only until recently has the concept become seriously doable without great expense. ... " 

Managing the Analytical Lifecycle

More from the machine learning meetings at UC a few weeks ago.  SAS went over some interesting case studies I am now examining:   Managing the Analytical Life Cycle for Continuous Innovation
From Data to Decision,  The following describes an old problem with analytical models of any form.  On the right the diagram I use to talk this, a slightly different view than the SAS approach.

" ... The organization has nearly 120 analytical models in production to support marketing, pricing, operational risk, credit risk, fraud and finance functions. Analysts develop these models without formalized or standard processes across business units to store, deploy and manage the portfolio of models. Some models don’t have any documentation describing the model’s owner, business purpose, usage guidelines or other information necessary for managing the model or explaining it to regulators. Model results are provided to management with limited controls and requirements. Because different data sets and variables are used to create the models, results are inconsistent. There is little validation or back testing. Managers make decisions based on the model results they receive, and everyone hopes for the best. ... " 

Procter Product Lifecycle Management

From Consumer Goods Magazine:

P&G Optimizes Packaging and Artwork Initiatives
Procter & Gamble (P&G) extends the scope of its V6 PLM implementation to incorporate global packaging and artwork initiatives. This builds on the previously announced strategic selection of Dassault Systemes' (DS) solutions for an enterprise-wide PLM process. Together, P&G and Dassault are developing an integrated suite of products to help make the packaging process more efficient, improve speed to market, increase shelf impact and, ultimately, create a better experience for consumers. Streamlining these services is another example of how DS is supporting P&G's focus on "Simplify, Scale & Execute", which is one of the company's key growth strategies.

"As P&G continues to serve more consumers, in more parts of the world, more completely, it is essential we have the right tools in place to drive greater efficiency," says Michael Telljohann, PLM director, P&G. "To address these opportunities as they arise, it's imperative that mission critical business processes like artwork and packaging move from a series of best-in-class point solutions to enterprise-wide integrated solutions. Dassault Systèmes' suite of V6 PLM products will help P&G drive scale, improve R&D productivity and accelerate the delivery of new products to market." .... '