archive.Joe Lamantia.com

If you need such a deeper understanding of data science than Drew Conway’s popular venn diagram model, or Josh Wills’ tongue in cheek characterization, “Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” two relatively recent studies are worth reading.

‘Analyzing the Analyzers,’ an O’Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists — effectively personas, in a design sense — based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That’s not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science.

And the paper ‘Enterprise Data Analysis and Visualization: An Interview Study‘ by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there’s correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I’d suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work.

We’ve undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years — part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities — and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I’ll share and discuss in upcoming posts.

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My slides from Blogtalk 2008 are available online now: I went through a lot of ideas quickly, so this is a good way to follow along at your own pace…

FYI: This version of the deck includes presenters notes – I’ll upload a (larger!) view-only version once I’m back from holiday in lovely Eire.

Using the automotive industry and an analogous variety of software mega-packages with three-letter acronyms as examples, we’ve been discussing the death of the traditional enterprise for a few weeks. We’ve observed that enterprise efforts relying on massive top-down approaches become inefficient and wasteful, if not counter-productive. They also either fail to support the health of the individuals or groups involved – customers, users, sellers, employers – or in fact directly reduce the relative health of these parties. With Conway’s Law as a guide, we discovered that the structure or form of an organization influences or determines the nature and quality of the things the organization creates.
This all concerns the past: so now it’s time to look ahead, at the new enterprise. Of course, scrying the future inevitably relies on a mixture of hand waving, vague pronouncements, and the occasional “it’s not possible yet to do what this implies” to point the way forward. What’s often lacking is a present-tense example to serve as clear harbinger of the future to come. I came across an example today, drawn from the debate surrounding the proposition that the U.S. Army is close to a breaking point. In an episode of On Point titled Are US Forces Stretched Too Thin?, several panelists (names not available from the program website yet) made three telling points about the Army that show it as an organization in transition from the old model enterprise into a new form, albeit one whose outlines remain fuzzy. I’ll paraphrase these points:

To support this practice, company commanders created a forum for sharing innovations amongst themselves, called CO Team: CompanyCommand. The description reads, “CompanyCommand.com is company commanders-present, future, and past. We are in an ongoing professional conversation about leading soldiers and building combat-ready units. The conversation is taking place on front porches, around HMMWV hoods, in CPs, mess halls, and FOBs around the world. By engaging in this ongoing conversation centered around leading soldiers, we are becoming more effective leaders, and we are growing units that are more effective. Amazing things happen when committed leaders in a profession connect, share what they are learning, and spur each other on to become better and better.”
It’s the third point that gives us a clue about the nature of the new enterprise. CompanyCommand.com is an example of a ‘knowledge marketplace’ created and maintained by an informal network within an organization. Knowledge marketplaces are one of the components of what McKinsey calls The 21st Century Organization. Knowledge marketplaces allow knowledge buyers “to gain access to content that is more insightful and relevant, as well as easier to find and assimilate, than alternative sources are.”
McKinsey believes that these markets – as well as companion forms for exchanging valuable human assets called talent markets – require careful investment to begin functioning.
“…working markets need objects of value for trading, to say nothing of prices, exchange mechanisms, and competition among suppliers. In addition, standards, protocols, regulations, and market facilitators often help markets to work better. These conditions don’t exist naturally – a knowledge marketplace is an artificial, managed one – so companies must put them in place.”
On this, I disagree. CompanyCommand is an example of a proto-form knowledge marketplace that appears to be self-organized and regulated.
Moving on, another component of the new enterprise identifed by McKinsey is the formal network. A formal network “…enables people who share common interests to collaborate with relatively little ambiguity about decision-making authority – ambiguity that generates internal organizational complications and tension in matrixed structures.”
In McKinsey’s analysis, formal networks contrast with informal social networks in several ways. Formal networks require designated owners responsible for building common capabilities and determining investment levels, incentives for membership, defined boundaries or territories, established standards and protocols, and shared infrastructure or technology platforms.
My guess is that CompanyCommand again meets all these formal network criteria to a partial extent, which is why it is a good harbinger of the forms common to the new enterprise, and a sign of an organization in transition.
Can you think of other examples of new enterprise forms, or organizations in transition?
In the next post in this series, we’ll move on from the structure of the new enterprise to talk about the new enterprise experience, trying to track a number of trends to understand their implications for the user experience of the new enterprise environment.

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OCLC has caught the socially constructed metadata fever. A release on the OCLC site titled “User-contributed content pilot” discusses a pilot program to allow Open WorldCat users to add publicly visible metadata, in the form of reviews and descriptive details, to existing records.
This looks the latest step in the wave of exploration of methods and models for putting socially constructed metadata into practice that’s playing out in public. (Is this necessarily done in public? I’m curious to hear thoughts on how this might be done with closed or cloaked communities, like IBM’s intranet).
Broadly, it looks like a wide variety of entities are following the standard new product or service development cycle with regards to socially constructed metadata. A simplified version of this cycle is:
1.Conceptualization, technology development
2.Product development
3.Introduction to market
4.Market Acceptance and growth
5.Ongoing Market as conventional product
A quick review of known social bookmarking / tagging ventures distributed over a number of organizations supports the idea that each experiment is at one of these stages.
Some visualizations of development and prototype cycles are available here, and here.
Where’s it headed? I think we’ll see at least forms forms or applications of socially constructed metadata stabilize and become publicly recognized and accepted in the near future, with more on the way that will surprise everyone. Those four are:
1. Fee for services models, paying for access to premium quality pools of collectively managed information under professional (paid) editorial custody. OCLC could adopt this model.
2. Non-commercial community driven pools of social knowledge. This might be delicio.us.
3. Deployment as an enabler or attribute of other product / service models. Flickr is an example of this perhaps.
4. Publicly free but commercialized information mining operations, deriving salable value from formalizing the semantic relationships between people, groups, and information objects. TagCloud.com might fall into this group, or maybe Cloudalicious.
5. Something very innovative I will wish I’d thought of when it’s released.
Excerpts from the OCLC release:
“As of October 9, 2005, Open WorldCat users are able to add their own content to authoritative WorldCat information about library-held titles. Available under the Details and Reviews tabs, this functionality permits those who have located library items through Open WorldCat to return to the interface and add evaluative content.”
“User-contributed content will help extend the OCLC cataloging cooperative to include non-cataloging library professionals and – more importantly – patrons. Their shared participation in WorldCat content creation and management could foster a larger sense of library-centered community and generate more interest in library resources.”

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