How "Tech Mining" Can Enhance R&d Management

By Porter, Alan L. | Research-Technology Management, March/April 2007 | Go to article overview

How "Tech Mining" Can Enhance R&d Management


Porter, Alan L., Research-Technology Management


Reflect back briefly on what was considered good manufacturing management circa 1960 and what we expect today. Prior to the quality movement, manufacturing operations were based heavily on intuition. Experienced shop floor personnel knew their machines and generated a certain level of efficiency and quality-few suspected that anything better was possible. Then came the quality movement-collecting performance data along with statistical analyses enabled supervisors to discern much finer performance tolerances. And, lo and behold, production processes improved dramatically, leading to far fewer flaws in automobiles, integrated circuits or whatever. And that provided tremendous competitive advantage to the Japanese and others who implemented successful quality management (1).

Managers in most fields are exploiting data to enhance performance. Finance, logistics, marketing, and sales depend heavily upon empirically based knowledge. Even sports management is being made over. The headline in a Sporting News article reads: "Playing the numbers game: now more than ever, the sports world is looking to statistics for performance-enhancing insight, fueling the quest to devise perfect predictors of success" (2). The article discusses the expanding use of empirical indicators across all the major sports. This notion gained popular notoriety with Moneyball-the tale of how Billy Beane, as general manager of baseball's Oakland A's, gets more bang for his limited bucks by using a variety of telling player statistics (3). For instance, CERA-catcher's earned run average-incorporates a catcher's talent for handling pitchers together with other defensive skills. Data convey valuable intelligence.

How about R&D management? I assert that research managers still reach decisions based largely on intuition. For example, the cornerstone in allocating federal research funding by the National Science Foundation and the National Institutes of Health is peer review. When the government confronts difficult science and technology ("S&T") issues, it turns to the National Academies (Science, Engineering, and the Institute of Medicine) for eminent expertise. This use of expert opinion is certainly laudable, particularly when compared to the alternative of uninformed, arbitrary choices. But, the point of this article is that we can do much better by incorporating a richer base of empirical information into our R&D management processes. The data and the tools to analyze them are available now. It's time to augment expertise with empirical knowledge. In so doing, we should be able to enhance our effectiveness at least as much as has been proven possible in manufacturing and other arenas.

I see payoffs in R&D management from the introduction of carefully targeted mining of R&D information resources. These range over the spectrum of R&D and innovation processes. The researcher contemplating a new project can position this work against what others are doing to identify novel approaches, locate potential collaborators, and exploit the most promising opportunities. The portfolio manager can optimize resource allocation to target the timeliest opportunities and take full advantage of his or her assets. The intellectual property manager, new product developer, and other technology managers can similarly enhance their positioning and payoffs (4). Managers who comprehend the research landscape, backed up by explicit information, have a major competitive advantage over counterparts driving solely on intuition. Let's consider how exploiting R&D information resources can make a difference now.

Tech Mining Concepts

The analogy to production and sports management understates the challenge. Empirical knowledge in those domains derives mainly from numerical data. For S&T, the data are a mix of numerical and text. "Tech Mining" is my shorthand label for text mining of technical information resources (5). It depends upon an understanding of what matters in technological innovation processes, text mining tools to get at the pertinent intelligence, and effective delivery of findings to users. …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

How "Tech Mining" Can Enhance R&d Management
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Author Advanced search

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.