Technology and Organizations

This is a continued response to my infrastructure audit: What activities does an innovation infrastructure for open innovation need to support? My prior short answer was that “innovation infrastructure must keep the project’s top goals… top of mind…” Here I provide the promised longer answer and point to both team and organizational activities. For the organizational activities, I focus on the new book, Robert’s Rules of Innovation.

Team Activities:

Gibson and Gibbs give us a summary of the innovation activities needed by innovation teams. They note:

The ability of teams to innovate depends on how well they generate, import, share, interpret, and apply technological and market knowledge, particularly of local markets, economies, and customers. That knowledge is a combination of information, experience, context, interpretation, and reflection (Davenport, De Long, and Beers, 1998). It must be openly shared across contexts through relationships and networks, and there must be confidence in the value of that knowledge for achieving the objectives of the collaboration (Kanter, 1988). Once these requirements have been met, innovation involves dissemination and application of the knowledge, including combining and integrating it to develop novel insights, solutions, processes, or products (Obstfeld, 2005).

Applying the above to an innovation infrastructure suggests that we need to apply systems savvy and weave together the technology, organizational practice, and human motivations. We need systems that allow for the wide part of the innovation funnel: a wide and diverse set of information ideas. At the same time, we need systems that then allow the innovation project to be effectively and efficiently managed. I suspect that a project dashboard (next post) that shows both current project status and has a variety of news and discussion streams is one way to both keep project goals in mind and support the wider ideation activities.

Organizational Activities:

Robert Brands (with Martin Kleinman) recently released Robert’s Rules of Innovation: A 10-Step Program for Corporate Survival. I’ve been following Robert (here and here) for a while. Now I have a one-stop opportunity for his insights. Speaking at the organization level (with specifics also related to teams), he notes the following as “the imperatives to deliver profitable growth through innovation”:

Robert’s Rules of Innovation helps you develop both energy and structure around organizational innovation activities. The presentation provides a broad set of examples: from how to create an innovation culture to a clear discussion of intellectual property issues. I was especially happy to see a discussion around the evidenced based management of innovation:

Observation, measurement, and tracking of NPD results are essential to optimal ROI. Create your baselines first, with initial observations and measurements. Then capture the time to each gate, the time spent inside each gate, and so on. (p. 36)

Building on the idea of a dashboard within our innovation infrastructure, we need ways to track our experiments. Brands asks, “Do you have a set of metrics to serve as an innovation dashboard and track your innovation activities?” (p. 43). These metrics may be general (applicable to all innovation efforts — participate in the Innovation Coach survey here), or they may be specific to the particular effort. We also have to be aware that how we expect an innovation to play out may not be what happens in the wild — thus our observations need to be openended.

Have you had success, or even surprise, in tracking an innovation? What did you do that put you in a position for effective tracking? Do you know of any summaries of surprising innovation outcomes? Comments and links appreciated below.

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I can’t say there was standing room only at Marissa Mayer’s (VP of Search Products & User Experience) PARC Forum talk “Innovation at Google: The Physics of Data” — because the fire code for the auditorium doesn’t allow standing. They did bring in extra chairs and let the rest watch via TV in the lobby.

So many reasons for the talk to be popular: Google, Innovation at Google, Big Data issues, Marissa’s presentation style (engaging, you feel like your getting a secret tour). I’d gone because of the big data and innovation focus and came away with a better understanding of their process, and how their management style is as empirical as their products.

She opened by explaining the title of the talk. Besides the fact that it sounds cool, Google is on a physics kick with products like: Google Wave, Google Fusion, and their focus on acceleration.

She quickly moved to how data helps them build better products. She gave a clear example in terms of the testing that went into their choice of the shade of blue used in their links (see mention on Gigaom). Another example was how Google Trends can help anyone know if a product is trending up or down to help them think about stocking. Below is a Trend search on Crocs.

The highlight for me was how a culture of data/evidence supports their management. I’d seen Marissa talk about this in a video (below, section starts at 16:28) as “Data is Apolitical.” Evidence-based management (here and here) at a company that builds the tools that let us all organize our evidence.

From the video:

I think that the internal politics inside of Google have remained minimal compared to other corporations of its size because we rely so much on the data and we do so much measurement that you don’t have to worry, will your idea get picked because you’re the favorite, or will someone else’s idea get picked because they’re the favorite or because they have a better relationship with the person who’s the decision maker. The decisions get made based on data, and that really frees people from a lot of those types of concerns.

How can we use big data to make better management decisions? In earlier posts (here and here) I’ve listed a few tools and asked similar questions. Marissa described the power of other Google Tools to help us “understand problems in new ways”:

• Trendalyzer -- animations of trends over time
• Fusion Tables -- largely for researchers -- mashups and embedding of datasets.
• Google Squared -- googling your Google search results (thus squared) or the square that results. Not perfect -- but it is editable (e.g., you can add your own columns).
• And of course their (and everyone else’s) move to manage the real-time web.

What problems can we understand in new ways? How can the exploding amount of data (for example from the US Government) help us manage better?

Some starters: Facilities planning, recruiting, flexible work based on better predictions of business cycles, data mining of employees’ social media content…

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I’ve been talking about systems savvy, but the rubber needs to hit the road. How do you measure it? To help people develop systems savvy you need to know how to assess their starting and progressing competency. If we can’t measure it, we can’t effectively develop training, hire people who have it, or track its organizational impacts.

cc: flickr.com/photos/ideonexus

Systems Savvy is the “ability to grasp the capabilities of a technology and how that technology might be meshed with organizational practice. People with systems savvy understand that technologies and practices are intertwined — and they know how to make adjustments to both the technology and the practice to effectively weave them together.”

Systems savvy is a bit more than systems thinking (For example, Senge’s The 5th Discipline). Systems thinking is ability to see the whole and thereby use the leverage of small changes to make improvements. Systems savvy includes understanding how to intertwine the technology, organizational, and people components for better performance — not just focusing on one small change, but rather more on overall design.

Complicated ideas. Complicated measurement. But we do have some foundations to build on. M. Frank has developed measures of capacity for engineering systems thinking. The topics measured include:

• Desire to work with systems and to ‘love’ working on the systems level
• Understanding the synergy of the system
• Understanding the system from multiple perspectives
• Not getting stuck on details
• Interdisciplinary knowledge
• Learn or analyze the customer’s or market’s needs
• Perform engineering & economic optimization

Other measurement for systems thinking focuses more on the dynamics. Sweeney & Sterman present simple problems such as graphing the contents of a bathtub over time given how much is flowing in and flowing out. This gets at whether people understand system concepts like feedback, delays, and stocks and flows.

Measures of intelligence also can provide background for measuring systems savvy. In a 2006 paper, Hedlund, Sternberg (an expert on measuring tricky things like tacit knowledge), and their colleagues describe their creation of measures of “practical intelligence.” They note that:”.. individuals who effectively solve practical problems are able to recognize that a problem exists, to define the problem clearly, to allocate appropriate resources to the problem, to formulate strategies for solving the problem, to monitor their solutions, and to evaluate the outcomes of those solutions. Furthermore, in order to understand the problem in the first place, individuals need to be able to filter relevant information from irrelevant information, relate new information to existing knowledge, and compile information into a meaningful picture. The effective use of these skills to solve practical, everyday problems can be viewed as an indicator of one’s practical intelligence. ”

Their measures of problem solving skills were based on the solutions provided to a variety of business scenarios (though they were designed to be answered without business background) and then the open-ended solutions were rated by business school alumni and current students on: (a) time requirements, (b) realism, (c) accuracy and sufficiency of information, (d) prerequisite knowledge or experience, and (e) types of skills/abilities addressed.

Measuring systems savvy would include a similar set of steps (following Sternberg et al.): Approach organizational leaders with the request to identify people with clear systems savvy (using the definition given above). Ask the identified “savvy” people to describe a situation that required them to use systems savvy. Have them describe what they did and why it involved systems savvy. Have them describe what a novice or person without system savvy might have done instead.

This first portion provides the basic scenarios and some better and worse responses to the scenario. The next step is to have other experts help you create additional possible responses to the situation. The experts are asked to create responses that indicate high and low levels of systems savvy. Each of the responses is then weighted (again using experts) to create the score for choosing the particular response. In the case of systems savvy, we will need to be sure that the responses include the possibility of only focusing on technical or organizational solutions (lower scores), as well as responses that intertwine technical and organizational possibilities in sophisticated ways (higher scores). The scenarios themselves should focus on initial analysis tasks (how to get a clear picture of the organizational and technical context), problem solutions, and evaluation of results.

The validity of the measurement tool is initially tested by approaching still more experts — and now also novices — and having them select (via multiple choice) responses to the scenarios. The results should find the identified savvy experts scoring significantly higher than the novices. If so, your measurement tool is ready for the open road.

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Google Squared is a new Google search feature.  The question for me is how we can use our own “proactive integration” to make this an even more powerful knowledge management tool?  By proactive integration I mean the personal combination of information or knowledge into a new idea or understanding.  When we read a variety of reports and come to our own conclusion about overall trends, for example, that is proactive integration.

Google Blogoscoped provides a nice review, but the basics:

• Your search is presented in spreadsheet form: Items matching the search are presented in the first column, some other columns may automatically appear (I did a search of “Management Journals” — image, description, ISSN, telephone, publisher were automatically added)
• You can add different columns (e.g., I added rank and peer reviewed)
• You can delete results that don’t fit and then ask for more instances to be added
• Each column provides a link to its source, and the confidence around its value

Google Squared gives us the opportunity to either blindly collect a spreadsheet of information, or to carefully craft searches that help us create new insights.

I haven’t had a chance to play with Wolfram Alpha.  How do you think it compares in terms of pushing for our own deep and active thinking?

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Celeste LeCompte references some deeper looks in her article Can Videoconferencing Save the Earth? In Short: No.  Her’s is a more global (truly) approach than some of the more internal organization experimental approaches I’ve discussed before, but this provides a nice reminder: evidence based management should be based on all the evidence, not just that from our own firm.

Video conferencing can provide great overall value, especially if it is saved and used as part of an organization’s knowledge management approach.  However, doing it because it’s “green” may be a good, but not significant justification.

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