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I presented at the ISPIM Conference in 2020 (ISPIM: International Society of Professional Innovation Management) on how co-production can bridge the gap between academic research and industry use-cases creating shared, collective intelligence. Co-production in this context is people with different skills and areas of expertise working on a common goal. By sitting at the intersection of these divergent perspectives, co-product marries aspects of each viewpoint, bringing ideas born in research to life in industry.

Innovation relies on new perspective. We’ve found there are two ways to get that: collaboration and data. Either one can free our attention from the daily productivity push and spur an innovation.

Cleaning data is often the primary job of the data scientist, and not necessarily the one they signed up for.  While getting the "words" right with computers is important, it is not fraught with the nuance that getting words right with humans is. Across disciplines the same word can have entirely different meanings and this miscommunication is only one aspect of team building that can go awry.

Innovation requires collaboration, but due to the current state of our filter bubbles, collaboration is stuck in a rut. Data science utilizing knowledge graphs and team and portfolio optimization software can help us climb out. It can increase the scale, the intentionality, and the nuance of how we collaborate. With the right data and algorithms, we can use software to optimize our teams and facilitate innovation.

I recently moved from Boston to Oklahoma City. My wife got offered a tenure-track position at the University of Oklahoma, which was too good an opportunity for her career for us to pass up. Prior to the move, I had done a lot of traveling in the US, but almost exclusively on the coasts, so I didn't know what living in the southern Midwest would bring, and I was a bit trepidatious. It has turned out to be a fantastic move. There is a thriving high-tech startup culture here. I've been able to hire some great talent out of the University, and we're now planning to build up a big Exaptive home office here. Even more important, I was delighted to find a state that was extremely focused on fostering creativity and innovation. In fact, the World Creativity Forum is being hosted here this week, and I was asked to give a talk about innovation. As I thought about what I wanted to say, I found myself thinking about . . . cowboys.

Bloor Group CEO Eric Kavanagh chatted with David King, CEO and founder of Exaptive recently. Their discussion looked at the ways in which service-oriented architecture (SOA) has and has not fulfilled it's promise, especially as it applies to working with data. Take a listen or read the transcript. 

The Einstellung effect is a psychological phenomenon that changes the way we all come to solutions and impedes innovation.

How do you pick who works together, who reports to whom, and who exchanges information with whom? Usually it gets done within a department, within a project team, or based on some other common ground. It turns out we should be focusing on our differences a bit more.

Human Resources... HR... Human Capital Management, no matter what you call it, people are the beating heart of any organization. Taking a data driven approach to mapping the skill-sets of the people in your organization not only allows Leadership to optimize team formation, but provides an opportunity for hidden talents in your people to emerge.

You can find all sorts of “how to” treatises on assembling a team built for innovation, and they all include some subset of the following concepts - bring in experts, make sure they are diverse, have wide-ranging skillsets and have them be people who aren’t afraid to buck the system or go against the flow. Then don’t distract them with mundane tasks and give them enough time and money to actually accomplish their innovation mandate.

Unlocking team creativity during a pandemic.

We’re all familiar with the way knowledge is siloed by organizational structures and the momentum of daily work. Cross-pollination is key to innovation, but it goes against the current. So it’s hard, and the serendipity of chance conversations at the water cooler or a conference happy-hour often determines if it happens.

Even if an organization intentionally shares across domains, knowledge gets trapped behind disciplinary barriers. A surgeon doesn’t speak the same scientific language as a chemist. But thoughtful sharing between them led to a major innovation in medical science.

Most innovation management software (a.k.a. innovation software) is focused on generating and curating ideas. Ideas are explicit. They’ve been articulated, inside your head at the very least. Implicit knowledge, the context of an idea, is where many big opportunities reside.

Innovation relies on new perspective. We’ve found there are two ways to get that: collaboration and data. Either one can free our attention from the daily productivity push and spur an innovation.

It’s hard to create effective interdisciplinary teams or facilitate useful interdisciplinary exchanges. Research grants are increasingly calling for interdisciplinary collaboration as a requirement for funding. Interdisciplinary endeavors are popping up in academic settings and research foundations. Interdisciplinarity is a necessity for solving complex problems. But executing interdisciplinary collaboration is easier said than done.

For 20 years, I have worked with researchers in academic settings to help them design, develop and obtain resources to support the research they do. Over those 20 years, I have cultivated relationships and developed partnerships to the benefit of these researchers, sometimes brokering relationships and other times developing partnerships on behalf of others. I am not advancing my own research or research interests. Instead, I am developing my own understanding of what others do with their research and communicating that understanding to other audiences. Knowledge transfer of this sort is somewhat common in academia, and in other sectors such as industry or non-profit organizations, this form of communication might also be referred to as marketing, storytelling, and/or knowledge mobilization.

Some problems are complicated. Finding a solution requires expertise and analysis, but the solution exists. Sky scrapers are complicated. Some problems are complex or even wicked. These problems have no solution. They’re too big, too slippery, too thorny.

The goal: investigate huge amounts of research data in new ways. The pool for teams: neuroscientists, data scientists, and software developers. The result: answering questions we didn’t even know we had.

Ever work in a place where you looked up to everyone there because they are experts at whatever they do and can pretty much solve any problem that arises? That’s what working at Exaptive is like. It’s a mix of expertise across various computer/data fields that works very well. Ever felt like you didn’t belong in a place like this?

Within a group, a team, a network, or organization that relies on members being connected to one another, connections can be based on a number of factors but almost always rely on the availability, awareness, and mobility of knowledge or information essential to the group. How does information move within a group or across groups? We are interested in identifying catalyzing actions that occur in group interactions to facilitate the ease of information and knowledge exchange and the establishment of new connections of members in the group. Research suggests that ideas have value to the extent that they can be shared with a new or different audience (Burt, 2004). This research also suggests that individuals who can establish new connections within a group bring competitive advantage to the development of new ideas within that group. In our experience, the purposeful translation of ideas to new audiences reduces serendipitous connections and takes advantage of certain individuals’ natural tendencies to broker these connections.

In the early 1990s, Burger King began an ad campaign that had a massive impact on consumers, especially in the United States. Anytime I hear the words, “Your way, right away,” I immediately hear the jingle that went along with it. The promise that you could have anything you wished -- and have it immediately -- was adopted in many sectors and pushed as good customer service, whether you’re in Human Resources and your customer is an employee, or you’re in Client Support and your customer is the client.

At some point in your life, you’ve found yourself describing a project you’ve worked on to a friend. They interject, “I’ve done something similar to this before,” and go on to describe a field or skill you didn’t know they were familiar with. You’ve just uncovered some dark assets about your friend: a set of skills or knowledge that were only discovered due to an accidental trigger.

This can be problematic when it comes to group projects, whether you’re working with an existing team or you’re putting one together. The people and tools available to you are limited to those you are aware of or those cataloged in scattered directories and lists across the internet. There are far more dark assets than known assets.

In Stanley Kubrick’s famous film based on Arthur C. Clark’s book, 2001: A Space Odyssey, a mysterious black monolith appears on Earth millions of years before modern humans. It’s the classic “black box.” We don’t know who made it, what’s in it, or how it works, but it’s miraculous and powerful and somehow results in jumpstarting the entire evolution of humankind.

I am constantly amazed by the energy and momentum around data science. Only a few years ago, I would be met with a blank stare when I told someone I planned on going to grad school for machine learning. Today, there is no need for my “it's like computer science, linear algebra, and statistics had a combined love child” analogy as most people instantly respond with “Oh, like AI!”

A collaborative blog series about collaborative research: a data scientist and a cognitive psychologist combine perspectives.

Dr. Alicia Knoedler: For the past 18 years, I have sought opportunities and means to advocate for researchers working to develop and accelerate their research programs. I had the very fortunate opportunity to meet Dave King in 2014 when he relocated his company to Oklahoma City.

Have you ever read the book Travels with Charley by John Steinbeck? The first part of the book is John Steinbeck talking about his lifelong affliction with wanderlust. I spent a few years living out of a VW camper van, indulging my own wanderlust affliction, so the book quickly claimed a special place in my heart. Steinbeck is such a skilled writer, and he describes the feeling -

When the United Nations released a report earlier this year that a catastrophic two-degree Celsius (3.6-degree Fahrenheit) rise in global average temperatures is expected to occur in the next decade, there was a media firestorm about the dire predictions. You know who wasn’t surprised? Climate scientists. (Read about the difference a half-a-degree can make.)

James Verdier: Hi, I'm James Verdier and welcome to the American Institute of Biological Sciences’ BioScience Talks which is a forum for integrating the life sciences. On the second Wednesday of each month we discuss the latest bioscience publications. And as a reminder if you'd like to read more point your browser to academic.oup.com/bioscience.

If your job is to get your company, team, or community to innovate, you know how organizational forces can make it hard to even try something new. Visualizing the resources available is an effective first step in overcoming some of those organizational forces. Simply being able to see, and show, what you have allows you to make a compelling case for marshaling resources and even spark some initial interactions in that direction.

Everything is derivative. Take advantage of that. “New” ideas are the next step in an extensive network of existing people and ideas. If we can get the data and reconstruct the network, we can analyze it and understand where branches of a network have the potential for innovation.

“Good ideas are getting harder to find,” Exaptive CEO Dave King quotes a recent paper by MIT and Stanford researchers. He points to the skyrocketing number of researchers employed in the U.S. and contrasts it with the inverse slope on a chart monitoring efficiency of researchers along the same timeline. “Those growing number of researchers are failing to produce value that outpaces what we’re spending to innovate.”

Since Exaptive launched in 2011, we’ve worked with many researchers, particularly in medicine and the natural sciences. PubMed®, a medical journal database, pops up repeatedly as a key tool for these researchers to develop hypotheses. It’s a tool built in a search-and-find paradigm with which we’re all familiar. Execute a keyword search. Get a list of results. Visualization can make search - and, therefore, research - much more meaningful.

Faster is different. It sounds strange at first because we expect faster to be better. We expect faster to be more. If we can analyze data faster, we can analyze more data. If we can network faster, we can network with more people. Faster is more, which is better, but more is different.

The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place under a common schema so it can be analyzed.

Have you ever met a homesteader who owns a mansion? Me either. My neighbor, Bill (80), is a homesteader who tries to be as self sufficient as possible. From what I can see, it’s an immensely rewarding and humble existence. Life-satisfaction oozes out of his every pore and, eventually, even enduring the hardships must have become rewarding to him.

So many fantastic quotes are attributed to Albert Einstein. If you hear our CEO Dave King speak, he may bring up his favorite: “Combinatory play seems to be the essential feature in productive thought.” To have an aha moment, we have to play with a challenge from a variety of perspectives. We have to build collaborative teams to tackle complex problems.

One thing we love doing at Exaptive – aside from creating tools that facilitate innovation – is hiring intelligent, creative, and compassionate people to fill our ranks. Frank Evans is one of our data scientists. He was invited to present at the TEDxOU event on January 26, 2018.

A crucial aspect that sets a data application apart from an ordinary visualization is interactivity. In an application, visualizations can interact with each other. For example, clicking on a point in a scatterplot may send corresponding data to a table. In an application, visualizations are also enhanced with simple filtering tools, e.g. selections in a list can update results shown a heat map. 

We recently had to prototype a data application over a supercomputer tuned for graph analysis. We built a proof-of-concept leveraging multiple APIs, Cray’s Urika-GX and Graph Engine (CGE), and a handful of programming languages in less than a week.

The ability to reuse and repurpose - exaptation - is often a catalyst for exciting breakthroughs. The Astronomical Medicine Project (yes, astronomical medicine) was founded on the realization that space phenomena could be visualized using MRI software, like highly irregular brains. The first private space plane, designed by Burt Ratan, reenters the atmosphere using wings inspired by a badminton birdie. Anecdotes like this abound in many fields, and the principle applies to working with data and creating data applications, as much as it does any innovation. 

Parallel coordinates is one way to visually compare many variables at once and to see the correlations between them. Each variable is given a vertical axis, and the axes are placed parallel to each other. A line representing a particular sample is drawn between the axes, indicating how the sample compares across the variables.

Often times, when we're looking at a mass of data, we're trying to get a sense for relationships within that data. Who is the leader in this social group? What is a common thread between different groups of people? Such relationships can be represented hundreds of ways graphically, but few are as powerful as the classic network diagram.

Nexflix has collected an impressive amount of data on Hollywood entertainment, made possible by tracking the viewing habits of its more-than 90 million members. In 2013, Netflix took an educated guess based on that data to stream its own original series, risking its reputation and finances in the process. When people were subscribing to Netflix to watch a trove of television series and movies created by well-established networks and studios, why create original content? Now, few would question the move.

Late last year I turned the venerable age of 40, and graying and balding jokes aside, I've spent a good bit of time reflecting on the accelerating pace of change in technology. It's not just that things are getting faster, better, cheaper. It's that whole new capabilities are now possible that we could only dream about even a few decades ago. Mail is electronic. A TV and a computer are basically the same thing. And you can talk to your phone.

In recent years, machine learning as a service has come of age, with robust capabilities from Amazon, Google, Microsoft and others now available through REST APIs for a fraction of the cost of deploying or developing your own capabilities. One of the better known – if not easy to separate hype from reality – is IBM Watson. While Watson gained fame as the Jeopardy!-winning supercomputer, IBM now uses the brand for a wide variety of machine learning capabilities from speech-to-text and conversational bots to text mining algorithms for understanding the concepts, references and tone of text-based content.

In this post, we'll cover how to integrate one such IBM service – Natural Language Understanding – and rapidly prototype an application that you can try on your own content. It includes how to get started with IBM's hosted service Bluemix and the Python code to connect to the REST API. I've also included a working data application that you can run with your own text.

There are data visualizations. There are web applications. If they had a baby, you'd get a data application.

Data applications are a big part of where our data-driven world is headed. They're how data science gets operationalized. They are how end-users - whether they're subject matter experts, business decision makers, or consumers - interact with data, big and small. We all use a data application when we book a flight, for instance. 

Bayesian inference is a statistical method used to update a prior belief based on new evidence, an extremely useful technique with innumerable applications. Uncertainty about probabilities that are hard to quantify is one of the challenges of Bayesian inference, but there is a solution that is exciting for its cross-disciplinary origins and the elegant chain of ideas of which it is composed.

Can we predict what the next hub of tech entrepreneurship will be? Could we pinpoint where the next real estate boom will be and invest there? Thanks to advances in machine learning and easier access to public data through Open Data initiatives, we can now explore these types of questions.

Continuing with our recent theme of abstraction in data applications, Dave King gave a talk last month explaining his design principles for "Making Code Sing: Finding the Right Abstractions." Nailing the best abstractions is a quintessential software challenge. We strive for generality, flexibility, and reuse, but we are often forced to compromise in order to get the details right for one particular use case. We end up with projects that we know have amazing potential for use in other applications but are too hardcoded to make repurposing easy. It’s frustrating to see the possibilities locked away, just out of reach.