In my discussions and readings, I’ve come across several ways that leaders try to improve organizational performance.  Let’s consider one approach that might be called “Just do it” and could be parodied as follows.  The Maximum Leader decides that twenty different metrics express the performance of an organization, and then mandates a 50% improvement in each metric.

Pretty simple.  What’s wrong with that approach?  There are several drawbacks.

The first problem is that people can’t work on too many things at once.  The same is true about organizations in the context of initiatives that involve multiple parts of the organization and so require coordination.  People and organizations both need to focus.

A second problem is that it may not be practical, or sometimes even theoretically possible, to achieve large performance increases across the board.  Each metric expresses a different operational aspect and may be subject to different, practical limits to achievable improvements.

Third, people and organizations need to be convinced and inspired to act.  Unless you can mandate prison time – or worse – on top of mandating all the improvements, people need reasons which are best articulated persuasively and linguistically, that is, as sentences.  (Mere words aren’t reasons and so aren’t best at persuading, although they can summarize and inspire, e.g., Onward and Upward!)

A conventional alternative to massive mandates is comparing oneself to other organizations in order to identify several important areas in which an organization is falling short (problem #1 – focus), other organizations achieving better results (problem #2 – practicality), and to express the problem and need succinctly in order to persuade others to get fully on board (problem #3 – articulation).

Benchmarking in theory can achieve all of these, since it starts with data that enable comparison with other organizations.  As we have written elsewhere, benchmarking has been held back because it’s applied often in cases where data availability is a problem, and because the lack of automation leads to high costs, uncertain outcomes, and the biases that are necessarily introduced by the manual methods that are employed.

Benchmarking engines are the way forward, especially when they lead to concise, specific insights, expressed well, on a large variety of dimensions that can be addressed departmentally, not just organization-wide.  These insights are a spur to action – the action of deciding whether something is a practically addressable issue that should be a near- or mid-term priority, and then doing something about it.  The issue could be either a problem or a cause for praise, and the actions can improve on the problems or lead to copying the practices that resulted in the good outcomes revealed by benchmarking.

Organizations need to focus on practically soluble issues that can be set up for action and can be articulated persuasively to people.

Raul Valdes-Perez

People who know me have been asking why I joined OnlyBoth – an early stage technology startup. That’s quickly followed by the question: what is OnlyBoth anyway? Fair questions.

First, the “what” question.  Founded by entrepreneurs Raul Valdes-Perez and Andre Lessa, OnlyBoth is the pioneer of artificial intelligence-based benchmarking software. Its fusion of proprietary artificial intelligence and natural language generation technology enables companies in a variety of industries to automatically discover critical business insights from data, and to communicate these in plain English.

Now the “why” question.  There are actually five reasons, ranging from the lofty to the practical.  Let me explain.

Reason One: Impact.  I see this as an opportunity to make a huge impact – on customers, industry, and society, as well as on OnlyBoth’s employees and partners.  The bigger the impact, the more energized I get.  We’re addressing a pervasive need – to know how someone or something is doing in comparison to others. Capitalizing on the combination of today’s Big Data and OnlyBoth’s software, uncovering comparative insights and triggering business improvements is cheaper, simpler and more convenient than ever. It automates a process, which has been, until now, largely based on expensive and scarce talent. And it has a potentially strong position in a large, attractive market – key prerequisites for the most successful products.  OnlyBoth’s unique technology was years in the making, so it won’t be quick or easy for anyone else to create an alternative that delivers better results. So I see OnlyBoth’s technology as disruptive – in a good way.

Reason Two: Startup.  The company entered the market this summer. I joined at the end of July, and prior to this, I was occasionally advising and helping the co-founders discover the market problem/opportunity. For me, joining a company when it first gets started is the ideal time to learn and grow together. I’m at my best building stuff from scratch into something of value.  I’ve helped to build new businesses, new product lines, new product categories, new customers and new skills for four previous startups, as well as three multinational corporations and Carnegie Mellon University. OnlyBoth provides an opportunity to work on all of these.  So the timing on this one was nearly perfect.

Reason Three: Values and Respect.  I respect a lot of entrepreneurs and business people, but I haven’t always been at ease with their values.  However, Raul, OnlyBoth’s CEO, is someone I believe I can work with comfortably for a long time.  That’s important because it can take years to build something new that makes a real impact. A company’s CEO, more than anyone else, shapes the culture, standards and identity of an organization. Raul is very keen on doing what’s right, whether it’s for customers, employees or other stakeholders. I’ve found Raul to be straightforward and open.  He has integrity, values loyalty, and has a strong customer-value orientation.  He stays focused, seeks input from others, gives people freedom to use their talents, and delivers on commitments.  I share those values; they give me confidence we can work effectively together.  And besides, experience matters. Raul has made this journey before.  IBM acquired his first software startup twelve years after it was founded.

Reason Four. Finances. As a result of IBM acquiring their previous business, the founders are in a position to finance and scale OnlyBoth by themselves. Although it’s an attractive opportunity for investment capital, and VCs are actively funding other projects in the artificial intelligence and natural language technology spaces, we can devote our energies to solving customer problems, creating value with our products, and building a solid business, rather than pitching it to potential investors. Even better, we’re not dealing with embryonic technology.  The research and development of OnlyBoth’s technology started back in the late 1990s with the support of a National Science Foundation grant, and it’s now ready for market.

Reason Five. Responsibilities.  I’m very excited about my new role as Chief Customer Officer.  I’m leading the development of our customer base and making sure our customers get the maximum value from those products and from our relationship. But in the early stages of a startup, flexibility is key, so my role could evolve and take on new dimensions. In just my first month, I’ve had insightful discussions with over 30 people from organizations in our target market.  I’m learning a lot about the customer problems, the options they have to solve them, their readiness for a new solution, and where our communications can be improved. The people I’ve met have been very generous and helpful, for which I’m extremely grateful.  It’s a great way to help build a solid foundation for business success.

That said, you’d think this would have been an easy decision for me.  But it wasn’t.  As you may know, I’d been itching to create another startup and I spent a lot of effort exploring technology solutions to a few business problems and evaluating a number of opportunities to commercialize technology originating from Carnegie Mellon University and the University of Pittsburgh.  I was actually getting very close to starting a tech business.

And then, to make my decision even more difficult, I was surprised and honored to be asked to teach at a prominent west coast university and to lead a tech company’s product organization.  I will continue teaching my strategic marketing and product management course at Carnegie Mellon, but I feel lucky that Raul asked me to join when he did.  Opportunities like this don’t come by often; I’m glad I didn’t miss it.

Jim Berardone

Automated Insights has been acquired by Vista Equity Partners in conjunction with Stats LLC, a sports data and analytics vendor also bought by Vista (see TechCrunch, Xconomy, ZDNet). There has been media speculation about the implications of automated-writing (aka natural language generation) technology, sparked by this acquisition and by ongoing deployment successes by Automated Insights and also Narrative Science out of Chicago.

A frequent flaw in such analyses consists of treating artificial-intelligence software with a double standard that is not also applied to people. For example, way back in 1998 I wrote a letter to The Scientist dismantling criticisms of the Arrowsmith program which discovered unnoticed connections between themes in the medical literature. Critics had said that “everyone has some skepticism that you can find something new from what’s already out there”. Gee, people do that all the time.

Human writers succeed less often by solely becoming skilled at writing than by also becoming knowledgeable in a subject or mastering an analytical skill, which then give them something to write about. This is true of novelists, scientists, journalists, historians, and the rest: First comes the content, then come the sentences. Under this lens, one can argue that Automated Insights and Narrative Science technology is not so much automated writing but rather recurring event summarization, i.e., one of many analytical skills.

Thus, I’ll predict that the emerging technology for writing will pair up (1) specific subject-matter or analytical expertise with (2) writing as the more easily-mastered skill. Technical progress on (1) will be the bottleneck.

Raul Valdes-Perez

Today we added insights on baseball teams to our baseball application. This also introduces a third distinct data model, in addition to the models underlying our colleges and players applications. A data model refers to a generic type of application, not merely to a different dataset.

Here, an entry is a team and season, e.g., the 2013 Boston Red Sox, who won the World Series that year. Here are several outputs for that team/year:

1. The 2013 Boston Red Sox had the 6th-most doubles (363) of the 2,745 teams.
   beat out by the 2008 Texas Rangers (376), the 1930 St. Louis Cardinals (373), the 1997 team (373), and the 2004 team (373), and 1 other.
2. The 2013 Boston Red Sox struck out the most (1,308) of the 114 teams who won the World Series.
   surpassed the 2004 team (1,189), the 2008 Philadelphia Phillies (1,117), the 2010 San Francisco Giants (1,099), and the 2012 San Francisco Giants (1,097), and others, ending with the 1887 Detroit Wolverines (258).
3. The 2013 Boston Red Sox were the only team who had players born in Aruba, Canada, Cuba, the Dominican Republic, Japan, Mexico, Puerto Rico, Saudi Arabia, USA, as well as Venezuela.

Before, entering a team/year would return a list of that team’s players during that season. To get that list, the user now needs to add the word “roster” to the query, e.g., “Red Sox 2013 roster”.

Adding this hasn’t been a simple change from the data-analytics viewpoint. Here’s why: Our colleges application does not have a time-element. There is no sense in which Harvard 2014 and Harvard 2004 need to be present. Any time-dependent aspects are expressed via the data attributes, e.g., the tuition increase over the last three years.

No so with baseball players. There, the entries are a player/team/year. A player can play for multiple teams during different seasons, and even within a single season, and it’s conventional and interesting to consider, say, Babe Ruth in 1929 as a distinct object of analysis.

Baseball teams represent a data model intermediate between the colleges model and the players model. It’s interesting to compare teams across seasons, e.g., the 2012 and 2013 Red Sox, but there is no interesting sense in which a team belongs to another entity.  Sure, teams belong to owners, but owners don’t have a large stable of teams, and there aren’t a thousand teams playing every year. If both the latter were true (but they aren’t), then team analytics would indeed resemble player analytics.

In summary, OnlyBoth has launched a new application that is interesting because (1) of what it has to say about historical baseball teams, and (2) it represents a third, distinct data model for OnlyBoth-style discovery and writing.

Raul Valdes-Perez

Finding interesting and timely insights about baseball players is now easier and faster than ever with Scan Baseball, the new OnlyBoth bookmarklet feature.

Imagine how hard it is for baseball commentators and writers to come up with new, interesting angles when they have to cover certain games, teams, or players.

And what about those baseball articles that mention several players and you can’t quite put your finger on what makes each player special or unique?

If that sounds familiar, don’t worry. We just launched a new feature that lets you research baseball players from just about anywhere on the web. To install it, visit this page and drag the “Scan Baseball” button to your bookmark bar on any browser.

Then, next time you browse the web and see a baseball player you want to discover something novel about, just highlight the player’s name, click the bookmarklet, and OnlyBoth will launch with insights for that player (assuming we can match the name).

The OnlyBoth bookmarklet automatically selects all text on a page by default. But if you just want to research a single player, highlight the name first, and OnlyBoth will take you straight to that player’s insights page.

Our easy-to-install and lightweight Scan Baseball button lets baseball researchers (i.e., media and commentators) quickly scan a web page for baseball players that OnlyBoth can provide new insights for that aren’t available elsewhere.

Whether you’re researching someone who played last year, or you’re intrigued by a former or famous baseball player from the 1800s, our new feature makes it easy for you to improve your baseball knowledge like you’ve never experienced before.

Onlyboth is about more than automated discovery and writing. It’s also about doing so in context, helping people just in time. Still all automated.

Andre Lessa

In mid-July we launched a baseball application, but only on batters. Users would enter the name of a hitter and year (plus team if the player was traded in mid-season) and get OnlyBoth insights back. The idea was to launch a separate service on pitchers later.

We’re happy to announce that it’s been possible to mix hitters and pitchers in the same application. Continue reading →

Welcome to the inaugural blog post for OnlyBoth, founded in March 2014. Let’s consider what is OnlyBoth’s founding idea. Continue reading →