A core tenant of “Competing on Analytics” is that in order to make correct decisions, you need data.  Data requires measurement. And, as I’ve come to learn, it requires determining which data, among all of the data that one could collect, is the most important data.

A core tenant of “18 Minutes” is that getting the right things done requires that you decide, among all of the possibilities, the relatively small number goals that you want to achieve over the coming year. These are the things that you absolutely want to get done, and get done right. Another core tenant is that you need to evaluate each of your activities against those goals.  It suggests, among other things, that you start your day by evaluating what is on your calendar, not in your email InBox. Almost immediately upon rising, ask yourself,

What am I scheduled to do today?

Is my schedule consistent with progress against my goals?

If not, what changes do I need to make?

As I write this, I recognize that the above statements may sound very basic to some of you. Some of you already live your lives based upon very specific goals and measurement.  I suspect, however, that many more only think you live your lives this way, and haven’t actually taken the time to determine your personal goals, separate from everyone else’s goals. Nor have you decided what specific data will tell you whether you are making the correct choices in achieving your goals. I recently made that embarrassing admission to myself. It was especially embarrassing for me, given that I love science and studied, among other things, mathematics in college.

I won’t bore you with my own specific goals for this year, but suffice it to say that by measuring my time against my priorities, I have determined how long it takes me to complete certain tasks. I’ve learned what work is profitable and what work is less profitable than I had previously thought. I’ve learned the direct impact of diet and exercise on weight and heart rate. And I’ve learned that if I schedule things as soon as I make a verbal commitment, that I almost always keep my commitments.

I first met Mark, when I hosted IDC’s StorageVision conference in San Jose in May 2000, a year in which we recruited Steve Luczo, then CEO, and now Chairman of Seagate, and Joe Tucci, then the newly appointed President and COO  of EMC.  Mark wasn’t the best dressed (that was Joe) or most poised (that was Steve), but he gave, by far, the best talk. The talk that was discussed three times this month wasn’t, however, his StorageVision speech. That honor goes to this talk: “It Always Takes Longer and Costs More.” On Page 15 of the presentation, Mark begins a discussion of the Sales Learn Curve (SLC), which is the sales equivalent of the Manufacturing Learning Curve (MLC). The SLC is critical, Mark argues, to knowing when to step on the gas with sales.

Almost every company I’ve talked to measures the cost of sales. But where organizations differ is in the costs that are included in the cost of sales. Mark includes:

• Marketing
• Product management
• Product marketing
• Product  support
• Sales engineering
• Sales

Some of these costs are more fixed than variable. Marketing, product management and product marketing don’t need to grow linearly with sales. Support, sales engineering, and sales costs are, however, more directly proportional to revenue.

Since more sales means that the fixed costs in the cost of sales calculation decline as a percent of sales, many startups are tempted to hit the gas early on deploying sales and sales engineering resources. But until they have gone through the iterative process of perfecting the sales process, this approach just burns cash.  Instead, he stresses the importance of  investing for learning in the early stages, making iterative improvements in the sales process. When the sales process is perfected, then, and only then, should a company put the “Pedal to the Metal,” making significant investments in sales and sales engineering.

I’m always a little shocked by companies that see unhappy customers and fail to take immediate action. My wife recently took her car into the local dealership for routine service.  Between a post-doctoral fellowship, an active private practice, and several non-profit board seats, she’s very busy. So she wanted to know how long the service would take. The answer was, “No more than an hour.” After a two and a half hour wait for her service to  be completed, she was obviously steaming. She complained to the service manager, who apologized and then sent her on her way.

When the post-service customer satisfaction survey call came, she gave candid answers. She was not satisfied. Less than 24 hours later, she received a call from the service manager apologizing profusely, telling her to call him directly, the next time she had her car serviced. He then offered her a complimentary car detailing service along with a polite recommendation that she wait until the winter season was done, in order to get the maximum benefit from the detailing. The coupon for the free detailing arrived the next day in the mail. My wife was somewhat calmed by the gesture, but still doesn’t mind repeating the story of the dealership’s poor service.

Now, imagine how different her reaction might have been, had the service manager, to whom she expressed her dissatisfaction before she left the dealership, had then offered her a discount, or a free detailing service. Her anger would probably have been assuaged immediately. What if it didn’t require the service manager’s involvement at all, but the clerk at the service desk had been empowered and taken the initiative to make things right? What if the dealership’s response had been as it was for me at a local restaurant, when the waitress offered my meal for free, because I had to wait too long to be served. Rather than a detractor, I became a promoter, for her willingness to proactively do the right thing.

I think data is important, and I like to know what corporate executives care about, so, now, every time I meet with a startup company, I ask the CEO what they measure. Typically on the finance side, I’ll get answers like revenue, cash flow, burn rate. On the sales side, they tell me they track new customers, number of deals, repeat sales or renewals, and average deal size.  And on the development side, they will track the total number of bugs, sometimes by criticality, and the bug retirement rate. These are all good things to measure, so, if you are measuring these things, good for you.  And if you’re not measuring them, time to break out a yardstick and some monitoring tools.

My very successful friend gave me one more number to track before all others. Given his track record, I paid attention. The most important number for him is the percentage of customers that say they would recommend the  solution to a colleague. He actually has someone call every single customer and ask only one question:

Would you recommend our solution to a colleague?

This is not the same question as “Would you be a press reference or allow us to do a case study on your installation?” That brings with it the usual baggage of legal and PR approval processes. No, for an early stage company, this question, “Would you recommend our solution to a colleague?” encapsulates the only truly important metric into it. It answers the question, “Am I building the right product?”

To help me with that, I’m re-reading “Competing on Analytics: The New Science of Winning,” written by Thomas Davenport and Jeanne Harris and published by Harvard Business School Press back in 2007. The first thing that became abundantly clear was that I didn’t have enough data on myself, my activities, and the results of those activities.  So, I decided to collect some. As a starting point, I decided to analyze my activity publishing content on Wikibon.

I posted my first article, “StorMagic Announces SvSAN and Offers Free Download,” on Wikibon on February 19, 2009. It’s the only article I published that year, and it was an experiment. It was also, admittedly, a little self serving, since I’m a non-investor director on the board of StorMagic. Upon analysis, the results of the posting were pretty good. It’s been viewed over 3000 times and received a community rating of 4 out of a possible 5. Perhaps it was ranked a little lower, because the article was a little self serving, though defensibly 100% accurate. Given the results, you’d think I might have published more, but I didn’t.

In 2010, I posted 18 articles on Wikibon, and I posted another 6 in 2011, despite an amazing amount of disruptions, which I won’t go into here. So over the almost three years, I’ve posted a total of 25 documents. The total views across all of my documents is almost 48,000, the average number of views is a respectable 1900+ and the average community rating is 4.8+, despite my lower starting point. I guess I’ve improved with age.

The documents were all relatively short (at an average of 525 words, a very quick read) and designed to be actionable. Personally, I think articles are best, when they spark a dialogue or provoke a comment, and I’m sorry to say that the average number of comments per post was just over .7 and more than half had no comments.  That’s something  that’s worth figuring out how to improve.

Reporting on minimums, maximums, totals, and central-tendency are interesting first steps. But they are just that: Reporting. The key now is to get to the next level, and evaluate the impact of article length, keywords, topics and themes on views.  If anyone can suggest an open-source text-analytics tool, I would be very grateful.

Over the next year, I have resolved to write more, measure more, and analyze more. Expect to see more articles published by me on Wikibon, because I like the team, and it’s an easy platform to use. I enjoy the exposure to end-users that Wikibon affords me, and I like the fact that when I publish content there, I know how much it’s being read. I also enjoy the opportunity to have an occasional conversation with an IT industry executive, with whom I have no current business relationship. I’m a curious and rather social guy, so it doesn’t have to all be about my business and potential business opportunities.

I also plan to learn more about the rapidly developing field of data analytics, currently promoted under the term “Big Data,” which is either a subset or super-set of analytics, depending on your point of view.  I’ve always enjoyed mathematics and analysis, but back when I was a math major (along with majors in Physics, Education, and Psychology), about the only opportunity for a B.S. graduate in Mathematics, outside of academia or education, was to become an actuary at an insurance company. Frankly, I didn’t want to spend my life figuring out morbidity rates. But the life of a data scientist, especially when that skill can be applied to making better products and creating more jobs, is significantly more interesting.

Finally, Amy O’Connor tells me that Nokia plans to re-invigorate a local Big Data user group that has been meeting at the Microsoft offices in Waltham. So you can expect to find me there. I’ll post details as soon as I get them. I hope to see you there.

Best wishes for a happy and healthy new year.

Videos are also becoming an important medium for companies to get the word out, to explain, and clarify. So as an example, after a 2-day planning meeting with one of my clients, StorMagic, where I serve as a member of the board, I asked my son to record five short videos of StorMagic’s CEO, Hans O’Sullivan, answering simple, direct questions. Each video is less than a minute long and answers one or two questions on topics such as the background of the management team, the strategic focus of the company, the impact of recent announcements, and the company’s relationship with one of its partners.

Videos seem to be all the rage.  I don’t know what will come after videos, but it seems to me that for the next few years, at least, video will be of strategic importance in getting the word out about your company.

Hope you enjoy these.

StorMagic’s CEO Discusses Multi-Site Installations of SvSAN for VMware

StorMagic CEO Discusses the StorMagic Team and Recent Growth

More videos regarding StorMagic can be found on YouTube by searching StorMagic. You can even learn how to install and manage an SvSAN just by watching a video.

One presentation that I found particularly valuable was by Michelle Borkin, who explained her interdisciplinary approach to data visualization.  She brought together professionals from astronomy, who were working on how to get better 3-dimensional  pictures of objects in outer space, with radiologists, who were trying to get better 3-dimensional pictures of organs in the human body.  The results were both beautiful and amazing.

It would be easy to say “Outer space is very different from the human body, so it has no relevance to what I’m doing,” but she took the opposite approach and asked, “What are they doing that is similar to what I’m trying to do, and what can I apply from what they have already learned?” With all of the discussion around data generated on the internet, I kept wondering, during her presentation, what data visualization techniques can be taken from astronomy and radiology and applied to understanding consumers and influence.

Rose has been a friend for many years, so, of course, I said, yes.  For those of you who don’t know her, Rose is an accomplished PR professional, who carries many titles, including:

• Chief Marketing Officer at Launchpad Europe
• Managing Director / Founder at Omarketing

It seems I’m not the only one who is a Rose fan.  I see on her bio that she is the 2005 winner of the Toigo Award for Best Storage PR Professional.  For those that are looking for PR support in Europe, take a look at Omarketing, and for those looking to expand a European channel, take a look at Launchpad Europe.

And under the  FTC’s Guides Concerning the Use of Endorsements and Testimonials in Advertising, which focus on “Testimonial Advertisements, Bloggers, and Celebrity Endorsements,” let me assure you that I am not paid by, nor do I receive commissions or referral fees from Launchpad Europe or Omarketing.

Oh, yes, the next BDEvent will be held at the Sheraton in Palo Alto, CA, on January 25 – 27, 2011.

Of course, since the source code represents the crown jewels of the OEM-Out, they will typically do whatever possible to limit the conditions under which the OEM-In gets access to the source code.  The OEM-In, on the other hand, wants the broadest possible range of triggers for access to the source code.  

Triggers that I have seen proposed by the  OEM-In include:

• Failure to support
• Failure to remain current
• Bankruptcy
• Acquisition by a competitor of the OEM-In
• Any change of control

The OEM-Out will look to limit these triggering events by adding time-period constraints and an Opportunity-to-Cure provision.  So, for example, the Failure-to-Support trigger may get limited by requiring the fault to be a “material” fault, and not some trivial event like the GUI had a bug that affects a small subset of the OEM-In’s customers.  And the OEM-Out may require that they be given 30, 60, or 90 days to cure the default.

The OEM-In can limit the impact of a default by the OEM-Out by keeping the OEM-Out’s software outside of the core of the OEM-In’s offering.  A bolt-on application is much less risky for the OEM-In than an element that is embedded in the core.

During discussions this past week on an OEM transaction, we asked the OEM-In if there was any real value in getting access to the source code.  Their very honest answer was, “No. Not if the access comes because your client is in default. How would we know how to support the software.”  Since I’ve been working a lot with Axxana, a developer of a very unique disaster recovery solution, lately, I got to thinking about the parallels between access to source code and the value of a disaster recovery plan.  Having worked on disaster recovery teams in two banks, I can say without hesitation that a disaster recovery plan is of no value, if the first time you use it is during a disaster.  Disaster recovery plans need to be tested.  You could say the same thing about access to source code.  If the first time the OEM-In gets access to source code is during a disaster, then the source code is of very limited value.

One way around this problem is to structure an OEM agreement in such a way as to give the OEM-In a limited license for access to the source code on day one.  This can be done without giving away the crown jewels by focusing the negotiations around limits on the use of the source code and limits on reverse engineering.  The OEM-In can begin learning what is involved in supporting the software. From a partnership perspective this also makes it more likely that the OEM-In will be willing to make the software component an element of the core product, and, over time, may want to take over some of the responsibility for support.

The key in any negotiation is to focus on what we are trying to accomplish, and then to test whether the language that we are proposing actually helps us attain that goal.

Although I make my living largely by helping companies establish strategic partnerships, including OEM agreements, I thought it would be helpful to list some reasons why potential partners would say “No” to an OEM agreement.

1. If it is strategic, I should develop it myself.
2. My OEM partner may OEM to my competitor.
3. My OEM partner may be acquired by my competitor.
4. OEM agreements limit my ability to differentiate from my competitors.
5. OEM components that get embedded in my solution make it harder for me to OEM my own solution.
6. OEM agreements make it harder to sell my company.
7. The loss of a single customer may affect the financial viability of the OEM.
8. If my OEM partner fails to adequately support their product, I get all the blame.
9. Unless I have access to the source code from the beginning, I will be unable to support my customer, if the OEM fails.

While an OEM agreement can make a tremendous positive difference for both parties, there are plenty of reasons to say “No” to an OEM agreement. But, as my co-founder, David Burmon, says, “Selling doesn’t start until your potential customer says ‘No’.”