Enterprise Software Sales and Procurement Need a Makeover

Time to Tie Payment to the True Measure of ROI:  User Adoption

Today, I believe that the only way for IT organizations to truly measure their effectiveness is by analyzing the usage patterns, satisfaction and productivity of their customers: end-users.  Period.  Any IT organization that doesn’t use this kind of measurement won’t be successful over the next 10 years.  As David Sacks, the founder/CEO of Yammer, has said: “Voluntary adoption is the new ROI.”

One of the things that bugs me the most about the software industry is the sub-optimal behavior that exists between vendors and buyers.   Software buyers and vendors are engaged in a dysfunctional dance that wastes money and stifles innovation – on both sides. This dysfunction is driven by outdated software business models, and further complicated by non-technical sales and procurement people whose identity is tied to controlling the buying process instead of optimizing value created for end users.

Simply put, times have changed dramatically; enterprise software sales and procurement haven’t.  Here are the new realities, as I see them.  (Most of these realities also apply to how software is sold to and purchased by federal and state governments.)

12 Realities That Vendors and Buyers Can Ignore – But at Their Peril

1.       The pressure of the “consumerization of IT” is game-changing. This pressure is coming down on enterprise IT organizations, although most still have their heads in the sand.  This pressure is going to get acute over the next few years as the gap widens between what is available to the average consumer on the Internet and what an employee’s IT ecosystem provides at work. This gap will reveal just how wasteful and disconnected most IT organizations are from end-users’ real needs. 

2.      Buyers purchase a ton of software that they don’t ever use, much less realize value from.  The enterprise software emperor has no clothes. 

Buyers need to step away from the standard perpetual license agreement.  Just put it down... and pick up a subscription/term agreement: it will be okay, really. Businesses haven’t imploded because they don’t use perpetual agreements.  Short-term, subscription-based agreements are working great for all those Google Enterprise and Salesforce.com customers who have month-to-month agreements.  Subscription/term agreements help ensure that buyers don’t waste bazillions of dollars buying software they don’t use. 

Now, the finance dudes will come in with their spreadsheets and models that show how you can finance perpetual agreements at a lower cost of capital.  Don’t believe them.  What these models don’t take into account is one important fact: that if you pay a vendor a bunch of money for a product that is supposed to do something and the vendor isn’t on the hook financially for this, then the vendor probably won’t do it.  There is little incentive for the vendor to ensure that the software is deployed, adopted and improved over time. The vendor (particularly if it’s one that’s oriented toward short-term goals) will likely take the money and run. Not necessarily because they are “bad people” but because the business (like everyone else) is under pressure to deliver more, faster, better. 

3.      Vendors sell a ton of software that is never deployed (see#2). And many of them don’t care.  As their businesses have matured, many of these vendors have sacrificed their souls to short-term thinking and financials. They are no longer driven by missions to deliver value or great experiences for end-users.  By comparison, the consumer Internet companies that have moved into the enterprise software market have used alternative models and behaviors and begun to disrupt the ecosystem.  Those customers that have embraced new usage-oriented models have benefited significantly. Those customers that haven’t are just wasting money and causing their end-users to continue to suffer with outdated and expensive technology that preserves the short term job security of narrow minded IT staff members.  

4.      Software vendor sales people and customers’ procurement staffs are disconnected from how software is used and developed in the enterprise.  Generally, neither procurement folks nor salespeople understand the technology or how it’s used. They are managed to objectives that have nothing to do with successful deployment, much less adoption of the software or technology.  Procurement people generally care about discounts and sales people care about commissions.  It’s time to get these folks out of the way or give them incentives that align with effective adoption and value created for end-users.  One of the powerful benefits of SaaS is that end users can buy their own capabilities and just expense the cost.  This is how many customers start with Salesforce.com, and I’ve seen this same adoption pattern occuring in infrastructure at companies such as Cloudant. 

5.      Software that sucks.  There’s a disconnect between the amount of money that big companies spend on software and the value they get from it. Many big companies only resolve this disconnect over long intervals, after tons of money has been wasted on useless technology projects that aren’t aligned with users’ requirements.  Moving toward shorter-term subscription models helps reinforce the need for software companies to create value within reasonable periods of time.  In other works, deliver software that doesn’t suck.

Part of the reason corporate IT projects take so long is that businesses don’t push their vendors to deploy quickly or drive adoption. Here’s an example, from the Front Office/Customer Relationship Management sector of the software industry.  Siebel Systems launched its system in the early to mid-1990s using the traditional third-party installed and heavily configured model. (I suspect that the rationale was: “It worked for ERP, so let’s do the front office the same way.”)  Unfortunately for Siebel, things didn’t play out this way. As Salesforce.com launched, customers realized that they could get immediate adoption, usage and value by just signing up for the Salesforce.com service. These customers perhaps didn’t get all the customization that usually came with traditional enterprise software, but most of that customization was being sold to big companies by consultants who wanted to make money as part of the enterprise IT ecosystem.  A lot of smaller customers didn’t need the customization, and ending up paying for overhead that they didn’t need.

A general rule of thumb for IT organizations was that you had to spend an additional 2X-5X in services to get a third-party enterprise software application deployed and working.  This never made sense to me, but I participated in the dysfunction along with everyone else for many years, on both the buyer side and the sales side.  As this thinking became more broadly accepted, it became a self-fulfilling prophecy: vendors could make money customizing the solutions for customers (regardless of their actual need for the customization), so it was in their best interests to create software that required a bunch of consulting to get it working for customers. (Software that sucks, in the classic sense of “suck”: time, money, corporate IT resources.) 

With the evolution of SaaS, all vendors now face more accountability, like it or not. Salesforce.com knocked it out of the park as an independent business while Siebel sold out to Oracle and has bounced along the proverbial third-party software bottom, collecting maintenance on software that they sold 10 years ago to big companies who are not capable of switching to Salesforce.com.

6.      Traditional business models encourage vendors to extract as much money from their customers as quickly as possible – regardless of whether the software works or the customer actually needs the software. During the 1980’s and 1990’s, this “sell first, ask questions later” model became standard practice for technology companies, based on the success of proponents like Oracle.  But now, we’ve evolved.  Customers shouldn’t stand for it. There are better alternatives.  And vendors in just about every enterprise-software category should realize that it’s only a matter of time before someone comes along and provides better solutions that work for users quickly.  Let the hangover of enterprise software purchases begin. 

7.      The perpetual-license model creates perverse incentives for both buyers and sellers.  The subscription/term license model creates a much more rational incentive for the seller of technology to deliver both short-term value (through adoption) and long-term value (through improvement to the software) for customers’ end-users.  With a perpetual license model, the seller gets too much value up front, misaligning his interests with those of the buyer.

8.      Traditional “maintenance” is just as dysfunctional as the perpetual license that it stems from.  Fifteen percent (15%) maintenance is not enough money to innovate and improve a new system. Therefore, vendors’ business models put them in a position where they have to “upsell” their customers’ perpetual licenses for some additional usage or a new product. 

9.      Many customers should be happy to pay larger subscription fees over time in exchange for significant probability of greater success, user satisfaction and innovation.  They just don’t realize this, because business owners, end-users and engineers aren’t involved in procurement processes.  This perpetuates a lack of accountability for vendors and feelings of helplessness among users and consumers of these software systems. 

10.   Multi-tenant Web services present a compelling alternative. The broad availability of commercial multi-tenant hosted web services (epitomized by Amazon Web Services and GoogleApps) is creating a widening gap.  On one side of the gap, there are buyers and sellers of software who are merely perpetuating outmoded models for consuming and selling software.  On the other side of the gap, are software buyers that demand that their vendors deliver value through reliability and innovation every single day – and have the means to measure this. 

11.    FUD continues to rule – for now.  Many of the procurement and sales establishment are using the FUD (Fear Uncertainty and Doubt) arguments to slow the adoption of new software-as-a-service models.  I can understand why: the new business models including SaaS challenge their very existence.  However, as a result,  their customers are saddled with a  sub-optimal state of productivity for their IT systems and infrastructure.  This is not sustainable as IT organizations are under dramatic pressure to reduce costs significantly.

12.   IT organizations that embrace new software models are more productive and efficient.  They can focus more on high-leverage skills like networking and integration – and worry less about lower-value activities such as racking and stacking servers or building and releasing software. These benefits have been documented among the likes of Google Apps enterprise customers (Genentech for example) as well as large companies that have embraced Amazon Web Services (Netflix for example).  

I believe that all software contracts should tie payments to end-user adoption.  Monthly software subscription deals can be used to accomplish this relatively quickly: if users adopt. you pay; if they don’t, you don’t pay.  

For software industry old-timers this is heresy.  But it’s time to leave this one in the rear-view mirror – or eventually suffer the consequences.  The packaged third-party software industry is due for a reckoning - it's time for vendors to modernize their business models which depend on bilking customers for perpetual licenses and maintenance streams on software that is never used.  And customers should start buying software as a service and not overpaying for big perpetual licenses that they may or may not ever use.  

Spend More Time on Your Mission - The Money Will Follow

Time – and how you use it – is the most important consideration for a Founder.  This is one of the most important lessons I’ve learned in my 20 years as an early employee, founder, and CEO of start-ups.

When you’re starting a new company from scratch, time is your most precious resource because it’s so scarce.  Initially all you have is yourself and one or two partners who are preferably (at least in my case) a lot smarter than you.

If you believe, as I do, that your success is determined by how you spend your time, then start by spending it on your mission and the experience of your customers – not on money.  Randy Komisar at Kleiner Perkins likes to talk about “mission-driven” founders and start-ups. I like that phrase a lot.  And I believe that you need to embrace the concept from Day One – by being mission-driven in how you allocate your time.

Spend your time talking to customers, recruits, and partners; not with consultants or financiers – it’s almost a complete waste of your time. 

As money for early-stage companies increasingly becomes a commodity, entrepreneurs’ success will be determined more by their ability to create a great business – and less by their networks and credibility with a small number of professional early-stage investors (who control the purse strings granted to them by a small number of relatively disconnected Limited Partners). 

I’m not saying that early-stage/venture investors don’t add value. Specific people absolutely do add value and are important to the success of many companies. However, early-stage/venture investors shouldn’t be the primary focus of your time – and the good ones don’t want you to focus on them anyway. They want to help you build a great business – to put the priority of your time ahead of their own.  The ability of a venture investor to prioritize the time of the entrepreneur ahead of their own time is a primary test of a great investor.

The best venture investor partners are those who embrace modesty as a primary quality: in other words, they exist to help make the company (and by definition the founders) successful.  In interviews, Peter Barris of New Enterprise Associates has specifically and frequently cited modesty as a primary cultural dynamic at NEA. I've experienced this first hand working with many partners at NEA – especially Harry Weller and Tom Grossi –and I believe it is a key component of NEA's ability to scale successfully. John Lilly at Greylock is another great example of someone who demonstrates this type of modesty – putting the entrepreneur first.

Often, the problem for Founders and venture partners is conflicting objectives relative to their time.  

As a company Founder, your goal is to get the best possible return on the time you spend achieving the mission of your company. For many venture investors, the goal is to meet with as many prospects as possible because their limited partners are essentially paying them to talk to people and gather information that the partners can use to make optimal investment decisions. This behavior is particularly true of younger, less-experienced early-stage investors, who have lots of time to spend.  Some young venture investors may meet with you even when they have no money to invest. Talk about a waste of time – it happens more than anyone would ever admit.  

You may ask:  “But don’t I get value from every meeting with investors?  Even if the meeting doesn’t result in an investment, won’t I get useful free advice?”  

This is a serious dysfunction, especially for first-time Founders. Sure, some of these folks can provide valuable advice, but to be mission-driven, it's much more important to focus on your customers, the people working for you and your business.   

The nature of the venture capital business is that it’s populated by a lot of people with relatively large egos. In my experience, such people are prone to radically over-estimate the value of the time they “invest” in your business. They do get a lot of data from a broad variety of sources – they get paid to meet with lots of companies ;) So if you're looking for a lot of data – great, go out and ask them a ton of questions. But be specific about what you are trying to get out of the interaction instead of just “catching up.” 

The best investors will want to get to decisions quickly and not waste time – yours or theirs.  They recognize that their success as investors will depend on how wisely you spend your time to build a great business in a short period of time.  In my experience, the best investors almost always start their side of the meeting with "How can I help?" not "You should think about...." They arrive prepared, they listen, they help and they give you decisions very quickly.

So, given all the "red flags" I talked about above, how do you find the best prospective investors and avoid wasting your time?

Time-Saving Tips for Dealing with Investors

Before you meet with any investor, try to qualify the person and the firm.   Sometimes this is difficult because of lack of transparency in the venture business.

Fortunately, transparency is steadily improving. Be sure to read the Kauffman Report, which does a great job moving us toward transparency for early-stage investing.  Kudos to the Kauffman team. 

It's somewhat ironic that so many venture investors  (who profess so much faith in capitalism and free markets) have worked so incredibly hard to limit transparency, both at the micro and macro level.   

• Who are the actual top venture firms by return?  
• Who are the partners who have created value for common shareholders vs. those that jump at the chance to dilute common shareholders at every opportunity?

This kind of information has traditionally been hard to find, even by doing primary research within entrepreneurship inner circles.

Here are some other to-do's:

Look up the investors on The Funded.  Get every source of information you possibly can about not only the firm – and the culture of the firm – but also the PERSON who you are taking money from. Relentlessly pursue personal and professional references. 

Before you agree to meet, ask them the following questions:

• How much of your existing fund(s) is still available to invest?
• When are you planning to raise another fund and what is the target size?
• How many investments have you (firm and partner) made in the past month, quarter, year?

Also, ask yourself:  “Who do I trust that has had experiences with the firm/partner?” Try to have candid conversations with those people.  True character in early-stage companies is measured by what people do during the worst of times and the best of times. This is where you see their true values reflected in their actions – you want to know that they will do when it looks most grim or when there is a ton of money on the table. Those who will support the mission of the company in either of those cases are the people you want to work with.  Empirical evidence is always telling.  

If you do meet with investors and they say anything other than “absolutely yes – we want to do this deal ASAP – here is a term sheet or I will have a term sheet to you within X days,” you should interpret their response as essentially a “NO.” 

Great companies are built by people (including investors) who are fiercely mission-driven.  If external financing is required (which is far less often than most entrepreneurs realize) – make sure that your investors believe that great companies are defined by their ability to create value for ALL shareholders through the achievement of the companies' missions – not a quick flip to pump up the value of a particular venture fund.  This is a long-term view that is all too rare among venture capitalists, but a key attribute of the most consistently successful early-stage investors. 

Transparency: It’s About Time

Fortunately, there is a massive culture change brewing in early-stage investing (thanks again to the Kauffman team and many others). The potential of democratized early-stage investing is becoming obvious: combine AngelList with the potential impact of the Jobs Act.  Entrepreneurs are realizing that it’s their time that’s precious (to themselves and their future investors), NOT the time of the investors.  This has always been true among the best companies and the best people at the high end of the start-up market, but it’s now coming downstream.  Hallelujah.

So, spend your time pursuing your mission, developing your idea and creating your technology.  Spend it with potential customers, turning them into real paying satisfied customers.  If you do this well enough, and are smart, disciplined and mission-driven, there will always be capital available for your company.

Finally, if you’re wasting time thinking “But I have a pit in my stomach because I can’t pay my mortgage,” stop. This is how it feels to take risks. It’s painful but it WILL make you stronger as a person (insert Nietzsche cliche). It will make you stronger as a role model for potential employees and customers, who will respect your commitment and sacrifice in the interest of achieving your mission.   And it will make you more attractive to the right kinds of investors, if and when you need them.

Enterprise Software CEOs Need a Dose of “Undercover Boss”

Connecting with the Disrupted Customer

What if enterprise software company CEOs were asked to do what other CEOs are doing on the TV show "Undercover Boss": to do the heavy lifting that their employees are doing every day? (It always makes me smile when I imagine Larry Ellison trying to write SQL.)

I think we'd find that there are very few software CEOs who appreciate what’s involved in developing or deploying their technology into the hands of customers. (Jim Goodnight at SAS may be a notable exception).

Most software CEOs can probably do what the average enterprise software salespeople do:  sit in meetings, make phone calls, and invent new PowerPoint charts.  But how many could actually put their hands on a keyboard and write code? Or even just configure their company’s products in order to create value for their end-users?  Although watching people – even great engineers – write code and run build-scripts isn’t quite the same as watching some executive load a garbage truck with dirty diapers during primetime.  

Obviously, I am having some fun here: programming is probably not required for running a multi-million-dollar or billion-dollar software company. But I'd go as far as to say that it's highly desirable for the CEO of a software company to understand the engineering of her/his company's technology.  Furthermore, knowing your customers (mostly engineers) and their needs is absolutely a requirement.  And, in general, it’s often incredible to me how disconnected many technology company executives are from what their products actually do for their customers’ end-users.

With the rapid maturation of the enterprise software industry over the past 10 to 15 years, the “nobody ever got fired for buying IBM” excuse is going away.  When buying enterprise software, customers today have many options and often radically reduced switching costs. They also have the ongoing pressure to improve efficiency of both capital and operating costs.

Meanwhile, corporate end-users are asking themselves and their managements: “Why can’t the systems that I use at work be as easy to use, simple and productive as the systems I use at home?”

Good question.  If your company has decided to use Google Enterprise Apps, you have an advantage. The capabilities you have from Google will improve at the pace that technology moves on the consumer Internet – not at the pace that your internal IT organization moves.  Many users who are frustrated with their captive IT organizations just pull out their AMEX cards and pay for cloud-based services that do the job without involving requiring a call to anyone in IT.  This is how Salesforce.com got its start and how systems such as Expensify today are replacing traditional enterprise expense management tools.  (Expensify has the best tag line in the world, in my opinion: “Expense reports that don’t suck!”)

If you work for a traditional enterprise software company, the consumerization of IT should be a huge wake-up call. If you are the CEO of one of those companies, I'd suggest embracing a model other than "milk my customers for maintenance revenue."

Even if you don’t expect a software company CEO to write code on national TV, it’s a good idea for her/him to get together with end-users who are actually using the software (or, more worrisome, not using it).  Then, he/she will have direct information on how good (or how bad) the software actually is and can make the decisions necessary to fix it quickly if necessary.  Start-up CEOs have the advantage of being closer to their customers, for both constructive feedback and complaints. A little start-up thinking could go a long way for most large enterprise software companies.

But I’d still like to to see the CEO of a large enterprise software company write some code ;)

Many Small Stock Grants Over Time Should Be the Rule in Start-Ups

An Alternate Approach

In my last post, I wrote about how important it is for founders to take a proactive and decisive approach to crafting their ownership models – to the extent of being a control freak about it.  Getting the correct ownership model in place up front is one of the more important decisions you will make as a founder, and it will guide you through key hiring decisions.  

And it doesn't matter how many or how few employees are involved* – it's still important. Do you grant stock up front to new employees? Or do you delay the grants (either through vesting or follow-on grants) over some period of time?  There are costs and benefits to various approaches, and many different mechanisms for executing any given approach.  Having a great start-up lawyer is key: the best folks I've ever worked with are Mitch Zuklie at Orrick and Marc Dupre at Gunderson.  

"Front-loading" stock options – granting large options up front to key employees – has been a popular approach in the past for many start-ups. But I've come to believe that a better approach is to give smaller stock grants up front (with relatively standard vesting of four years total with one-year cliff, then monthly for the last three years); followed by many small grants over time, at least annually but preferably every six months or so.  

When you grant options in this way, you essentially create a "ladder" of stock vesting that helps ensure that the rate of vesting for any given highly valued employee is going up consistently during that employee's first five to ten years at your company. 

Of course, you can always make exceptions, giving them a large up-front grant. However, I believe that such grants should be the significant exception rather than the rule.  

Here's my rationale.

How It Works and Why

With a laddered approach, you're focusing on the RATE of vesting, not the size of the initial grants, to increase the likelihood of retaining superstars over time.  Key employees who you want to reward and retain for the long haul enjoy an ever-increasing RATE of vesting: the number of options that vest each month or quarter after they pass their cliffs.  This also has the benefit of minimizing the impact of bad hires, who – assuming you are doing your job – will leave the organization before they vest too much and/or will not receive follow-on grants.  

The laddered approach has a powerful psychological impact on the employees. They're getting regular feedback on their performance. They FEEL appreciated on a regular basis and their efforts are recognized by the company through ownership, the most valuable form of compensation in mission-driven startups.  The sacrifices they are making are often “unnatural acts” of saying no to family, friends and personal comfort in the interest of doing “whatever it takes” to make a company successful.  Being recognized with more ownership over time, in my experience, means more to the average start-up employee than most investors can ever appreciate.

Another benefit of this approach is that it can protect you from a common pitfall: inadvertently selecting people who are great negotiators but perhaps not as good at actually doing the work and earning the stock.  For example, sales-oriented people tend to view the negotiation as an end in itself,  while engineers tend to view the work as what determines value and earns rewards.  By appealing to mission-driven people who know that there is more ownership available to those who perform, you'll attract people who are confident and willing to prove themselves and for whom the mission of the company is enough to get them on-board initially.  

Some Downsides

This approach does have some downsides. It can require a lot of administrative support and careful expectation-management so that employees don't expect both frequent grants AND large grants regardless of company and individual performance.  

Another potential downside is that the strike price of options or the purchase price of restricted stock increases over time.  So, the weighted average price for the individual is higher – sometimes dramatically higher during the first five years of a new company’s life cycle.  This can be especially hard if you are allowing your employees to exercise their stock early.  However, I suspect that most companies would be more than willing to compensate for this by granting a larger quantity of stock for the ultra-high performers.  It’s a net-positive-value decision to give top performers a disproportionate amount of stock.  The basis for their retention compensation is that they create more value than other people. Therefore, locking them in with more stock is equivalent to fundamentally increasing the value of the stock.

Ultimately, I believe that this approach actually costs the company less in the end. 

I believe that the practice of large initial stock grants is nothing more than an artifact – and one that doesn’t optimize growth for the company or the investors.   Let’s bury this practice once and for all and use it only as an exception not a rule.

* Except of course for the extreme case where no one gets any ownership other than the founder. If you are looking for examples of those, there are plenty - for example, Kenan Systems and Ab Initio in Boston or Trilogy in Austin.

Beware the "Accidental" Ownership Model

As a Founder, You are the Master of Your Domain

Over the past 20 years, I’ve participated in many start-ups, both bootstrapped and venture-backed.  During that time, I’ve seen many approaches to start-up employee ownership across a broad spectrum of philosophies – from radically low/no employee ownership (beyond the founder/founders) to ultra-high employee ownership (where many or all employees are treated as owners).  At Infinity, we referred to this broad form of ownership as "Citizen Ownership."

I’ve also experienced the best and worst of how ownership can change over time in early- stage companies: from too few people owning too much of a company, all the way to people who contributed little or nothing to the company's success owning a large stake.  In between, of course, is a broad spectrum of ownership, which is where most companies end up.  

I believe that it's very important for founders to (1) consciously and proactively decide up front their aspirational ownership model and (2) plan how to achieve that model using specific corporate and legal mechanisms to execute it.  Don't let your ownership model happen accidentally. This happens - and more often than you would think.

Part of your responsibility as a founder is to protect the interest of early-stage common shareholders from the nature of capitalistic individuals who are not capable of starting companies themselves and/or are just trying to make a $ and don't care about your mission.  If you as a founder aren't looking out for the interest of the common shareholders, it's possible that the people who put in the hard work, sacrifice and commitment to start the company from scratch may be pushed aside by investors or late-comers who desire to capitalize on others' hard work.  

Just to be clear: I’m not advocating that founders be greedy and not share ownership with employees (or investors) who come in at later stages when new skill sets are required.  Quite the opposite: I believe sharing ownership with those who come in later in your company's life-cycle is essential for most entrepreneurs to be successful - I've done this consistently in companies that I've helped start and it has often worked well.  It's critical to embrace the reality that few people are wired like Bill Gates or Steve Jobs and have the skills to lead a company from founding all the way through world domination. 

However, I am advocating that founders be VERY disciplined in ensuring that they protect their own interests and the interests of other early-stage employees, particularly engineers.  If you are successful in your start-up's mission, many people will come along who want to capitalize on your hard work. Sharing your ownership with the right people can create a lot more value as you grow.

So, what's the best ownership model?

Over the past 20 years, I’ve concluded that there is no single ownership model that works broadly across all different types of companies. Founders must match their companies’ ownership models to their management philosophies, as well as their business goals and their expectations for their companies.   

There are as many ways to successfully configure a cap table as there are entrepreneurs. But the end-game configuration that hurts the most is the one where you feel like you've given too much ownership to people who don't deserve or appreciate their ownership in an entity that you started from scratch and that you believed in when no one else did. 

Unfortunately, all too often I see first-time founders accept outmoded or antiquated ownership practices and beliefs that may or may not have worked in another situation/project/company: "In our companies, Founders get X%, Investors get Y%, post-founding employees get Z%."  Often these ownership practices are driven by investors' “models” for how they would like to run their funds: essentially as a series of relatively homogeneous ownership structures that minimize cost and complexity for the fund and help them reduce risk.

I’m not blaming the investors for trying to reduce cost, simplify their businesses and minimize risk.  However, I've observed that often, the most successful companies – those that venture investors strive to fund (call them “The Fundable” ;) ) –  have strong founders who are deliberate in terms of the ownership models that work for them and their new companies. 

The impending Facebook IPO is a strong reminder that founders can and should pursue ownership models that work for them and their companies, regardless of how different it might be from current venture capital ownership dogma.  No matter what investors say, they make all kinds of exceptions to most of their “rules,” all the time.  As a founder, you may or may not have the leverage required to trigger their exceptions. If potential investors say “We never do X,” what they usually mean is “You don’t have enough leverage to make us do X.”

One interesting example of ownership innovation is the “Founder Preferred” model, which has become accepted in Silicon Valley.  Unfortunately, this model has yet to become broadly used in smaller, more provincial markets such as Boston/Cambridge, Seattle and Austin.  It’s hard to believe that such basic practices have not yet been widely adopted, but I hope we will begin to see this happen. 

The recent improvement in the economy has reinvigorated founder confidence, and increasingly founders have the benefit of improved transparency from sources such as “The Funded” and publicly available templates that enable founders to compare deal structures and terms proposed by their potential investors to those of other founders. Let's hope that a successful Facebook IPO will help validate the benefits and integrity of some of these innovative mechanisms that benefit entrepreneurs and thus further encourage even more entrepreneurship/start-ups.  

In my next post, I'll talk about one very important facet of ownership when starting your company: how to optimally structure options grants for early employees.

Building an Analytics-Driven Culture

Turning Big Data and Big Analytics into Business Opportunity

If you haven’t read my friend Tom Davenport’s book Competing on Analytics (Harvard University Press, 2007), you should.  If you have read it, it’s a really good time to read it again. 

Why?  The Big Data revolution.  Or I should say, the Big Analytics revolution. (BTW, I think that "Big Analytics" isn't a great term either. But what the heck, let's just make it easy for the marketing folks to transition from Data to Analytics by using the same adjective ;) 

In his book, Tom talked about organizations that were using analytics – analyzing massive amounts of data – to gain a real competitive edge in their business performance.  Practitioners ranged from health care organizations and pharmaceutical companies to retailers (such as Best Buy) and the entertainment industry (Harrah’s Entertainment, whose CEO Gary Loveland, an MIT graduate, wrote the foreword to the book).  And we can’t forget the sports teams – not just the Oakland A’s, famously portrayed in Michael Lewis’ book and then the movie Moneyball, but our own Boston Red Sox and New England Patriots. Professional sports is being transformed radically by analytic tools, techniques and culture.  

Today, I think we’re on the edge of a secular shift in business: the ability of virtually any business – not just large and technically sophisticated businesses with big budgets – to get a competitive edge by using analytics every single day.

Big Analytics for the Rest of Us

Big Data is only part of the story. What matters more is what you do with Big Data: Big Analytics.

Big Data is a fact of life for almost every company today.  It’s not something you run out and buy.  You already have it, whether you want it or not – or whether you know it or not. It’s the large quantities of data that companies accumulate and save daily about their customers, their employees and their partners; plus the vast corpus of public data available to companies elsewhere on the Web. 

In years past, the constraints of traditional database technology – such as first-generation relational database architectures and the outdated business models of the companies who commercialized these systems – made it difficult and expensive for people and organizations to store and access such volumes of data for analysis. Today, the popular adoption of innovative technologies such as the Hadoop distributed data file system (HDFS)/MapReduce, as well as many other "built for purpose" database systems enable data to be aggregated and available for analysis cost efficiently with extreme performance.  (BTW, I believe that Hadoop/MR is way over-hyped right now: it's great and very useful, but only one piece of the Big Data/Big Analytics puzzle.)

Some of the innovative products/companies that I've had the privilege to be a part of include:

• Vertica* 
• VoltDB*
• Paradigm4*
• Cloudant*

There are MANY others – which is good database innovation mojo – especially compared to seven years ago when Mike Stonebraker and I started Vertica.  At that point, the standard response from people when I said I was working with Mike on a new database company was "Why does the world need another database engine?  Who could possibly compete with the likes of Microsoft, IBM and Oracle?"  But the reality was that Oracle and the other large RDBMS vendors had significantly stifled innovation in database systems for 20+ years.  

Jit Saxena and the team at Netezza deserve huge kudos for proving that, starting in the early 2000s, the time was right for innovation in large-scale commercial database system architectures. Companies were starved for database systems that were built for analytical purposes.  I'm not a fan of using proprietary hardware to solve database problems (amazing how quickly people forgot about the Britton Lee experiment with "database machines").  But putting the proprietary hardware debate aside, thanks to innovators like Mike Stonebraker, Dave Dewitt, Stan Zdonik, Mitch Cherniack, Sam Madden, Dan Abadi, Jit Saxena and many others, now we're well on our way to making up for lost time.

Some other database start-ups of note include:

• NuoDB, Jim Starkey's company
• Akiban Technologies
• ParElastic
• Hadapt,  Dan Abadi's company
• Basho, the makers of Riak
• 10Gen, the MongoDB company
• Cassandra, not really a company (yet, I think) but a viable key value store 

There are many new tools out there for managing Big Data, and new innovations are being delivered to the market every month, from big and small companies alike.  I've actually been impressed with the progress that Microsoft has made with SQL Server of late, mostly driven by Dave DeWitt, PhD, and his new MSFT Jim Gray Systems Lab at University of Wisconsin.  Most business folks don't realize that many of the technical principles behind systems at Teradata,  Greenplum, Netezza and others were based on innovations such as the Gamma parallel database system as well as the dozen+ systems that Mike Stonebraker and his vast network of database systems researchers have been churning out over the past 15+ years.  

The challenge now for most commercial IT and database professionals is the process of trying to match the right new tools with the appropriate workloads.  If, as Mike and his team say in their seminal paper "one size does not fit all for database systems," then one of the hardest next steps is figuring out which database system is right for which workload (a topic for another blog post).  This problem is exacerbated by the tendency to over-promote the potential applications for any one of these new systems, but hey, that's what marketing people get paid to do ;)

Until just recently, however, another key element that has been missing is the focus on how data is going to be used when people implement their Big Data systems.  Big Data is useless unless you architect your systems to support the questions that end users are going to ask. (Yet more fodder for another blog post.) 

For many decades, there was no open, scalable, affordable way to do Big Analytics. So the kind of capabilities that Tom Davenport talks about in Competing on Analytics were available only to companies with huge financial resources – either to pay companies like Teradata (which is where the Wal-Marts and eBays of the world ended up) or hire tons of Computer Science PhDs  and Stats professionals to build custom stuff at large scale (Google, Yahoo, etc.)   The analytics themselves were even further restricted within those companies, to professional analysts or senior executives who had staff to make the results of these analytics digestible and available to them. These capabilities were kind of a shadow of the Executive Information Systems trend in the 1980s.

Today this is changing.  Established companies and start-ups are creating technologies that “democratize” Big Analytics, making large-scale analysis affordable for even medium-sized businesses and usable by average people (instead of just business analysts or professional statisticians). A great example is Google Analytics. Ten-plus years ago, the kind of analytics you get today with Google Analytics were only available to Webmasters who had implemented specialized logging systems and customized visualization. Now, my son Jonah gets analysis of his Web site that would have cost big bucks a decade ago.

However, there are still missing pieces. I believe we need:

• Large-scale, multi-tenant analytic database as a service, similar to Cloudant and Dynamo  but tuned/configured specifically for analytical workloads with the appropriate network infrastructure to support large loads
• Large-scale, multi-tenant statistics as a service – equivalent functionality to SPSS, R, SAS, but hosted and available as an affordable Web service. The best example of this right now is probably Revolution.   I guess the acronym would be Statistics as a Service  – or Statistics as a Utility
• Radically better visualization tools and services: I think that HTML5 has clearly enabled this and is making tools like Ben Fry's Processing more accessible so that the masses can do "artful analytics"

Once analytic databases and statistical functionality are available as Web services, I believe we'll see the proliferation of many new affordable and sophisticated analytic services that leverage these capabilities.  One of the best examples of this that I’ve been working on is a product created by Recorded Future.*  I believe it is one of the most advanced analytics companies in the world.  Christopher Ahlberg, Staffan Truve and the entire amazing team at Recorded Future are making some of the most sophisticated analytics in the world available to the masses.  Another example in Boston is Humedica, where my friend Paul Bleicher (founder of Phase Forward) is doing fantastic work – perhaps some of the most advanced health care analytics in the world. 

At this point there are still significant technical hurdles. But in the very near future, the challenge for most companies won’t be technology: it will be people, especially those who will no longer be limited to static reporting.

To be successful, businesses will need to build analytics-driven cultures: cultures where everyone believes it's his job to be information-seeking and to think analytically about the integrated data that can help him make better decisions and move faster every day.  

The #1 Step Toward Building the Right Culture

So, how do you go about building an analytics-driven culture? 

This is obviously a long discussion; Tom’s book is a great primer on this, particularly Chapter 7 where he points out that it’s analytical people that make analytics work.  But I think that the most important step for businesses is to rethink the way we build systems and to respond to the call to action created by the “consumerization” of information technology in the enterprise.

Every day new analytic tools are being made available to consumers over the Internet.  Those  consumers then walk into their workplaces and are faced with a pitiful cast of static, mundane and difficult-to-use tools provided by their IT organizations – most of which are 10+ years behind on analytics.  (Sorry if it hurts to hear this, but it's true – and I include myself as one of the people who is under-delivering on meeting enterprise end users' analytical expectations).

To build analytics-driven cultures,  businesses need to shift IT’s emphasis from process automation/"reengineering" (popularized by the late Michael Hammer and others) to decision automation.  With process automation, the average worker is treated as a programmable cog in a machine; with decision automation, the average worker is treated like an individual and an intelligent decision point.

This isn’t New-Age management theory or voodoo; there’s already a good track record for it.  Southwest Airlines empowers its gate agents do things that gate agents at American Airlines can’t even think of doing. Ditto for Nordstrom and Zappos in retail, where sales representatives have broad discretion on how they satisfy each customer. These companies believe in the individual identity of every person in that organization and use data and systems to empower them.  (The antithesis to these companies are companies that are stuck in post-industrial employment models  – most amusingly like Charlie Chaplin’s employer in "Modern Times" [watch]. And, yes, such companies do exist even today – otherwise we wouldn't have shows like "The Office" or movies like "Office Space.")

Unfortunately, the technology people in most companies don’t think this way, and most of their vendors are still stuck in the process automation mindset of the 80s and 90s.  If companies thought of their competitive edge as being decisions, they would expect their systems and their user experiences to be radically different.  In some ways, this is the process of thinking about an organization's systems in context of how data is going to be used/consumed instead of how the data is being created.  Because we tend to build systems with a serial mindset, many systems in today's organizations were built to "catch" the data that is being generated. But the most forward-thinking organizations are designing their systems from the desired analytics back into the data that needs to be captured/managed to support the decisions of the people in their organizations.  

Ironically, there are a bunch of us artificial intelligence (AI)  people from the 1970s and 1980s who experienced a technology trend called expert systems.  Expert systems really involved taking AI techniques and applying them to automating decision support for key experts. Many of the tools that were pioneered back in the expert systems days are still valid and have evolved significantly.

But we need to go one step further. Today, consumer-based tools are providing data that empower people to make better decisions in their daily lives. We now need business tools that do the same: Big Analytics that enable every employee – not just the CEO or CFO or other key expert – to make better everyday business decisions.

Here’s one great example of what can happen when you do this.

Over the past decade, synthetic chemists have begun to adopt quantitative/computational chemistry methods and decision tools to make them more efficient in their wet labs.  The use of tools such as Spotfire, RDKit and many others have begun to change the collaborative dynamic for chemists, by enabling them to design libraries using quantitative tools and techniques and perhaps most importantly to use these quantitative tools collaboratively.

It’s very cool to see a bunch of chemists working together to design compounds or libraries of compounds that they wouldn’t otherwise have created.  Modern chemists use their remarkable intuition along with incredibly powerful computational models running on high- performance cloud infrastructure.  They analyze how active or greasy a potential compound could be or how soluble, big, dense, heavy or how synthetically tractable it might be. Teams of chemists spread across the globe use this data to make better decisions about which compounds are worth synthesizing and which are not as they seek to discover therapies that make a difference in the lives of patients.  

This is where the magic comes from – from being decision-oriented not process-oriented.  Big Analytics can make average people junior artists – and natural artists wizards – by giving them the  infrastructure to make sense of data and interact with people.  It makes art and magic more repeatable.

Google Analytics is a good example of what happens when a business adopts an analytics-driven culture.  By using Google Big Table with the Google filesystem, Google expressed the value of its analytics in a way that could be given to anyone who manages a Web site. Google then watched the value of the analytics get more rich, statistical, analytical.  

I predict that this is what will happen in the rest of the business world as Big Analytics takes hold and analytics-driven cultures become more the norm  – and expected of every enterprise system.

I have seen what can happen close up, many times. As the co-founder of Vertica, I was fortunate in having Mozilla and Zynga as two of our best early customers.  Zynga thought of itself as an analytics company first and foremost.  Yes, their business was providing compelling games, but their competitive edge came from making analytics-based recommendations to their customers in real time within games and about where to place ads in their games.  Another company that I work with closely that is providing this type of capability is Medio Systems.*  Companies like Medio are democratizing Big Analytics for many companies and users.  

By rethinking how you build systems – within the context of how the data in the system will be analyzed/impactful, and thinking of every person in the company as an intelligent decision point – you’ll smooth the path to Big Analytics and 21st-century competitive analytics.

Analytics as Oxygen

In the future, analytics won’t be something that only analysts do. Rather, analytics will be like oxygen for everyone in your organization,  helping them make better decisions faster to out-maneuver competition.  Competing on analytics is not something to be done only in the boardroom: it’s most powerful when implemented from the bottom up.  

By empowering the people who are closest to the action in their organizations, Big Analytics  will have an impact that dwarfs the potential suggested by Executive Information Systems and Expert Systems. Companies that figure out how to leverage this trend will reap significant rewards – not unlike how companies like I2 and Trilogy realized the value of artificial intelligence decades after AI was perceived to have failed.  

*Disclosure: I am a board member, investor or advisor to this company.