Challenges For On-premise Vendors Transitioning To SaaS

As more and more on-premise software vendors begin their journey to become SaaS vendors they are going to face some obvious challenges. Here's my view on what they might be.

The street is mean but you can educate investors

Sharp contrast between Amazon and Apple is quite clear. Even though Amazon has been in business for a long time with soaring revenue in mature categories the street sees it as a high growth company and tolerates near zero margin and surprises that Jeff Bezos brings in every quarter. Bezos has managed to convince the street that Amazon is still in heavy growth mode and hasn't yet arrived. On the other hand despite of Apple's significant revenue growth—in mature as well as in new disruptive categories—investors treat Apple very differently and have crazy revenue and margin expectations.

Similarly, traditional pure SaaS companies such as Salesforce is considered a high growth company where investors are focused on growth and not margins. But, if you're an on-premise vendor transitioning to SaaS the street won't tolerate a hit on your margins. The street would expect mature on-premise companies to deliver on continuous low double digit growth as well as margins without any blips and dips during their transition to SaaS. As on-premise vendors change their product, delivery, and revenue models investors will be hard on them and stock might take a nosedive if investors don't quite understand where the vendors are going with their transition. As much as investors love the annuity model of SaaS they don't like uncertainty and they will punish vendors for lack of their own understanding in the vendor's model. It's a vendor's job to educate investors and continuously communicate with them on their transition.

Isolating on-premise and SaaS businesses is not practical

Hybrid on-premise vendors should (and they do) report on-premise and subscription (SaaS) revenue separately to provide insights to investors into their revenue growth and revenue transition. They also report their data center related cost (to deliver software) as cost of revenue. But, there's no easy way, if at all there's one, to split and report separate SG&A costs for their on-premise and SaaS businesses. In fact combined sales and marketing units are the weapons incumbents on-premise vendors have to successfully transition to SaaS. More on that later in this post.

The basic idea behind achieving economies of scale and to keep the overall cost down (remember margins?) is to share and tightly integrate business functions wherever possible. Even though vendors sometime refer to their SaaS and on-premise businesses as separate lines of businesses (LoBs), in reality they are not. These LoBs are intertwined that report numbers as single P&L.

Not being able to charge more for SaaS is a myth

Many people I have spoken to assume that SaaS is a volume-only business and you can't charge customers what you would typically charge your customers in your traditional license and maintenance revenue business model. This is absolutely not true. If you look at some of the deal sizes and length of SaaS contracts of pure SaaS companies they do charge a premium when they have unique differentiation regardless of volume. Customers are not necessarily against paying premium - for them it is all about bringing down their overall TCO and increasing their ROI with reduced time to value. If a vendor's product and its delivery model allow customers to accomplish these goals they can charge them premium. In fact in most cases this could be the only way out. As a vendor transitioning from on-premise to SaaS their cost is going to go up; they will continue to invest into building new products and transitioning existing products and they will significantly assume the cost of running operations on behalf of their customers to deliver software as a service. They not only will have to grow their top-line to meet the growth expectations but to offset some of the cost to maintain the margins.

Prime advantage on-premise incumbents have over SaaS entrants

So, what does work in favor of on-premise vendors who are going through this transition?

It's the sales and marketing machine, my friends.

The dark truth about selling enterprise software is you need salespeople wearing suits driving around in their BMWs to sell software. There's no way out. If you look at high growth SaaS companies they spend most of what they earn on sales and marketing. Excluding Workday there is not much difference in R&D cost across vendors, on-premise or SaaS. Workday is building out its portfolio and I expect to see this cost go down in a few years.

Over a period of time, many on-premise vendors have built a great brand and achieved amazing market penetration. As these vendors go through SaaS transition they won't have to spend as much time and money educating the market and customers. In fact I would argue they should thank other SaaS vendors for doing the job for them. On-premise vendors have also built an amazing sales machine with deep relationship with customers and reliable sales processes. If they can maintain their SG&A numbers they will have enough room to deal with a possible initial hit on revenue and additional cost they would incur as they go through this transition.

Be in charge of your own destiny and be aggressive

It's going to be a tough transition regardless of your loyal customer base and differentiating products. It will test the execution excellence of on-premise vendors. They are walking on a tight rope and there's not much room to make mistakes. The street is very unforgiving.

Bezos and Benioff have consistently managed to convince the street they are high growth companies and should be treated as such. There's an important lesson here for on-premise vendors. There is no reason to label yourself an on-premise vendor simply making a transition. You could do a lot more than that; invest into new disruptive categories and rethink existing portfolio. Don't just chase SaaS for its subscription pricing but make an honest and explicit attempt to become a true SaaS vendor. The street will take a notice and you might catch a break.

Rise Of Big Data On Cloud

Growing up as an engineer and as a programmer I was reminded every step along the way that resources—computing as well as memory—are scarce. The programs were designed on these constraints. Then the cloud revolution happened and we told people not to worry about scarce computing. We saw rise of MapReduce, Hadoop, and countless other NoSQL technology. Software was the new hardware. We owe it to all the software development, especially computing frameworks, that allowed developers to leverage the cloud—computational elasticity—without having to understand the complexity underneath it. What has changed in the last two to three years is a) the underlying file systems and computational frameworks have matured b) adoption of Big Data is driving the demand for scale out and responsive I/Os in the cloud.

Three years back, I wrote a post, The Future Of The BI In Cloud where I had highlighted two challenges of using cloud as a natural platform for Big Data. The first one was to create a large scale data warehouse and the second was lack of scale out computing for I/O intensive applications.

A year back Amazon announced RedShift, a data warehouse service in the cloud, and last week they announced high I/O instances for EC2. We have come a long way and more and more I look at the current capabilities and trends, Big Data, at scale, on the cloud, seems much closer to reality.

From a batched data warehouse to interactive analytic applications:

Hadoop was never designed for I/O intensive applications, but Hadoop being a compelling computational scale out platform developers had a strong desire to use it for their data warehousing needs. This made Hive and HiveQL popular analytic frameworks but this was a sub optimal solution that worked well for batch loads and wasn't suitable for responsive and interactive analytic applications. Several vendors realized there's no real reason to stick to the original style of MapReduce. They still stuck to the HDFS but significantly invested into alternatives to Hive which are way faster.

There are series of such projects/products that are being developed on HDFS and MapReduce as a foundation but by adding special data management layers on top of it to run interactive queries much faster compared to plain vanilla Hive. Some of those examples are Impala from Cloudera and Apache Drill from MapR (both based on Dremel), HAWQ from EMC, Stinger from Hortonworks and many other start-ups. Not only vendors but the early adopters such as Facebook created Hive projects such as Presto, an accelerated Hive, which they recently open sourced.

From raw data access frameworks to higher level abstraction tools: 

As vendors continue to build more and more Hive alternatives I am also observing vendors investing in higher level abstraction frameworks. Pig was amongst those first higher level frameworks that made it easier to express data analysis programs. But, now, we are witnessing even higher layer rich frameworks such as Cascading and Cascalog not only to write SQL queries but write interactive programs in higher level languages such as Clojure and Java. I'm a big believer in empowering developers with right tools. Working directly against Hadoop has a significant learning curve and developers often end up spending time on plumbing and other things that can be abstracted out in a tool. For web development, popularity of Angular and Bootstrap are examples of how right frameworks and tools can make developers way more efficient not having to deal with raw HTML, CSS, and Javascript controls.

From solid state drives to in-memory data structures: 

Solid state drives were the first step in upstream innovation to make I/Os much faster but I am observing this trend go further where vendors are investing into building in-memory resident data management layers on top of HDFS. Shark and Spark are amongst the popular ones. Databricks has made big bets on Spark and recently raised $14M. Shark (and hence Spark) is designed to be compatible with Hive but designed to run queries 100x times faster by using in-memory data structures, columnar representation, and optimizing MapReduce not to write intermediate results back to disk. This looks a lot like MapReduce Online which was a research paper published a few years back. I do see a UC Berkeley connection here.

Photo courtesy: Trey Ratcliff

How I Accomplished My Personal Goal Of Going To Fewer Meetings

As part of my job I have to go to a lot of meetings. As it turns out, all meetings are not equally important. Many times, either during a meeting or after the meeting, I end up asking myself why the hell did I go to this meeting. Sounds familiar?

A couple of yeas back, instead of just whining about it, I decided to do something about this situation. I set a personal goal to cut down the meetings that I would go to by 20%. Not only I succeeded but I kept the same goal the year after and I accomplished that as well.

This is how I did it:

Ask for prep documents and an upfront agenda

If the meeting that I am invited to does not have an agenda in the meeting request, I ask for it before I commit to it. This approach has two positive effects: 1) it forces an organizer to think what he/she wants to accomplish that invariably results in a productive meeting b) I have an opportunity to opt out if I don't receive an agenda or the agenda doesn't require my presence. I also ask for prep documents for a meeting; I prepare for all my meetings and I firmly believe that meeting time should be judiciously used to discuss what people think about the information and make important decisions as opposed to gathering information that could have been accomplished prior to a meeting.

Opt-out with an alternative ahead of a meeting

If I believe the agenda is partially useful but I won't add any value by being part of the meeting, I connect with an organizer ahead of the meeting to clarify a few things or give my input, either in person or via email or phone. In most cases, me reaching out to an organizer serves the purpose and I don't have to go to the actual meeting. If I do end up having to go for such meetings I ask an organizer for a permission to either walk-in late or leave early. This saves me a lot of time and I don't have to sit through a meeting when I am not required to be there.

Postpone a non-critical meeting

If I see that I am invited to a non-critical meeting, I ask to postpone it by a few days citing my non-availability. In many cases, the issue would have been resolved in a few days and we won't be required to meet. It is important to decline the original meeting request and ask the organizer to create a new meeting request in future even if you have an intent to postpone and not cancel the meeting. Most people don't create a new meeting request and I won't hear back from them.

DVR the meeting

I ask organizers to record certain meetings when I believe that parts of a meeting would be useful at later stage. I fast forward non-interesting parts of such meetings and listen to the parts that I like. I underestimated the effectiveness of listening to a recording until I organized a few meetings as podcasts and listened to them during my commute. Most fascinating part of this approach, other than an ability to fast forward, is being able to listen to a meeting as an information session without having to worry about understanding all details and anxiety to make decisions.

If everything else fails, multitask

I believe it is somewhat rude and distracting to others when people bring their laptops/tablets to a meeting and keep working on it and not pay attention to the meeting. But, this isn’t true when the meeting is an audio conference. I don't work on my laptop or tablet when I am in a meeting room; I am fully committed to the meeting and completely present. However, for certain meetings, when I know that I don't have an option to opt out and it is going to be a waste of time, I dial into the meeting instead of being there in person. I do continue to work on my laptop while participating into the meeting. This is not to confuse with remote meetings that I participate in or lead when all people are not at the same location. I am fully present for those meetings.

Before you ask, yes, I did meticulously measure the time I saved. I had a simple spreadsheet that did a great job. I was a little hesitant in the beginning to push back for meetings but l became more comfortable as I started saving more and more time. I would highly encourage you to follow these rules or create your own and save yourself some quality time that you can use do other useful things.

Photo courtesy: Ho John Lee

Big Data Platform As Technology Continuum

Source: Wikipedia

A Russian chemist, Dimitri Mendeleev, invented the first periodic table of elements. Prior to that, scientists had identified a few elements but the scientific world lacked a consistent framework to organize these elements. Dimitri built upon existing work of these scientists and invented the first periodic table based on a set of design principles. What fascinates me more about his design is that he left a couple of rows empty because he predicted that new elements would be discovered soon. Not only he designed the first periodic table to create a foundation for how elements can be organized but he anticipated what might happen in future and included that consideration in his design.    

It is unfortunate that a lot of us are trained to chase a perfect answer as opposed to designing something that is less than perfect, useful, and inspirational to future generations to build on it. We look at technology in a small snapshot and think what it can do for me and others now. We don't think of technology disruption as a continuum to solve a series of problems. Internet started that way and the first set of start-ups failed because they defined the problem too narrowly. The companies that succeeded such as Google, Amazon, eBay etc. saw Internet as a long term trend and didn't think of it in a small snapshot. Cloud and Big Data are the same. Everyday I see problems being narrowly defined as if this is just a fad and companies want to capitalize on it before it disappears.

Build that first element table and give others an imagination to extend it. As an entrepreneur you were not the first and you are not going to be the last trying to solve this problem.

The Dark Side Of Big Data

Latanya Sweeney, a Harvard professor Googled her own name to find out an ad next to her name for a background check hinting that she was arrested. She dug deeper and concluded that so-called black-identifying names were significantly more likely to be the targets for such ads. She documented this in her paper, Discrimination in Online Ad Delivery. It is up to an advertiser how they pick keywords and other criteria to show their ads. Google, like most other companies for which advertising is their primary source of revenue, would never disclose details of algorithms behind their ad offerings. Google denied AdWords being discriminatory in anyway.

Facebook just announced they are planning to give more options to their users to provide feedback regarding which ads are relevant to them and which ads are not. While on surface this might sound like a good idea to get rid of ads that are not relevant and keep marketers as well as users happy, this approach has far more severe consequences than what you might think. In case of the Google AdWords discrimination scenario the algorithm is supposedly blind and has no knowledge of who is searching for what (assuming you're not logged in and there is no cookie effect), but in case of Facebook, the ads are targeted based on you as an individual and what Facebook might know about you. Algorithms are written by human beings and knowingly or unknowingly they could certainly introduce subtle or blatant discrimination. As marketers and companies that serve ads on behalf of marketers know more about you as as an individual, and your social and professional network, they are a step closer to discriminate their users, knowingly or unknowingly. There's a fine line between stereotyping and what marketers call "segmentation."

AirBnB crunched their data and concluded that older hosts tend to be more hospitable and younger guests tend to be more generous with their reviews. If this is just for informational purposes it's interesting. However what if AirBnB uses this information to knowingly or unknowingly discriminate young hosts and old guests?

A combination of massively parallel computing and sophisticated algorithms to leverage this parallelism as well as ability of algorithms to learn and adapt to be more relevant, almost in real-time, are going to cause a lot more of such issues to surface. As a customer you simply don't know whether the products or services that you are offered or not at a certain price is based on any discriminatory practices. To complicate this further, in many cases, even companies don't know whether insights they derive from a vast amount of internal as well as external data are discriminatory or not. This is the dark side of Big Data.

The challenge with Big Data is not Big Data itself but what companies could do with your data combined with any other data without your explicit understanding of how algorithms work. To prevent discriminatory practices, we see employment practices being audited to ensure equal opportunity and admissions to colleges audited to ensure fair admission process, but I don't see how anyone is going to audit these algorithms and data practices.

I have no intention to paint a gloomy picture and blame technology. Disruptive technology always surfaces socioeconomic issues that either didn't exist before or were not obvious and imminent. Some people get worked up because they don't quite understand how technology works. I still remember politicians trying to blame GMail for "reading" emails to show ads. I believe that Big Data is yet another such disruption that is going to cause similar issues. We should not shy away from these issues but should collaboratively work hard to highlight and amplify what these issues might be and address them as opposed to blame technology to be evil.

Photo Courtesy: Jonathan Kos-Read 

Purple Squirrels

It is fashionable to talk about talent shortage in the silicon valley. People whine about how hard it is to find and hire the "right" candidates. What no one wants to talk about is how the hiring process is completely broken.

I need to fill headcount: This is a line that you hear a lot at large companies. Managers want to hire just because they are entitled to hire with a "hire or lose headcount" clause. Managers spend more time worrying about losing headcount and less time finding the right people the right way.

Chasing a mythical candidate: Managers like to chase purple squirrels. They have outrageous expectations and are far removed from reality of talent market. Managers are also unclear on exactly what kind of people they are looking to hire.

Bizarre interview practices: "How many golf balls can fit in a school bus?" or "can you write code with right hand while drawing a tree with left hand?" We all have our favorite bizarre interview stories. But, even if not bizarre, by and large, interview practices have been quite unscientific, inconsistent, and highly subjective. Most companies don't have a good way to objectively conduct interviews and identify the right candidates to hire. This sounds silly but unfortunately it's true.

If we are really serious about talent we should focus on our ability to attract, acquire, and retain talent as opposed to whining about it.

Always be sourcing

Cultivate hiring culture; always keep looking for people in your network even if you have no immediate plans to hire. In many cases, the best hires are the ones that are not actively looking for a job. The references from your best current employees are the right ones to get started with. Go to conferences and talk about your company and projects. Use this as a learning opportunity to calibrate your understanding of the market and seek out an outsider's perspective on what might be the right hiring strategy for your organization. You are constantly making an effort to attract talent. Treat this as an ongoing task as opposed to one time hiring activity.

Pulse has redesigned their technical hiring process by introducing a "try before buy" model where the prospects can get to actually work with Pulse's team on a real project as part of an interview process. Hiring someone is a critical decision and this approach is a win-win situation. This is also the reason why interns make good hires as both sides get enough time to check each other out.

Treat interviewing as an important skill

Most employees are trained to do their work but they have a little or no training in interviewing other people. I find it astounding that we hire social scientists, ethnographers, and user researchers to meticulously and scientifically interview users to better understand their behavior and eventually design a product that meets or exceeds their expectations. But, we don't spend anytime training our own employees to better understand the prospects and hire the ones that would actually design these products.

"Years ago, we did a study to determine whether anyone at Google is particularly good at hiring. We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship."

I have seen interviewers either rejecting interviewees in the first few minutes of an interview solely relying on their hunch and intuition or mistaking interviewee's confidence as his or her competence without any kind of objectivity. I find it strange that the technical as well as the business folks who believe in science, have been trained to trust empirical evidence, and possess great analytical skills fall for subjective interpretations based on their pre-conceived biases. Interviewing objectively is hard because it is boring to follow an objective approach leaving your subjective smartness aside. Very boring and very hard.

Look for behaviors and not just skills

Have your interviews designed to measure past behaviors and not skills alone. Skills are easy to learn, but behaviors are hard if not impossible to change. Start with your most successful employees and identify what behaviors they exhibit and how these behaviors have made them successful and valuable to your company. I cringe when I hear words "chemistry" and "cultural fit." These are actually behaviors that people find it hard to describe and evaluate. There's a way to break down this chemistry and cultural fit into measurable behaviors that you could look for during an interview. Don't judge people based on what they can do during an interview because it does not represent a real working life scenario. Asking people to solve a puzzle or draw something on a whiteboard during an interview doesn't prove much. Infamous for ridiculous interview practices Google has confessed them to be complete waste of time.

"On the hiring side, we found that brainteasers are a complete waste of time. How many golf balls can you fit into an airplane? How many gas stations in Manhattan? A complete waste of time. They don’t predict anything. They serve primarily to make the interviewer feel smart." -- Laszlo Block, senior vice president of people operations at Google.

Unless you have designed a consistent interviewing process that focuses on asking questions to objectively assess candidates based on the behaviors they have exhibited in their previous jobs you will become a victim of your own biases and subjective interpretations.

Retaining talent is as important as attracting and acquiring talent. A separate blog post on that topic some other time.

Photo courtesy: Harvard Business Review

Chasing That Killer Application Of Big Data

I often get asked, "what is the killer application of Big Data?" Unfortunately, the answer is not that simple.

In the early days of enterprise software, it was the automation that fueled the growth of enterprise applications. The vendors that eventually managed to stay in business and got bigger were/are the ones that expanded their footprint to automate more business processes in more industries. The idea behind the killerness of some of these applications was merely the existence and some what maturity of business processes in alternate forms. The organizations did have financials and supply chain but those processes were paper-based or part-realized in a set of tools that didn't scale. The objective was to replace these homegrown non-scalable processes and tools and provide standardized package software that would automate the processes after customizing it to the needs of an organization. Some vendors did work hard to understand what problems they were set out to solve, but most didn't; they poured concrete into existing processes.

Traditional Business Intelligence (BI) market grew the same way; the customers were looking for a specific set of reporting problems to be solved to run their business. The enterprise applications that automated the business processes were not powerful enough to deliver the kind of reporting that organizations expected to gain insights into their operations and make decisions. These applications were designed to automate the processes and not to provide insights. The BI vendors created packaged tools and technology solutions to address this market. Once again, the vendors didn't have to think about what application problems the organizations were trying to solve.

Now with the rise of Big Data, the same vendors, and some new vendors, are asking that same question: what's the killer application? If Big Data turns out to be as big of a wave as the Internet or cloud we are certainly in a very early stage. This wave is very different than the previous ones in a few ways; it is technology-led innovation which is opening up new ways of running business. We are at an inflection point of cheap commodity hardware and MPP software that is designed from ground up to treat data as the first class citizen. This is not about automation or filling a known gap. I live this life working with IT and business leaders of small and large organizations worldwide where they are struggling to figure out how best they can leverage Big Data. These organizations know there's something in for them in this trend but they can't quite put a finger on it.

As a vendor, the best way to look at your strategy is to help customers with their Big Data efforts without chasing a killer application. The killer applications will be emergent when you pay attention and observe patterns across your customers. Make Big Data tangible for your customers and design tools that would take your customers away from complexity of a technology layer. The organizations continue to have massive challenges with semantics as well as the location and format of their data sources. This is not an exciting domain for many vendors but help these organizations bring their data together. And, most importantly, try hard to become a trusted advisor and a go-to vendor for Big Data regardless of your portfolio of products and solutions. Waiting for a killer application to get started or marketing your product as THE killer application of Big Data are perhaps not the smartest things to do right now.

Big Data is a nascent category; an explosive, promising, but a nascent category. The organizations are still trying to get a handle on what it means to them. The maturity of business processes and well-defined unsolved problems in this domain are not that clear. While this category plays out on its own don't chase those killer applications or place your bets on one or two killer applications. Just get started and help your customers. I promise you shall stumble upon that killer application during your journey.

About the picture: I took this picture inside a historic fort in Jaisalmer, India that has rich history. History has taught me a lot about all things enterprise software as well as non-enterprise-software.