I recently on multiple occasions engaged into conversations about whether or not Fortune 500 organizations are ready to move all their data to the cloud. While I’m not arguing about the benefits of distributed systems, I did encounter a significant number of organizations that are not ready to move to a SaaS model. Despite the obvious security reasons, I think it is an maintaining control over the core of your business to drive innovation is crucial (see Telsa example). Furthermore, many organizations’ strategy seem to be going towards build IaaS/PaaS and eventually SaaS within their own IT. These tendencies lead me to believe the dichotomy between SaaS and traditional in-house implementation isn’t absolute. Therefore, the market will see the advent of solutions enabling control over internal data while leveraging SaaS functionalities.
Since I work for a company offering one of these solutions, I wrote a white paper about it, so here it. Enjoy the read!
I am an ambitious person. In my mind, whenever I do something (I’m refraining myself to use the word accomplish because I’m never truly satisfied), I always have this Batman Begins scene in my head: Rachel sees Bruce Wayne running out with two models after buying out a hotel; Bruce Wayne poses and says: “Rachel, all- all this, it- it’s not me, inside, I am, I am more.”. For some reason, mostly because I am a Batman nerd, this scene resonates so much. The funny thing is that I sometimes forget Rachel’s response: “Bruce, deep down you may still be that great kid you used to be, but it’s not who you are underneath, it’s what you *do* that defines you.”. Here it is: if you think you are more, then show that you can do more. I was actually reminded of Rachel’s wisdom this week by a friend/mentor of mine who enlightened a path for me to do more and get better at my job. Looking back on it, I realized that I managed to have an unconscious belief that I reached a finite knowledge/expertise about my job which could not be any further from the truth. That’s why in this blog post, I’d like to give you what really helps me going: You are never done. You don’t get to finish the game. And that’s pretty awesome.
The hinder nature of achievement
The first thing to acknowledge is the fact that the belief of achieving a certain potential is crippling. As mentioned in my very recent life experience, my unconscious belief of having reached a certain expertise in my job prevented me to get to the next steps of my career. But this is true for a lot of other things. To give you another personal example, being a fairly dedicated runner, you get to assume a few paces at which you run a certain type of races, e.g. this pace is my 5K pace, and it’s very hard to re-teach your brain to think that you can go faster than your “5K pace” when racing. It’s when you have no pre-conceived notion of what you are capable of that you can improve. But here is the secret: when you do the best race of your life, you did the best race of your life… so far! And in a similar manner that the best way to combat cognitive biases is to deliberately scrutinize them when trying to form a fallacy-free thought, you should look for your unconscious beliefs of potential and strip them of their crippling nature.
The value of consistency
The fun thing when you realize that all of your preconceived beliefs about your current potential are ill-informed, is that you get to contemplate the abyss of the work that needs to be done in every aspect of your life if you want not to get schackled by them. If you ask me, staring into the abyss is always fun. All kidding asides, it raises a very difficult question: if I can always improve, how do I get better? I think that this plays very nicely with one of the most important core belief I and many share: consistency. Let me take an example from the fitness realm again, in this case evaluating the benefits of muscle confusion versus progressive overload (spoiler, the title of this article: ‘Muscle Confusion’ Is Mostly a Myth). Too often we are confronted with miracle fitness solutions, founded on the idea that dramatically shaking things up will enable you to unlock your maximum potential. As debunked here, the only method with tangible results is consistent incremental improvement. I think we can draw a fairly straight forward corollary to the selection of our method of improvement. A muscle confusion-like does not work for improving the skills that we are trying to improve here: you are trying to improve something at which you are already proficient, which implies that you have already done a lot of work in figuring out what works best and what doesn’t. The only way to get better is to slowly augment the resistance. Look at what you have done so far. If you’re comfortable with it add more until you get comfortable. Repeat.
Coping with never being done
Now if you agree with me that you will never reach a finite potential and that the only way to improve is consistent slow incremental changes, this can be a little overwhelming. Since I agree with myself, at least for the next 10 minutes, I am a little overwhelmed. The way that I found I could cope with this incessant work that will eventually lead to my death is three fold. First, I plan things out. I set actionable, trackable short-term goals. For instance, I wanted to get better at writing and communicating so I set myself a goal of writing a blog post every week. I have done that so far, even if I missed one week over that past few months. Secondly, I prioritize. I acknowledge for instance that I don’t want to sacrifice some of the time I am spending working or running playing Magic, and that therefore I will not get to play the pro-tour any time soon or ever for that matter. Understanding what you decide not to not improve is crucial. Finally, I allow myself to enjoy the present. Granted, I’m not super good at it as of today, but I haven’t reached my full potential yet!
I spent last week enjoying the Cassandra Summit, so much that I did not take the time to write a blog post. I had a few ideas but I chose quality over quantity. That being said, something interesting happened at the summit: we coined the term “augmentation” for one of my companies key go to market use case, instead of data layer modernization or digitalization. even got the opportunity to try both terms to the different people visiting our booth. In this extremely small sample, people really tended to have a much better degree of understanding when I used the word augmentation, which got me thinking. I even read a very interesting article from Tom O’Reilly called: Don’t Replace People. Augment Them. in which he argues against technology fully replacing people. Could this concept of augmentation be applied in a broader scale to understand our data technology trends? Maybe, at least that’s what I’m going to try to lay out in this article.
Technological progress relies on augmentation.
That’s the first thing that struck me when I pondered on augmentation in our world, and more specifically when it comes to software. At the exception of very few, the platforms, apps and tool that we use are all based on augmentation of existing basic functions: Amazon? Augmentation of store using technology. Uber? Augmentation of taxis. Chatbots? Augmentation of chat clients. Slack? Augmentation of email + chats. Distributed/Cloud applications? Augmentation of legacy applications. To some extent even Google is an augmentation of a manual filing system. I would admit listing examples that confirm an idea that I already had is close to a logical fallacy, so I tried to find counter examples, i.e. software solutions that try to introduce completely new concepts, but could not think of any. Of course we could argue over semantics in defining what constitute true innovation versus augmentation of an existing technology, but ultimately I think it is fair to say that the most successful technologies are augmenting our experience rather than being completely disruptive, despite what most of my field would argue. Therefore, augmentation must be at least considered as part of the future of any software industry, such as the Big Data industry.
Augmentation is better than transformation
Human nature needs comfort, that’s why most of us prefer augmentation over disruption. By disruption, I’m talking about transforming or replacing the existing systems, not adding features: selling unpaired socks over internet is not disrupting the sock industry, despite what the TED talks would like me to believe. Seriously, when you have existing technologies, as every company does, a replacement/transformation is a hard pill to swallow. Loss of investment, knowledge, process, etc. It is especially risky and complex when talking about data layer transformation, as I argued before in this very blog. So when given a choice, augmenting existing data layers is an obvious choice for risk-advert IT organizations.
Augmentation drives innovation
Perhaps the most convincing argument towards acknowledging that augmentation is the future of data is the analysis of the most innovative big data software solutions: machine learning, neural networks and all of these extremely complex systems which behaviors are almost impossible to predict, even for experts. These systems are design to augment their own capabilities, instead of having a set of deterministic rules to follow. Indeed, these systems are designed to approach the capabilities of complex biological systems and therefore incorporate their “messiness”. We can’t think of big data systems using physics thinking (i.e. here is an algorithm, here is a set of parameters, this is the result expected), but we should rather rely to biology thinking (i.e. what is the results I get if I input this parameter). A great example of this type of thinking is Netflix’s Chaos Monkey, a service running on AWS to simulate failures and understand the behavior of their architecture. Self-augmentation is the principle upon which the technologies of the future are built. We understand the algorithms we input but not necessarily the outcome, which can have unintended consequences sometimes (see: Microsoft Tay), but ultimately is a better pathway to intelligent technologies. I’m a control freak, and not being able to understand a system end to end drives me nuts, but I’m willing to relinquish my sanity for the good of Artificial Intelligence.
Conclusion
With software Augmentation being part of our everyday life, a safer and easier way to add features to existing data layer, and the core concept of machine learning, I think it is fair to say that it is the future of Data. Did I convince myself? Yes, which is good because my opinion is usually my first go to when it comes to figuring out what I think. Seriously though, what do you think? As always, I long to learn more and listen to everyone’s opinion!
