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Becoming intimate with Big Data

by paul 0 Comments
Becoming intimate with Big Data
Come on guys, we are all made of blue glass inside.

About a year ago, I had a chance to have a discussion with one of the smartest person I’ve ever met, currently a board member of our company. This man has not only built his fortune out of nothing by being able to identify trends in the market and position his companies accordingly, he is also a genuine human being that forces admiration. But I digress. During this conversation, he mentioned that one of the things that helped him succeed was his capacity to understand the intrinsic values that define a generation. As an example, he mentioned that his generation, during the 90s was all about financial success. The following generation, the 2000s kids was all about fame (big brother anyone?). Then he told me that he was yet to figure out what my generation was all about. Since then I have been able to understand what makes my generation tick. After about a year of poking around, I think that I found the answer: my generation is the selfish generation. We are all selfish and think about our individuality. Look around, it’s selfies, freedom above all, my Facebook or my privacy, my right for an opinion, my right for an outlet to express my idea. I’m including myself in this of course, I am writing a blog after all. What’s interesting about this realization is to understand the consequences it has on the market, and specifically in a domain in which I have at least a bit of expertise: Big Data.

Big Data is driven by the individual

In a recent report from Forrester (link), companies were asked “Which use cases are driving the demand for continuous global data availability at your organization?”. The most common use case representing 52% of the answers received was 360-degree view of the business, product. This means that more than half of the big data drivers are coming from the consolidation of data to represent an individual unit of business. Make no mistake, in many cases, the product is you. What drives big data is the intimate knowledge of the individual. This makes perfect sense if you agree with the premise of my first paragraph: big data, and the market in general wants to cater to the selfish generation, and therefore is implementing solutions to know each individual personally.

This report is only one of numerous examples corroborating what I’m trying to explain here. We see machine learning algorithms and data scientists arguing about what algorithm is the best to target individual with the right add. IoT is tracking and personalizing every aspects of our lives. Anecdotally, I even witnessed the re-naming of a data analytics team in a large company to “Your Data”.

What does this mean for your Big Data implementation

First you need to consider that in order to be able to keep a relevant edge on your competition, you must be able to have access to a solution to individualize your data collection. I have expressed this opinion quite a bit, but I believe that ultimately individualization of data is a use case that requires its own solution. There is no magic end to end consolidation platform that will do everything. You need to consider a big data individualization platform, as opposed to a big data generic platform that you then try to morph in order to cater to your individualization needs. Once implemented, this data individualization platform can be leveraged to implement further features like real-time provisioning, data virtualization, personalized analytics or real customer centric support, but your platform must be intimate with your unit of business first.

5 reasons why software consolidation always fails

by paul 0 Comments
5 reasons why software consolidation always fails

Let’s start with a dare: I dare you to go to any large corporation, find an IT architect and ask them to give you a diagram of their complete architecture. I honestly think that they will politely ignore you, but for the sake of argument, let’s assume they are able to have access to this end-to-end architecture and that this architecture is accurate (and that you can find a screen or a piece of paper that is big enough to fit all of it in one page); by looking at this diagram, you will quickly understand why software consolidation is a very appealing proposition: multiple pieces of software serving the same purpose, duplicated teams, disparate processes… Think of all the money you can save if you buy this giant universal platform that everyone will use and will give you complete control over your IT!

Except that never happens. This giant convergent platform never gets implemented, even if it restricted to a certain functional vertical (e.g. billing, ERP, etc.). So why can’t we consolidate pieces of software into one? Let me give you my two cents.

Note: Hopefully the example I gave speaks for itself, but let me clarify the context of this article: I am specifically addressing software consolidation for very large organizations; of course if your organization employs 10 people and you’re all using google apps then this does not apply to you.

1. Large systems are complicated

This goes without saying but it’s better to say it: the answer to the ultimate question of life, the universe, and everything is fictional. Seriously though, it is so complicated to imagine a solution that would cater to the need of every company and every use case is ludicrous.

2. Enterprise softwares are outdated

While we can all agree that a universal solution is a utopia, this does not mean that you can’t create a solution that gives a large percentage of the solution, is what the smart guys at big enterprise software companies must have thought. To cater to the remaining few percents, customization can be added, (for a fee, charged by the software provider itself). And they have. These large enterprise software implementation have become colossi (at least I think that’s the plural of colossus) that are really hard to move: they are gigantic, expensive, slow-responsive and use backend technologies from the 70s.

As a result, these platforms become engorged and most of the innovation around them is about managing them more efficiently rather than offering a competitive advantage against the rest of the market. Let’s be clear, I’m not saying big enterprise software is dead, they are necessary.

But in an established competitive environment, you distinguish yourself by fighting for the edges, which means fast reactivity, which is incompatible with these outdated massive implementations.

3. Companies need solutions not platforms

How does one find its competitive edge? By implementing efficient targeted solutions. And as far as I could witness, this trend does not seem to be slowing down, quite the contrary (which I believe is a very healthy response). However, the multiplication of targets solution contributes to rendering the consolidation problem even more complicated and necessary.

4. Budget and learning curves are real constraints

Again, this might seem banal but is worth saying. An enterprise is driving a team of people, with their own expertise and responding to the demand of the market. Any change has a cost upfront and downstream, especially when replacing a well-known software as part as a consolidation effort.

5. Consolidation softwares aren’t business driven

In this realm where a single solution does not exist and businesses tend to purchase more and more specific solution, data consolidation platform flourish. Unfortunately, in order to cater to the complexity of the systems we’re dealing with, they are often driven by the underlying technology and not the business requirements.

This sounds a lot like business jargon, so let me explain this with an example: your software relies on its data back-end, and if you have tried to consolidate multiple back-end systems together, whether you use a traditional or distributed data platform, the first thing you end up doing is designing the data schema of the platform, then implement a way for the data to move from multiple backends to this system.

This is not the way your business want to see consolidation. Your business has a clear idea of what is the most important entity from which they can gain insight (for example analyzing user or customer behavior). This means that your consolidation platform schema needs to always be able to adapt to your business and not your business to try and fit into a schema.

So what’s next?

Software consolidation has tremendous application in giving insight to any business owner. But it needs to be a solution, not a generalized overhaul of the IT eco-system. Therefore I think it requires a good data virtualization solution. This solution must have at least the following qualities:

  1. Be business oriented
  2. Be able to publish fresh data on demand
  3. Be flexible enough to interface with any new element of the IT eco-system
  4. Be able to handle any amount of data
  5. Be able to publish results using known methods (using standard connectors/languages)

Of course, I work for a company that provides all these capacities, but that does not make my analysis unfounded. I would not work for a company if I didn’t believe it provided something truly unique and needed by the market. I genuinely believe that this type of solution will be the cement of the future IT eco-systems.