The Big Data market in 2017
Accepting reality is not trivial. As we sit in our echo-chambers, particularly exacerbated by our social networks, preference algorithms and suggested searches, our cognitive biases betray our picture of the world around us. Add that to the fact that everyone else than us is an idiot with whom we should not engage in a conversation (or if you prefer, call this mild social anxiety), and pretty soon you can convince yourself of anything. With this in mind, I decided to take the time today to expose my understanding of the reality of the big data market in 2017, for large entreprises. While it is inherently biased, arguably like any piece of writing, I did try to do my research reading a lot of white papers recently (of which you can find some reference below), but mostly, this is my domain of expertise which means I’m confronted to it every day. Of course, this categorization is subject to discussion and constructive criticism that I always welcome.
Out, or very little relevancy
- Data Lakes: The fascination for unlimited data distribution has passed. Enterprises struggle to find a use to their data lakes and the layers written on top of them to make them useful seem too much effort for little reward.
- Pure data analytics: The terms Data Analytics or Business Intelligence encapsulate a vast number of concepts that will always be useful one way or another in our data driven world. What is nowadays losing momentum is solutions making analytics the end goal (analyzing trends, population sub group preferences, etc.). BI is a very small portion of what Big Data offers and if the end goal of a solution is to give you trend analytics, it is too reductive.
- SOA, ESBs, Convergent applications: This is been dead for a while but worth mentioning. The idea of a single convergent enterprise solution to encapsulate all data and functionalities is practically not feasible (too much market change, too much cost, too much complexity, too little agility).
Extremely relevant for 2017
- Agile data platforms: At the opposite end of ESBs and massive consolidation into one data system is the micro-services architecture. The architecture enables extremely rapid and agile deployment of applications to respond to an ever changing market where end customers have more choices than ever and thus are very hard to retain. The bottleneck of micro-services architectures is often data. Being able to consolidate, cleanse and expose rapidly data to anywhere is a complicated proposition but some platforms can do it. If a platform is able to integrate from multiple sources, consolidate and expose data rapidly, then it enables today’s hottest use cases: digital transformation, agile test data management, micro-services implementation, and more.
- Cloud enablement: More than ever, and despite previous reticense vis-a-vis security (just like older generations are still reluctant to use their credit card numbers on the web), the movement to cloud applications and platforms. Enabling the cloud, that is not only exposing/migrating data to cloud applications but also ensuring security, compliance and control over the data exposed is therefore a very important market trend.
- Data Personalization: we live in a world where everyone expects their experience to be catered to them. Having to repeat your identity while being tossed from department to department on a help desk line is one of the most infuriating experience (after the complete loss of human rights and dignity one experiences in an airport). Seriously though, enabling the understanding of the individual is crucial, whether that individual is a person, a product or a machine in IoT use cases.
Not quite there yet
- Predictive Individual Analytics: We already see some implementations of this in ad personalization or preference settings, but being able to predict what an entity (a person, a machine, a car, a product) will do, what it wants and needs is going to open the door to systems that give answer instead of respond to questions. It requires the problem of data personalization to be solved beforehand though.
- Smart Data Discovery: Once agile data platforms are in place, the use-case of automated data mining will explode. Too many systems with too little experts on the systems will give birth to solution that’ll enable the enterprise to recover a fair percentage of the relevant data without human intervention.
- Expert AI systems: Finally, and most exciting of all are expert AI systems. These are software that will replace the way that data is currently fed to our everyday software (CRM, machine monitoring, marketing analytics, etc.). The use cases are still not clear in my head but I know that finding the point where human intervention is the most costly (where it requires pattern recognition), and replacing it with automated AI will be a game changer.