Let’s put 2016 to bed and look to the future; what does it hold in store for Big Data? Well, after years of chipping away at the coal face of what was a fledgling science, researchers and developers are closer than ever before to achieving their glorious vision of a data-utopia, in which insight powers all.
Note that I said “closer than ever before.” We’re not quite at that stage yet, but that is not to say that there aren’t some incredible advances to be made in Big Data this year. With this in mind, let’s take a look at some of the Big Data trends which will be causing a stir in 2017.
Machine learning is going to see some major developments in 2017. Funding is pouring into machine learning departments across a range of different companies, and the race is well and truly on to create more effective and more efficient systems to handle all of that data. Watch this space!
Cognitive Automation v.1.
Artificial intelligence is very much on the cutting edge of BI and analytics, but it is still in its infancy. The Holy Grail in AI is cognitive automation; the point at which intelligent machines will be able to cognitively reflect and respond to stimuli. Expect to see forays into this territory in 2017, although I’ve put “v.1.” because I feel the finished article is still some way off.
When the news broke from Japan that artificially intelligent systems are taking over roles previously filled by real-life, flesh-and-blood data analysts, there was understandably a degree of anxious hand-wringing from data professionals in this country. 2017 will see a reimagining of the role of human involvement in data science, and the negotiation of a situation in which smart machines and human teams work side by side.
So, So Much Data
The secret is out; there is data literally everywhere. Thanks to the Internet of Things – or IoT – we now have the means by which to access and process this data. Cue headaches and major anxiety attacks from business owners and data analysts as leaky management systems groan under the pressure, but there are also massive rewards for those who manage to harness it.
Focus on Security
With more data, comes more danger. It is no surprise, then, that security is going to be a major focus for data managers and system designers in 2017. This year, expect to see artificial intelligence creeping into security too, as User and Entity Behavioral Analytics – or UEBA – becomes increasingly prevalent.
Augmented and Virtual Visual Partnership
There have been questions about the future of Big Data visualization. Is augmented reality the future, or is it virtual reality which holds the key? In 2017, the answer might simply be ‘both.’ Partnerships between augmented and virtual visualization look set to be a major fixture of both Big Data and data vis in 2017.
Democratization of Big Data
Big Data usage is not the exclusive club it once was. Instead, there are a wealth of tools that organizations of any size can put into action, should they want to harness its power. Big-Data-as-a-Service products featured heavily in the business plans of many organizations at the tail-end of 2016. Expect this to continue as we move into 2017.
What’s the point of having all of that data to play with if you can’t access it? Or, what if you can access it, but super, super slowly? Neither of these situations are going to make a data analyst or business owner jump for joy, so developers have been hard at work clearing the jam in the pipes and facilitating speedier data transfer. This is likely to lead to an impatient 2017, as analysts demand insight and data resources hot off the press in real time.
Diverse Data Sources
The rise of IoT – something we have discussed above – turned just about anything into a data source. This means that, rather than working with just a few metrics, analysts can broaden their scope with a diverse set of different datapoints and sources. There is no such thing as useless data, so don’t be surprised to see businesses leveraging every technique they can in the pursuit of more insight.
Bespoke Analytics Structures
It is no secret that a one-size-fits-all approach does not work for analytics. Businesses need data structures tailored to their specific needs, not some cumbersome arrangement built for another company entirely. It is surprising that it has taken us so long to deal with this issue, but bespoke analytics structures are going to be big in 2017 as organizations work to get the very best out of their data.
Unified Data, via The Cloud
The problem with having masses of data is that it tends to get itself in a bit of a mess. Fractured datasets, disparate storage solutions, and multiple heterogeneous data applications make managing large sets of data a veritable nightmare. Thank goodness, then, for cloud platforms, which provide convenient data unification solutions to organizations. Need to pool data from different locations and report on it ASAP? No problem; the Cloud has got your back.
Agility and Reusability as a Data Goal
Once upon a time, amassing large data lakes and then deriving insight from them was the endgame for Big Data analytics. Now, agility and reusability is increasingly highly prized, as business owners demand to be able to use their data, repeatedly, and in any application they desire. This year’s data management structures will need to be able to accommodate this demand.
This agility will be supported by in-memory analytics; an analytic function which enables systems to make rapid calculations and derive instant insight. 2017 looks to be a year in which technology can finally begin to meet the expectations of users in terms of both capability and speed, so expect businesses to be taking full advantage of this!
So, here we go once again! It’s going to be another exciting year in the world of Big Data.
Image via Pixabay