There’s no escaping it: in today’s world, data is the foundation of every modern business. Whether it is a huge tech conglomerate or a small independent shop, every organisation creates and collates reams of data every day, if not every second. 

With so much data being generated, the key to success is an organisation being able to utilise this data to the best of its ability.

With new cloud-based data management systems accelerating innovation, areas like edge computing and edge data analytics are becoming more prolific – helping companies achieve faster insights, improved efficiency, better security, and generally pushing the boundaries of what the cloud made possible.

Utilising the cloud

The cloud has undoubtedly been the single biggest enabler for data analytics innovation in the modern day, with the majority of organisations leaning on cloud computing as a key component of their digital transformation journey.

For many, leveraging the cloud means using it to help manage the reams of data they are collating and creating, but this approach has its pitfalls. There’s often a common misunderstanding that moving to the cloud means moving all data to the cloud – but, depending on your individual business requirements, this is not always needed.

Challenges around transferring data and the speed at which this happens means that organisations must consider what is absolutely necessary to move to the cloud for them, to avoid any errors, confusion, or compromises in quality. And although it’s quicker and more efficient at data transfer than the internal data centres of days gone by, for many organisations a remote infrastructure run on the cloud doesn’t cut it anymore.

Technology on the edge

For some businesses, the remote infrastructure of the cloud is simply not fast enough in transferring data from point A to point B. This is where edge computing and edge data analytics come into play.

And although it may sound very much like just another buzzword, those in the know understand that the word ‘edge’ is actually incredibly helpful in understanding this technology, as it refers to a literal geographic location. Edge computing is computing that is done near or at the data source – it is literally on the edge.

My favourite analogy for this is – if using the standard cloud is the equivalent of sitting in a restaurant while the chef cooks your meal in the kitchen, using edge computing is the equivalent of sitting at the chef’s table. You’re not fully in the action but you’re on the periphery, as close to it as possible.

With nearly every modern business having the ambition to be ‘data-led’, edge computing is offering a new way to process vast amounts of data in real-time without the lag experienced with the cloud.

To stick with my earlier analogy, edge computing cuts out the waiter; your food goes straight from the chef to you in as short-a-distance as possible and it’s this speed that is leading to huge technological advancements.

Innovations powered by edge 

Let’s use Tesla as an example. The car company is re-writing the rule book when it comes to what’s possible in automobiles, thanks to the use of edge computing and data processing.

With each car collecting vast amounts of data in real-time, the most efficient way to analyse and action the information is within the vehicle, at the point of collection. While using a remote cloud would only take fractions of a second longer than using edge computing, removing this lag is crucial in the advancement of technologies like automatic braking – those milliseconds can be the difference between an autonomous car colliding with a pedestrian or not.

It’s real-time analytics at the edge like this, that is helping to take ideas like autonomous cars out of science fiction and into reality.

The benefits

Using edge computing and edge analytics has allowed organisations to let the data model lead the process, rather than analysing the data in order to build the model, and this switch in thinking brings with it many benefits.

Having the information processing happening on the edge, where data is created and consumed, can bring huge efficiency gains, improve security, and accelerate data flows to achieve true real-time data analytics. 

It’s an important development for the financial sector, for example, where a delay in data transfer of just milliseconds can have a great, and costly, impact on trading algorithms. Speedy data transfer is also paramount in areas like healthcare, where data delays can impact life-or-death decisions.

On top of the benefit of speed that edge computing and data analytics offer, they also have the benefit of protecting systems from malfunction and hacks due to the fact that they decentralise the data process.

If all autonomous cars, for example, were hooked up to a centralised system it would be a very real concern that someone could hack that system and gain control of every car at once. Using edge computing, however, keeps the data localised so it’s less vulnerable to a malfunction – intentional or not – as the issues are localised to that single car, not the entire network.

Edge in the mainstream

The key to being able to utilise edge computing and advanced data analytics as they filter down into the mainstream is having the right data engineering in place. This allows businesses to ensure that the data they collate, process, and consume, is of the right quality and in the right format for teams to use.

Implementing DataOps can help with this as it can improve how data is processed and allow data science teams to begin building automation models to do a lot of the heavy lifting. 

Ultimately, edge computing, data analytics, and cloud innovations are of no use if the data they rely on is not in a usable format to begin with.

If mainstream businesses want to see the benefits of better insights, true ‘real-time’ data and consequently the better informed and more agile decision-making that comes with technological advancements like edge computing, then they can’t skip the hygiene factor.

Any investment in technology should align with a long-term business strategy or it will only lead to problems down the line. For example, we are now seeing a lot of companies reduce the data they have stored on the cloud, as the initial knee-jerk reaction to achieve digital transformation by putting everything on the cloud has proved inefficient and costly.

Some businesses and industries can benefit greatly from edge computing, but only if they plan and prepare for success.