Gaining data, interpreting that data, and doing all of this in real time – that’s the objective for data analytics, that’s the end game. Achieving this has proved difficult from day one, and has actually become something of an anomaly in the science of business, in that it has actually become harder to achieve as technology and understanding have developed, not easier.
A significant portion of the blame for this must land squarely at the door of the Internet of Things – or IoT. The growing connectivity of ‘stuff’ in our life has given us an almost unlimited amount of data points from which to draw insight and understanding. Where data scientists would have perhaps deployed a few sensors to collect information on several axis; they now have a wealth of objects at their disposal, all of which are pumping out data on a constant basis.
The toaster, the kettle, your trainers, your windows, your car’s wheels, your household lighting system – all of these previously inert objects are now potential data centers. We are living in the age of the smart toaster.
In theory, unlimited data points means unlimited data. In practice, the processing power required to achieve this proved, initially, unfeasible. What’s more, the volume of data is expanding every year – in fact, more data was created between 2013 and 2015 than in the entirety of human history up until that point. This means that, in order to future proof data interpretation infrastructure and software, it needs to be very, very powerful indeed.
Now, it seems we are catching up. Big names in the field of data, including Cisco Systems and SAP, as well as a few more surprising ones, are devising innovative, exciting ways to deal with this growing volume and to provide genuine insights in real time.
These innovators are applying machine learning and other developing tech concepts to the interpretation of data, both in the form of discrete batches of data with starting and end points, and in the form of indefinite streams of data. The result is a far more efficient and effective way of interpreting the data at hand, and delivers businesses a much broader scope in terms of what they can and cannot process.
What Does this Mean for the Data Industry?
So, to paraphrase the narrator from ‘70s sci-fi smash hit The Six Million Dollar Man, “we have the technology.” But what happens next? What can we do with this technology? What changes can we expect to see in the industry going forwards?
Below are a few of the developments we can expect to witness, as real time data streaming analytics becomes increasingly accessible to the masses who crave data and understanding.
New Players Emerging
Whenever there is a fluctuation or movement in the field of data analysis, a disruption is caused. This disruption forms cracks deep in the fabric of data, giving smaller organisations the chance to make serious in-roads into the market.
We can expect this to happen with the emergence of real time data streaming analytics. Innovative start-ups like DataTorrent have already started to shake things up. DataTorrent launched their unique open source platform for real time streaming and processing two years ago, garnering jealous looks from far more established names in their field, and pushing the science of data forward overnight.
Elsewhere, organizations are seeking to give smaller businesses the data capability they need. Blaze have weighed in with StreamLab, an intuitive and easy to use development platform for small and medium sized business.
Powerful Risk Analysis
Risk analysis has always been a key area of development for data-driven companies, particularly in areas such as finance, or with organizations who lack the resources to return from a major setback. Real time data streaming analytics makes it far easier for a business to recognize the risks that they face, to gain warnings ahead of time, avoiding any potential catastrophes which may be floating on the horizon.
However, this could put customers at a disadvantage. As real time factors are taken into account during risk assessment processes and decision making, customers and clients may feel that their actions or circumstances are being unfairly judged, without proper consideration. As real time analysis and interpretation becomes more prevalent, we can expect these teething troubles to be smoothed out.
With so much data flying around, and such a high potential cost in the case of a data breach, security must become a top priority. This is already well underway, thanks, in part, to the furor surrounding foreign political intervention into household objects in the United States – intrusions that have yet to be proven or disproven.
While such fanciful accusations are probably best filed away in the drawer labelled ‘paranoid fantasy,’ this does not mean that security is any less of a big deal. Expect to see security beefed up and bolstered across the industry before the end of the year.
Real time information gained from the IoT adds a whole extra dimension to data understanding. Increasingly, data is used to observe behaviour – whether that be customer behavior, or competitor behavior, or even the ‘behavior’ of a computer system. This opens the doors to qualitative analysis, rather than restricting ourselves to the quantitative. Add machine learning and AI to this equation and you have yourself a very heady concoction indeed, with data interpretation systems questioning not only “what, who and how many”, but also “why?”.
A New World of Visualization
And what do we do with the vast amounts of data that is streamed each and every day? We use it to construct compelling narratives, of course; narratives which unfold in real time and deliver direct insight to users. Data visualization will be at the forefront of real time streaming analytics, and this will be the tool which enables mass understanding and interpretation. In other words, DV will be the key that unlocks streaming analytics.