Six Big Data megatrends for 2016

Six Big Data megatrends for 2016

Big Data, one of the major drivers of the digital transformation, has the objective not only to optimise existing business processes, but also to continuously “re-invent itself” – ideal prerequisites for thriving in a world of constantly changing conditions.

Big Data is no longer just a marketing department slogan, and is increasingly becoming a more vital and integral part of current IT strategies. No wonder, because the pioneering technology helps companies to link scatteredcorporate data and information and to use it intelligently and profitably.

The increasing inter-connectedness of things makes big data even more significant: it sets up the conditions for the development of entirely new business ideas. Here we’ve put together for you a summary of the most important trends that will dominate the market in 2016.

  1. Intelligent systems based on machine learning

The development of intelligent self-learning systems – with machine learning and deep learning included under this heading – is progressing in leaps and bounds. When it comes to understanding information and recognising semantic contexts inside vast piles of data, machine learning algorithms demonstrate their clear advantage.

The issue of security is a big driver of machine learning. A good example of this is the building security sector. Together with the Technical University of Munich, Munich Allianz Insurance has developed a system based on big data and intelligent analysis that links via sensors countless items in a living space or office. This system learns to recognise normal behaviour and can differentiate between a burglary and other unusual, but non-critical incidents. Thus, false alarms are the rare exception.

Following the same principle, credit card company are attempting to make online shopping safer. In 2012, US citizens paid a total of 26 billion US dollars by credit card; the estimated damage caused by unauthorised transactions amounted to a whopping six billion dollars. Using machine learning, the systems that monitor all transactions around the clock can distinguish between normal and criminal patterns and implement precautions virtually in real time. In addition, these systems get better day by day.

Machine learning has a strong presence not just in the security sector, it also offers huge benefits to areas in which “intellectual assembly line work” turn everyday business into dull drudgery. A good example of this is the classification of incoming mail (i.e. office mail distribution). This is often still done manually, but thanks to semantic content analysis, this job will increasingly be automated. The same applies to the daily flood of e-mails, which will be prioritised on the basis of user behaviour.

  1. Business models are changing

The so-called digital transformation, which is being fueled among other things by big data and by increasing competition, requires businesses not only to optimise their business processes through digitisation, but are also the impetus for developing new business models.

Hagleitner, a mid-sized manufacturer of sanitation equipment, didn’t throw in the towel in the face of ever-cheaper competitors. Instead, it transformed its products into intelligent devices by creating soap dispensers with integrated sensors – under the banner of buzzwords like Internet of Things (IoT) or industry 4.0. This provides two distinct advantages. Firstly, process optimisation – the Salzburg company is always informed about the current levels and can provide its customers needs-based and automated replenishment.

Secondly, using the data that comes in and is analysed every second, a company that was once simply a hardware manufacturer has developed into a hygiene specialist that, particularly in the sensitive healthcare area, can make good use of its cutting-edge and innovative ideas.

New business models like that of Hagleitner often come about when data which is already available en masse anyway gets put into a new context and linked in an innovative way. This trend, which is already strong in the US, will continue to gain a foothold in Europe in the year 2016.

Intelligent tools are changing the rules of the game on the market. Today the battle is no longer “big vs. small” but “fast vs. slow”.  A company which sleeps through the digital transformation – no matter how large that company may be – can be beaten by a startup company that is committed to innovation.

  1. Predictive analytics: A game changer

Business processes are in flux: predictive analytics – the ability to create reliable forecasts on the basis of available data – is gaining wide use, with increasingly highly specialised objectives and in well-known areas of activity.

A typical example of this is the area of predictive maintenance. Let’s say, for instance, a locomotive experiences a breakdown.  The resulting losses and costs can be enormous, not to mention the additional high cost of the replacement parts. For these reasons, the German Rail has equipped its locomotives with sensors that continuously provide data. Analysts derive patterns from this information, such as determining certain behaviours or changes in the sensors that typically occur before a defect arises. These indicate damages and error patterns, which can be caught and dealt with proactively. The rail company benefits from a real-time maintenance system that can predict damage to the locomotives, and saves both cancellation losses and the costs of expensive spare parts.

Companies have used business intelligence tools to generate forecasts for quite a while now. However, the forecast creation was heavily dependent on structured data. Since big data analytics is capable of evaluating unstructured data (such as information from sensors or documents that are written in natural language), traditional tools will retreat to the background in the coming year.

  1. Lightweight data integration

Real knowledge in a business environment is only present if the information that is distributed within the company is linked and made available to all employees when they need it. The greatest obstacle to achieving total corporate intelligence now turns out to be organisational and technical “silos” in which crucial information is hoarded. For this reason, systems such as enterprise search (a special kind of big data), which manage to intelligently link data and information on all departmental and application levels, are becoming increasingly important. Another advantage is that the data remains right where it was created, so that it’s not necessary to turn the company or organisation’s entire IT landscape upside down. This type of lightweight data integration is becoming the standard, and will be brought to bear mainly in enterprise portals.

  1. Data visualisation and a 360-degree view

Exploratory navigation through information and its visualisation are one of the top priorities for end-users interested in accessing and using knowledge that directly supports their daily work. The tools are increasingly easier to use and are also accessible to non-specialists and their needs. Users are less dependent on the IT department to get their work done, since the solutions can be adapted without IT personnel – this is self-service IT.

Clever data visualisation helps employees capture and comprehend even complex subjects faster than ever before, or become better acquainted with their customers. Enterprise search transforms a plethora of very different data into a 360-degree view of people and topics, from which clearly-defined action plans in terms of sales, servicing or problem-solving can be derived.

  1. Big Data conquers new arenas

Big data applications are moving into new industries. In 2015, one of the top winners was the health care sector. Applications for big data range from research and diagnosis to intelligent distribution of resources and management. Add to that trends such as health tracking – see Apple Watch and other wearables – that provide data in the service of good health.

Another industry that will increasingly benefit from big data is the manufacturing industry. So-called smart factories, in which all components are cross-linked, can communicate with each other and use the available resources optimally, i.e. cost-effectively, in a highly automated way, maneuver a delicate balancing act that up until very recently seemed impossible: the ability to adapt to individual customer demands and the automation and affordability of mass production.

In addition to the healthcare sector and manufacturing industry, big data applications are perfect for those industries in which a lot of data already exists or where large volumes of data are expected to be generated through the integration of Industry 4.0/IoT solutions. In other words: the triumph of big data is expected to continue in the year 2016.


Daniel Fallmann, founder and CEO of Mindbreeze

Image source: Shutterstock/McIek

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