|By Puneet Pandit||
|January 17, 2014 02:00 PM EST||
Happy New Year - and what a year it's been.
With the technology advancements of phones, tablets, glasses, clothing and machines over the last year, there are volumes of unstructured and multi-structured data collected from machines and connected devices, and it's growing at a dizzying pace. The Internet of Things is quickly turning into the Internet of Everything - in today's always-on, always-connected world, and machine data is everywhere.
If 2013 could be classified as the year of the Internet of Things, what will 2014 bring? Looking ahead even further, IDC has estimated that in 2020 there will be 26 times more connected things than people. Wikibon issued a forecast of $514 billion to be spent on the "Industrial Internet" in 2020, and Gartner estimates 20 billion connected devices by 2020, a 30-fold increase from today.
So what's next? How to handle the rise of the machine? Here are our predictions for related trends that will take shape in 2014:
1. Analytics around the Internet of Things will become mainstream.
Just as we saw with the introduction of Google Glass, Fitbit and Samsung's Galaxy Gear, "things" will be even more connected in 2014, and enterprises will want to make sense of the data they generate. Emphasis on tools and solutions to analyze the data from these connected devices will become a key imperative this year as enterprises need to learn how to summarize and interpret the data, identify patterns and relationships and make intelligent predictions.
2. Log analysis will shift from operational intelligence to business intelligence.
2013 was a year of technical solutions around log analysis focused on IT admin users. However, with the explosion in data from connected devices, the needs of the business user will come to the forefront. Log analysis will go beyond troubleshooting to serve business users who require better ways of analyzing and making sense of this data, while enabling them to uncover new market and revenue opportunities in the process. Operational intelligence is a start, but the Internet of Things generates machine data extremely complex in variety, volume and velocity. The value of log analysis will naturally shift to the business user in the year ahead.
3. Mining multi-structured data will become a key technology differentiator for the Internet of Everything.
While solutions exist today for standard data sets, non-standard data generated from machines, devices and software applications will demand a more sophisticated approach for parsing and analysis. In 2014, homegrown solutions will give way to sophisticated commercial solutions, as companies seek to handle the increasing volume, variety and complexity of their data. The ability to handle multi-structured data will become a key technology priority as companies realize the high cost of designing, building and maintaining an in-house solution. In the classic build vs. buy scenario, companies will increasingly seek out the most comprehensive commercial solutions designed for the new age of multi-structured machine data.
4. In 2014, we move beyond Hadoop and the elephant in the room.
As the world looks for better Big Data solutions, companies will start to realize that while Hadoop is suitable for some Big Data use cases, it is not one-size-fits-all. In 2014, many vendors and solutions will emerge to fill the existing gaps - ranging from SQL on Big Data to pre-defined analytics apps on any Big Data platform. In 2014, Big Data will start to outgrow Hadoop.
It's an exciting time to be in this industry...
The market will only continue to heat up in 2014, with new requirements created by the Internet of Everything and the need for more powerful machine data analytics.
Companies can harness IoT and predictive analytics to sustain business continuity; predict and manage site performance during emergencies; minimize expensive reactive maintenance; and forecast equipment and maintenance budgets and expenditures. Providing cost-effective, uninterrupted service is challenging, particularly for organizations with geographically dispersed operations.
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