6 Machine Learning, AI, Analytics Trends To Watch
Big data and analytics are set for a big 2016, as more devices and types of software are connected and exchange information. Here are some considerations for businesses as they look to maximize the value of all this data.
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Artificial intelligence (AI) and machine learning capabilities will be included in more types of platforms and software, allowing business and IT professionals to take advantage of them without understanding how they work. Of course, developers and data scientists will help enable this integration with the assistance of Watson APIs, Microsoft Azure machine learning APIs, Amazon Machine Learning service, and open source projects including the TensorFlow machine learning library contributed by Google.
Also expect to see more amply funded startups in this area. As an example, H2o.ai, which offers an open source platform for data scientists and developers, just received $20 million in Series B funding. Venture Scanner, which helps companies understand startup ecosystems, is currently tracking more than 897 AI companies in 66 countries that represent a total of $3.98 billion in funding.
Gartner advises organizations to consider how they can use advanced machine learning for competitive advantage in 2016.
Those who have a better understanding of statistics than their counterparts are better prepared to deal with data-related uncertainty, and that's a good thing. Confidence levels and margins of error allow decision makers to make reasoned decisions from among several possibilities. If your tendency is to rely on a single number as if it represented an absolute, indisputable truth, it's probably time to start exercising your critical thinking skills.
Not everyone in the organization needs to become (or even has the aptitude to become) a statistician. However, universities are helping graduate, undergraduate, and executive students become savvy about the practical application of data. As part of that, they are being taught to think critically about and question what the data says rather than accepting what the data says at face value.
Many organizations continue to struggle with data governance because getting it right isn't easy. According to a recent survey by global consulting firm Proviti, one in three companies still lacks policies for information security, data encryption, and data classifications. However, given the strategic role of data and the security and privacy risks associated with it, organizations must place greater emphasis on data governance. As Michael Locke, VP and principal analyst, analytics and business intelligence, at Aberdeen, recently noted in a knowledge brief, "As a vital part of data sharing, companies also need to ensure the proper security and use of information."
The Internet of Things adds contextual awareness to everything from the weather conditions affecting industrial equipment to how individual consumers shop. CSC calls 2016 "the year of contextualized data" because context, as enhanced by the IoT, enables companies to provide more relevant experiences to their constituents. However, getting context right isn't easy. As contextual awareness becomes the new norm -- in retail, in cars, online, and in other everyday experiences -- consumers will begin to expect the same level of relevance across channels, devices, and experiences. However, in 2016 the challenge will continue to be accurately interpreting context..
More devices yield more types of personal information. In the race to innovate, some IoT device manufacturers have failed to build in the level of security that consumers expect. For example, in 2015, several parents were outraged when their baby monitors were hacked. Meanwhile, the owners of certain cars were urged to get a security update following the Jeep Cherokee hack.
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The FTC is concerned and is urging device manufacturers to build security into products that connect to the IoT.
A handful of new, and perhaps even shocking, high-profile breaches will likely make headlines in 2016. IoT device security will become a brand differentiator for some manufacturers, the seeds of which may begin to spout more obviously in 2016.
More software products are including some sort of analytics as a feature. Many of these products are interconnecting with one another via APIs so they can import, export, or share data as necessary to deliver a business or personal benefit. For example, application performance monitoring (APM) solutions help DevOps teams ensure their software delivers a great user experience, despite the increasing complexity of the software and the environment in which it runs. However, some APM solutions are also connecting to social networks and enterprise software so organizations (and more specifically, lines of business) can better understand the business value and business impact of their software. In short, disparate datasets formerly used for specific role-based purposes will continue to be combined in new ways to yield new insights, whether the goal is to better understand Alzheimer's disease or to become more efficient.
2015 was a strong one for big data and analytics, and the trend will continue in 2016 with a few twists. Like 2015, organizations will continue to scramble for talent while universities adjust their curriculums to help produce that talent. Also expect to see more hardware and software innovations that change the ways in which organizations leverage data.
On the hardware side, we'll see more Internet of Things (IoT) devices providing added levels of intelligence across industries. On the software side, we'll see more sophisticated, yet easier-to-use, analytics platforms and solutions designed to fuel business competitiveness.
The ability to leverage even more information will enable organizations to optimize more efficiencies in the value chain as well as between those organizations and their various constituents. With the added insights will come security and privacy concerns that must be addressed.
As we saw in 2015, our ability to innovate often outpaces our ability to govern the responsible use of those innovations, which is attracting the scrutiny of the Federal Trade Commission and the White House. The Internet Society also released a white paper that discusses the issues and challenges that result from a more connected world that includes the IoT.
[ Here's how to use Twitter to follow the big data and analytics thought leaders. ]
Despite all the technological progress that's enabled big data and is enabling the "Internet of Everything," what we call "advanced analytics" today is likely rudimentary by future standards. We are still in the early stages of machine-aided intelligence as evidenced by everyday situations, including poorly targeted marketing campaigns, irrelevant loyalty program offers, and off-base product recommendations -- even from industry leaders who continue to push the envelope of what's possible.
Such glitches, though mildly annoying, translate to lost revenue, which means there's still a lot of room for innovation. Venture capitalists know that and are making aggressive investments in startups focused on artificial intelligence, machine learning, and big data.
As organizations strive to become more data-driven in 2016, they will need to carefully balance people, processes, and technologies. Here's what to watch for as 2016 unfolds.
2015 was a strong one for big data and analytics, and the trend will continue in 2016 with a few twists. Like 2015, organizations will continue to scramble for talent while universities adjust their curriculums to help produce that talent. Also expect to see more hardware and software innovations that change the ways in which organizations leverage data.
On the hardware side, we'll see more Internet of Things (IoT) devices providing added levels of intelligence across industries. On the software side, we'll see more sophisticated, yet easier-to-use, analytics platforms and solutions designed to fuel business competitiveness.
The ability to leverage even more information will enable organizations to optimize more efficiencies in the value chain as well as between those organizations and their various constituents. With the added insights will come security and privacy concerns that must be addressed.
As we saw in 2015, our ability to innovate often outpaces our ability to govern the responsible use of those innovations, which is attracting the scrutiny of the Federal Trade Commission and the White House. The Internet Society also released a white paper that discusses the issues and challenges that result from a more connected world that includes the IoT.
[ Here's how to use Twitter to follow the big data and analytics thought leaders. ]
Despite all the technological progress that's enabled big data and is enabling the "Internet of Everything," what we call "advanced analytics" today is likely rudimentary by future standards. We are still in the early stages of machine-aided intelligence as evidenced by everyday situations, including poorly targeted marketing campaigns, irrelevant loyalty program offers, and off-base product recommendations -- even from industry leaders who continue to push the envelope of what's possible.
Such glitches, though mildly annoying, translate to lost revenue, which means there's still a lot of room for innovation. Venture capitalists know that and are making aggressive investments in startups focused on artificial intelligence, machine learning, and big data.
As organizations strive to become more data-driven in 2016, they will need to carefully balance people, processes, and technologies. Here's what to watch for as 2016 unfolds.
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