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5 Key Big Data Trends to Watch

Posted by John Mack on Tue, Feb 23, 2016
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Trends.jpgAs a company that works intensively with Fortune 500 companies, our finger is firmly on the pulse of the latest needs, wants, and aspirations of the world’s biggest Big Data drivers. Here are five key trends we’re seeing for 2016 and beyond.

(1) Man + Machine: Better Together
The topic of artificial intelligence (AI) has been with us for many decades now, both in scientific circles and popular culture and often features hints of a dark, dystopian future if mankind allows AI technologies to evolve without tight control. The advent and rapid technical advancement of Big Data analytics in recent years has spawned a similarly rapid evolution of machine learning (ML) technologies, which both complement and enable AI. In 2016, we will begin to see more evidence that AI and ML are becoming increasingly robust technologies, are performing valuable tasks with greater capacity and quality than man could achieve alone, and are enabling a symbiotic skills specialization that redefines our understanding of Man + Machine synergy at a level never previously possible.

(2) IoT: The Underpinnings of Sentient Infrastructure
The core concept that defines the Internet of Things (IoT) is that of connected infrastructure, which consists of objects networked to one other and to the Internet (or a private intranet), operating as an integrated and coordinated entity. The IoT already exists in a nascent state, both in enterprise and consumer contexts, and is evolving in terms of sophistication, market penetration, and criticality of tasks performed. In 2016, we will begin to see AI integrated into the IoT on a large scale, providing the early foundation for an evolutionary path toward truly sentient networks and infrastructure.

(3) Personalized Analytics: Optimizing Individual Experiences
Mass customization was originally a concept associated with manufacturing consumer products tailored to the requirements or whims of the individual and has evolved to include personalized marketing, customer service, and a host of service offerings for consumers, businesspeople, and business entities. Digitization of personal and business activities, aided by devices and software that track both our active and passive actions, now generates a massive trail of Big Data that reflects these activities and facilitates prediction of future events. In 2016, the experimentation and rudimentary efforts to analyze these activities will begin to yield to increasingly sophisticated personalized analytics, which will offer more sophisticated and accurate predictive and prescriptive guidance that optimizes the individual experience in the workplace, at home, and in between.

(4) Data Democratization Extends to ML and AI
The much publicized shortage of data scientists has constrained widespread adoption of Big Data analytics and its use at scale in organizations. Even the surging popularity of university programs and online coursework in data science seems unlikely to satiate market demand any time soon. A variety of factors (including, but not limited to, simplified application and platform architecture, simplified user interfaces, packaging of algorithms, and distilling and reuse of validated insights from algorithms) has helped overcome the shortage of data science talent and diffuse Big Data analytics within organizations, democratizing it across non-technical users and broadening adoption. In 2016, we can expect to see the extension of this trend to ML and AI, as the complexity of these analytical techniques and associated technologies becomes abstracted and integrated into Big Data analytics apps, accelerating insights, enabling new types of products and services, and delivering unprecedented levels of value.

(5) Trouble in Unicornstan: Culling of the Herd in Big Data Analytics
With the US Federal Reserve Bank raising interest rates in December 2015, the long period of stable and low rates that had prevailed since June 2006 came to an abrupt, if extensively telegraphed, end. The long period of historically low interest rates, coupled with the advent of multiple “core” technologies, created an “easy money” environment that encouraged venture capital investment across multiple technology segments, including Big Data analytics, and also led to mass experimentation and duplication. In 2016, the Big Data competitive landscape will consolidate substantially, driving out many companies with undifferentiated business models, weak value propositions, point solutions which don’t provide holistic technology approaches, and solutions that don’t simplify the user experience for non-technical business practitioners.

Conclusion
How can organizations effectively navigate the aforementioned trends as they play out in 2016 and beyond? The first step is to maintain situational awareness of how these trends are evolving, since assigning precise timing to the evolution of trends is usually not practical and since the momentary stage of any trend depends in great part on the vantage point of the observer. Which sequential milestones are observable? Which dominos are falling? What evidence do you need to see to confirm what you think you are seeing?

The second step is to objectively assess your organization’s Big Data analytics strategy and capabilities in terms of both business and technology dimensions. Have you procured a variety of Big Data analytics solutions that purport to do the same or very similar things? How extensively is your organization using the analytics solutions that you have, both within and beyond the data science team? How scalable is your advanced analytics capability, and is it truly integrated with your routine business operations?

Third, embrace and adopt a long-term strategy for advanced analytics, both in terms of business parameters and technology enablers. Seek platform-centric solutions that offer distinct and valuable capabilities, such as the ability to support scalable analytics, integration of advanced analytics into routine business operations, and machine learning that constantly tests and refreshes predictive algorithms.

There is no single “magic bullet” action that guarantees success with advanced analytics or that guarantees immunity from adverse market and technology trends. Still, taking this simple and methodical approach will help identify opportunities and mitigate risks associated with Big Data analytics in your organization.

See the numbers behind these Big Data trends — and more — with our latest infographic.

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John Mack is the Executive Vice President of Marketing at Opera Solutions.

 

 

 

 

 

 

 

Topics: Big Data, Machine Learning