Today, technology is at the forefront of every conversation. Not a day goes by without a story questioning the rise of artificial intelligence, intelligent systems, big data or any other technological advances. Whether it’s self-driving cars, an artificially intelligent assistant or an AI chat bot named Tay, intelligent systems are growing in all industries.
While 2016 was a rather exciting time for technological developments, this year brings new trends and technologies that will become more systematically deployed in the enterprise.
But, for these systems – AI, intelligent systems and data analytics – to make an impact, businesses will need to shift their focus away from the technology itself and look to applying it to business issues. These systems, in turn, will help to combine our typical operational systems and predictive algorithms to better workplace functions.
So, with this new view on technology in the enterprise mainstream, what exactly can businesses expect?
Analytics Ops inspired by the renaissance in DevOps
The increase in the publicity of AI and machine learning will translate into new agenda items for many enterprises this year. To realize the value, placing these systems in production is required. While building production-ready analytics systems is difficult, recent advances of DevOps, where software applications are deployed, can provide insight.
Containerization, micro-services, collaboration and automation, versioning, rollback strategy, model management, performance monitoring, and auditability are all elements of production and are highly under-estimated for analytics practitioners; therefore, the struggle when we try to move into production. This year, we will begin to see more established vendors and start-ups streamline and perfect this process.
A Transition to the Edge
While the cloud is still the popular choice, we will see a greater emphasis on the new idea of ‘edge’ computing, one that is driven by the Industrial Internet of Things (IIoT).
Gartner forecasts by 2020, there will be 21 billion connected devices in a global IoT – talk about a load of data. Because of this, data transmission into the cloud will be inefficient as it requires a great deal of bandwidth and processing. Edge computing, on the other hand, enables businesses to use low-cost devices to run their analytics and algorithms at the network edge, making it much cheaper and more efficient.
Edge computing is a natural extension of the Analytics Ops trend, as both enterprise machines as well as algorithms need to be deployed into messy networks and a variety of devices on premise.
Natural language programming, one that uses regular sentences, has been vastly used for some time now. But this year enterprise notebooks like Jupyter, Zeppelin and RStudio, will rise in popularity within the scientific computing and analytics communities.
These online notebooks help to blend a mix of code and rich text styles. Like regular notebooks, they can be read by people, but they have an added benefit – the ability to be embedded as code. This special coding can be executed as a computer program and can perform multiple tasks, such as data analysis.
Enterprise notebooks have become the standard for top data scientists due to their mixed approach, and we will continue to see adoption of these through multiple industries as these platforms help to ensure both data scientists and non-technical contributors have a transparent view of the analytics. Since analytical problem solving has become more of a team sport, many businesses benefit from utilizing these platforms to mitigate the risks of situations where data scientists may leave the company and take their IP along with them.
The Maker Culture Shake Up
The Maker Culture can be described as a technology-enabled version of our DIY culture, made up of inventors who develop technology-driven solutions to everyday problems on an experimental scale. This year will call for this concept to be applied to analytics.
Big businesses are constantly seeking low-cost innovation strategies, and this year will be full of the potential to do so. Businesses who replace their traditional R&D processes that tend to be overly complex, rigid and expensive, with a maker approach, will be able to cut prices exponentially.
The main advantage to doing so is the ability to create cheap prototypes more quickly. For example, 3D printing. We are able to test and improve pictures in a short time frame, and either build them out into enterprise solutions, or discard them with minimal loss. In 2017, this idea will continue to develop and will become a much more mainstream approach.
Looking to the Future
There are a few industries already ahead of the curve when it comes to AI and intelligent systems. Among these successful industries are retailers that use intelligent systems for tasks such as visually analyzing store traffic to make predications on business decisions, to more immediate outcomes like customer preferences. Additionally, intelligent systems are starting to emerge in the automotive and transportation sector to flag any mechanical or electrical problems early, before they’re likely to break down.
But, what does all of this mean for the future of business? It means that we will soon be able to use intelligent systems to predict business problems and solutions, both simple and complex.
This is especially true when considering the ever-increasing diversity, complexity and interconnectedness of businesses and the problems these intelligent systems will be designed to address. AI or big data alone couldn’t grasp and adapt to a more technical future. This is why this year will mark the shakeup of traditional analytics approaches in the enterprises in terms of both creating new organizational structures and processes in the world of business – and beyond.
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