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Artificial Intelligence Applications: Revolutionizing Data Management

By   /  May 4, 2017  /  No Comments

The Smart Machine Age is upon us and is likely to disrupt many different human processes, tasks, and activities over the next 10 years and far longer as the key technologies continue to develop. The gradual availability of higher computing power at low costs, the explosive volumes of business data, and advances in Machine Learning and Deep Learning technologies data all signal an era of rapid automation of life around us. In the last decade, Data Management personnel solved business problems with data; in the next decade, highly capable machines using Artificial Intelligence Applications will solve problems with available data in a scale unheard before. Some of the by-products of this Smart Machine Age that we need to prepare for are Smart Workspace, Smart Data Discovery, Virtual Personal Assistants, Interactive User Interfaces, Cognitive Expert Advisors, and Smart Robots, to name just a few.

Stephen Hawking has warned that the full growth of the “artificial intellect” could spell the doomsday for humanity. On the other end of the spectrum, many optimists believe that with full preparation, human society can look forward to a highly fruitful era of alliance between the human and the machine. The forthcoming “digital gold rush” surrounds the “how” of data technology, not just the “what.” Currently, technology product differentiation comes from the usefulness of algorithms. Thus, the algorithm economy provides a great opportunity to all technology providers – signaling the generation of specialized technology start-ups. Everyone wants to join the discussion about “software that thinks.”


Artificial Intelligence Applications are Transforming the Workplace

Four Fundamentals of Workplace Automation predicted that workplace automation can end up saving about “2 trillion in annual wages.” Contrary to popular belief, roles and tasks handled by very senior level people such as Finance Managers, Line of Business Managers, various members of the C-suite, or even CEOs can be automated to a large degree. Automated business processes have been tested to validate higher yield, better quality, more reliability, and lower costs. Over the last couple of years, much of these forecasts have proved to be true, generating fresh concerns relating to job loss and economic instability.

Is Deep Learning Here to Stay?

Although Deep Learning is still in a nascent stage as far as Advanced Analytics is concerned, it has recently gained a lot of attention from the smart software development community because of its wide application areas. As the algorithm economy continues to gain momentum among global businesses, the challenges facing Deep Learning are still real. Big Data going mainstream may successfully help combat the Data Management issues making Big Data and Deep Learning the formidable combination for unlocking any complex data handling problem. Read Deep Learning and Machine Learning Differences: Recent Views in an Ongoing Debate and A Brief History of Deep Learning for more details.

Internet of Things (IoT) has Boosted the Algorithm Economy

Although Data Quality controls the scale of Data Analytics to a large degree, finally it is the algorithm that will create a differentiation between competing businesses. The article titled The Internet of Things Will Give Rise to the Algorithm Economy proclaims that like Google, successful enterprises need to bind their data, smart device, software, and other IT assets together to deliver solutions that connect offers with new offers creating a unique marketplace for buying and selling algorithms. The Challenges of Artificial Intelligence professes that along with smart algorithms, businesses need proprietary data or access to vast amounts of data. Without data rights, algorithms cannot win in complex business scenarios. The implicit point is that Data Management will be driven by algorithms and Artificial Intelligence Applications far into the future.


Use of Artificial Intelligence Applications in the Healthcare Industry

The Medical Futurist article titled Artificial Intelligence Will Redesign Healthcare indicates there are at least ten ways that smart machines will impact the existing healthcare processes. Some of these processes are already in use such as telehealth service, medical data upload service, assisted nursing, videoconferencing etc.

As smart phones and sensors surround the human population, patients will be able to avail instant, high quality healthcare services directly from their smart phones. In many cases, the patient care platform may be powered by robots providing preliminary medical advice. All healthcare records will be centrally available to connected medical facilities, thus making it easy for both the patient and the doctor to access and consult such records whenever necessary from remote locations. Even nursing duties may be taken over by robots. Responsive medicine will usher in a new trend where health facilities like clinics, nursing, homes, or hospitals will be equipped with multiple advanced,

AI-powered diagnostic and monitoring equipment with screens. These equipment will continuously monitor patient condition and provide responsive feedback based on need. Instructions from doctors will directly enter the EHR systems, which the patient will be given access to. Artificial Intelligence Applications will assist medical practitioners to compare and contrast patient conditions for arriving at the best possible treatment solution.

Although some progress has been made in these lines, much of the existing progress is piece meal requiring more convergent practices. Moreover, many data-technology related regulatory and financial challenges are currently facing the US healthcare industry as a whole. In The Most Exciting Medical Technologies of 2017 suggests that digital technology will soon deliver data-powered cancer care systems. The medical insurance market will also go through an upheaval due to the availability of improved healthcare records through wearable devices and digital trackers.


Use of Artificial Intelligence Applications in the Construction Industry

The article titled How Artificial Intelligence Could Revolutionize Construction explains how AI solutions have resurrected the construction industry from traditional procedural red tapes delaying important decisions and fast actions. The $4 trillion industry labor is overburdened with controls. All projects ranging from business complexes to transport systems require a well-orchestrated plans and processes involving people, materials, and equipment.

Currently, AI can predict injuries, provides better human-machine interfaces for construction equipment, but cannot prevent human errors and accidents. If many construction workers could be replaced by intelligent robots to lay bricks, crush stones, or handle demolition work, then the global construction industry would be supercharged with “efficient but bloodless” workers. Many of these experiments have been tried in isolated projects, but mass use of smart construction workers and smart machines in construction is still a distant dream.


Use of Artificial Intelligence Applications in Human Resources

Artificial Intelligence Will Revolutionize Human Resources suggests that the most common AI trends in HR include talent analytics, compliance, and risk avoidance. With 75 percent of the workplace being populated by millennials by 2020, enterprise HR Departments will leverage Big Data and AI-powered Analytics to execute all major functions from talent acquisition to policy disbursement. In 5 Emerging Technologies that Every Office Will Have in 2020, the author claims that high-speed net connections and advanced mobile networks will make Remote Robot Workers a reality.

Use of Artificial Intelligence Applications in Industrial Operations

How Artificial Intelligence is Revolutionizing IT Operation Analytics companies are already leveraging AI-powered Operations Analytics to optimize real-time business operations with “unprecedented granularity, preciseness, and impact.” According to this article, industrial Data Management is no longer the fashionable pastime of a few trained scientists, but the active preoccupation of mainstream operations staff. In the current technology climate, speed of Data Analytics has lower priority than accuracy. In 2017, a lot of operations outfit may disappear if they do not learn to employ intelligent Data Management systems for optimal performance.
Photo Credit: Khakimullin Aleksandr/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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