The expansion of the Internet of Things (IoT) has added innumerable new sources of Big Data into the Data Management landscape and will be one of the major Big Data Trends in 2018 and beyond. Laptops, smart phones, sensors on machines, all generate huge amounts of data for the IoT.
Organizations that are flexible enough to manage and transform the data into useful Business Intelligence, this represents a significant opportunity to gain a competitive advantage (or remain competitive). As Big Data grows, businesses attempt to keep up with it, and struggle to turn the data into usable insights. Business Intelligence is key to staying competitive, and Data Analytics provides the up-to-date information needed.
In 2017, some companies expanded their services and software which translated Big Data into visualizations and graphs. This allowed researchers to gather and coordinate information about the general population more efficiently, and improve the customer experience. It also allows leaders to streamline the decision-making process.
The number of companies offering Cloud services will also continue to expand in 2018, resulting in competitive pricing, and allowing smaller businesses to access Big Data resources.
Business Intelligence in 2018
Organizational decision-making is currently undergoing a shift which will continue into 2018. In 2017, the goal of processing Big Data promoted ever-increasing efficiency and steadily decreasing costs. In turn, this has made the use of Business Intelligence, based on Big Data, more important to small and medium-sized businesses, and even start-ups. This trend will continue into 2018, and beyond, with the cost of processing Big Data continuing to drop. Expect the following:
- Use of Business Intelligence from the Cloud will increase.
- Analytics will provide improved data visualization models and self-service software.
- Decisions regarding expansion into new markets and geographies will be based on Big Data.
Cloud Trends in 2018
- Creating a Niche
In 2018, as more people become familiar with Cloud applications, specialization and niche work will evolve, just as it has in every other industry. This, in turn, will create additional research options and more competition within the industry. Data Scientists with a specialization, such as retail sales, or regional growth, will gradually become the norm.
- Hybrid Clouds
While the Cloud provides a convenient solution for storage, and processing Big Data, few are comfortable with the idea of turning over “all” of an organization’s data. In 2018, use of the Hybrid Cloud should grow significantly, as this scenario combines the best of both worlds. On-premise Data Management can be combined with the convenience of the Cloud.
- Other Departments will Access the Cloud
Typically, the IT department would serve as a “go-between” for other departments in accessing the Cloud. However, interfacing with Cloud technology has become quite easy. Other departments, such as sales and marketing, or human resources, can now access the Cloud, directly. Security becomes a significant issue as more people are given access to sensitive information.
Data Analytics in 2018
- Analytics will Include Visualization Models
A 2017 survey of 2,800 experienced professionals working with Business Intelligence predicted data visualization and data discovery would become a significant trend. Data discovery has expanded to include, not just the understanding of data analysis and relationships, but also ways of presenting data, to reveal deeper business insights. As a result, visualization models are becoming more and more popular as a way to translate data into useable insights. The evolution of ever-improving visualization models has become an integral part of gaining insights from Big Data. (At present, they’re a little on the clumsy, crude side, and could use a little evolution.)
The human brain has the ability to process visual patterns with great efficiency. It uses the subconscious in this activity and allows decision makers to process information by quickly scanning it. Compelling visualizations engage the brains’ capacity for pattern recognition, and useful visualization models will become the preferred option for processing larger data sets and is one of the significant Big Data Trends in 2018.
- Predictive Analytics
Many businesses have used “historical” Big Data research to support predictions of future behavior. However, current, updated research would be more valuable in making these predictions. The old adage of “past results not being a guarantee of future success” still holds true in the world of Business Intelligence. Predictive Analytics provides its users with an edge, and has incredible potential for increasing profits by “knowing the customer” in real-time. (Prescriptive Analytics is still in its infancy, and may not become a driving trends for a few more years).
Internet of Things in 2018
The Internet of Things will continue to grow. How the information from these devices gets used is something else, entirely.
- Improving Retail
In 2018, consumers and business-owners will profit from an increase in sensors and data coming from various customer-owned devices. The IoT gathers information and allows businesses to market their products more efficiently to prospective customers. Tech-savvy companies have begun investing in sensor-based analytics, which will allow them to track the areas in their stores trafficked most by customers.
- Reshaping Healthcare
Big Data is now being used to drive healthcare solutions, but may also reshape the ways people access their healthcare and how they pay for it. New, wearable technology monitors an individual’s health, allowing hospitals and clinics to improve the quality of healthcare. Patients can have a networked device remind them to take prescriptions, to exercise, and be alerted when blood pressure levels change dramatically.
- Changing Security Challenges
New internet security challenges will become a problem in 2018. It is predicted hackers will seek to access the IoT for destructive purposes. In October of 2016, hackers crippled large sections of the internet by using the IoT to carry out the attack.
As the IoT continues to grow, weaknesses in the global internet-infrastructure will also continue to grow. Artificial Intelligence and Machine Learning offer solutions which will steadily become more popular. As devices become more interconnected with one another, security experts will need to learn to work with AI and ML programs.
Machine Learning in 2018
Machine Learning is a training process for computers, which is currently being used by organizations for a variety of activities, such as real-time ads, pattern recognition, fraud detection, and healthcare. But in 2018, it will be smarter, faster, and more efficient.
Ronald Van Loon, the Director of Business Development at Advertisement, said:
“Your digital business needs to move towards automation now, while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.”
Several efforts to use Machine Learning for improving the art of teaching have been developed recently. For example, California State University has urged its faculty to find and use free, or low-cost, materials in teaching classes. To simplify the process, (replacing previous course material with free, or low-cost, materials, is time-consuming) Intellus Learning provided a solution, by indexing over 45 million online resources and teaching (by way of Machine Learning) the program/algorithm to make recommendations. The instructor can upload the free, or low-cost, materials into the course materials management system, and make them available to students.
Identifying different diseases, and diagnosing them correctly, is one goal of ML research. The healthcare industry has been developing computers/algorithms with the ability to identify and diagnose diseases. At the University of Texas, at Austin, a team of researchers has created a fully automatic method for combining models of tumor growth. Machine Learning algorithms automatically identified the brain tumors. Machine Learning has been used in various medical efforts, including:
- behavioral modification
- epidemic outbreak predictions
- drug discoveries
- electronic records
- diagnosis and disease identification
Artificial Intelligence in 2018
Artificial Intelligence research is currently focused on developing algorithms which allow humans and technology to communicate more naturally with each other, and ways to train those algorithms. The goal is to answer complicated questions in natural human language. AI and ML have made it possible to automate jobs normally requiring human discretion. These jobs include such skills as:
- reading handwritten materials
- identifying faces
- cognitive skills, such as planning, and reasoning using partial information
David Cearly, Vice President at Gartner Research, stated:
“AI techniques are evolving rapidly and organizations will need to invest significantly in skills, processes and tools to successfully exploit these techniques and build AI-enhanced systems. Investment areas can include data preparation, integration, algorithm and training methodology selection, and model creation. Multiple constituencies, including data scientists, developers, and business process owners will need to work together.”
- The Gluon Endeavor
Amazon uses Artificial Intelligence. Amazon’s recommendation engine uses AI to predict a customer’s interests, with roughly a 5-10 percent accuracy. In an effort to improve prediction accuracy, Amazon has joined forces with Microsoft in offering a novel endeavor to use Machine Learning in teaching AI entities. The new platform, called Gluon, grants access to AI developers of all skill levels. The Gluon platform is described as making it easier for AI developers to design and develop neural networks.
The Gluon platform will be set up on Amazon Web Services. The Gluon interface is “open-sourced and is ready for use.” For details in accessing Gluon, go here, and drop down the page to – Getting Started with the Gluon Interface.
- AI and Cyber Security
Harvard Business Review wrote:
“Ironically, our best hope to defend against AI-enabled hacking is by using AI. AI can be used to defend and to attack cyber infrastructure, as well as to increase the attack surface that hackers can target, that is, the number of ways for hackers to get into a system. Business leaders are advised to familiarize themselves with the cutting edge of AI safety and security research.”
As businesses realize the importance of developing a cyber security program, AI will become more popular. A well-constructed AI defense system can process years of attack history and learn various attack and defense strategies. It can create a baseline of normal user behavior, and then search for anomalies, much faster than a human. This is significantly less expensive than maintaining a team of security professionals to deal with daily cyber-attacks. AI can also be used to develop defense strategies. Expect AI to become more heavily involved with Cyber Security in 2018 as well.
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