2017 will prove to be a year that Big Data analysis trends will be at the forefront of collective global interest. With businesses in every industry utilizing data and analysis across departments for the advancement and alignment of business strategies and priorities, the current year will only indicate increased interest and implementation of various data and analysis technologies and tools.
Data analysis is not an isolated, segmented form of information management wielded solely by IT and data science companies. It’s an innovative, targeted, and insightful means for organizations to discover specific intellect about any number of issues affecting their business, both directly and indirectly. This would be ideally from determining which are the markets in that a company is performing the best to deciphering which online channel is producing the most profit, to pinpointing customer behaviors and sentiments, etc. And it’s not limited to these concerns; data analysis incorporates grander, more extensive areas of analytics, including machine learning, artificial intelligence, cognitive analytics, and so much more.
Data analysis is changing the world in leaps and bounds and shows indications of drastic growth in several areas over the course of this year. Marketing and advertising, healthcare, education, airline, and media, are just a few of the multitude of industries that are utilizing data analytics to make improvements and developments in our existing technologies. These technological advancements, fueled by data analytics, are improving the world, and bettering our experience within our environments.
As the New Year unfolds before us, we will witness five particular Big Data analysis trends, and they include a greater emphasis upon, and implementation of, machine learning, Internet of Things (IoT), predictive analytics, cloud convergence, and the customer experience.
Once a prop for futuristic sci-fi movies, machine learning is now an actuality and becoming an important part of our everyday lives. Computers are now able to self-learn autonomously from specific pre-programming measures and make adjustments accordingly. Much like human beings modify behaviors depending on their current situation, environment, and past experiences, machine learning is capable of taking the available information and reacting to suit the situation.
How does this work? The short version is that computers not only recognize patterns in existing data, but they learn to adapt as they are exposed to new data sets. Within the context of data analytics, machine learning is enabling organizations to obtain insights about future outcomes, trends, risks, and opportunities that will impact their business.
As more devices are being connected to the internet, it’s becoming necessary for companies to be able to accommodate this change and implement appropriate solutions. The Internet of Things is the connection between devices, hardware, people, digital machines, and the data generated and networked across this connection. Cell phones, city lamps, wearable devices, smart cars, and gaming consoles, all connect to the internet and produce massive quantities of data.
Gartner predicts that by 2020, over 26 million devices will be connected to the internet. Organizations must change and upgrade their platform technologies, internal infrastructures, cloud architectures, and security measures to reflect this massive surge in IoT data.
To stay pertinent and competitive within their market, businesses are adopting predictive analytics tools and techniques into their strategies at an increasing rate. It’s no longer enough to be able to analyze historical data; companies have to be able to address future outcomes, risks, and opportunities to stabilize their position. Predictive analytics is applied to analyze data and make informed, fact-based predictions.
The ability to reduce risks, create valuable marketing opportunities, more effectively manage resources, and detect potential fraud and other risks, is making predictive analytics a more widely used data analysis method. The validity of predictive analysis as an indispensable tool for the modern business will be substantiated in 2017, and beyond.
To improve organizational efficiency and share, and access data more easily and quickly throughout an entire business, companies are looking to cloud convergence solutions. The large quantity of data utilization within organizations requires a scalable, capable, and cost-effective way to manage, network, compute and store data effectively.
Organizations are relying on cloud infrastructures to propagate business continuity, merging multiple technology components into a single cloud system for Big Data processing. This will be the foundation for digital businesses, and Forrester predicts increased cloud adoption and convergence of multiple clouds across enterprises in 2017.
Businesses are discovering that the customer experience holds increasing value and significance, and they must work harder to offer a better level of personalization to address the customer’s needs. Modern Big Data and analysis tools and technologies allow companies to understand their customers, and provide greater customer experiences as they connect with a business over time.
Insights derived from data and analysis can indicate areas that require improvement, provide information about a customer’s emotional connection to the business, where and how they interacted with a product, and allow businesses to serve the needs of their customers in a more pertinent way.
These five Big Data Analysis trends are progressively significant for the modern business to focus upon given the sheer amount of data being produced in recent years. With so much information available to organizations, the world of data and analytics is less specialized and more widespread across career fields, applications, and intentions. 2017 will soon indicate just how consequential, and critical, these Big Data Analysis trends truly are for modern business leaders.
– Research Optimus