Who will drive big data in 2018?
When it comes to increasing the bottom line, more executives are focusing on making data-driven decisions powered by big data and analytics. It is undeniable that the big data revolution has unleashed a seismic shift in how businesses grow and evolve in 2018. Those that haven’t adapted to the change are due for a rude awakening. With renown market intelligence firms like the International Data Corporation, or IDC, estimating the big data market will yield worldwide revenues of more than $203 billion by 2020, many are asking the same question: Who will be the main drivers in 2018?
According to IDC, the sectors that have been consistently driving this growth include financial services, government, and professional services. Along with discrete and process manufacturing, these five industries accounted for half of worldwide big data and business analytics investments in 2016 and will remain the top five industries through 2020. Why is this so? Let’s take a closer look.
It is no wonder that the industry with the largest investment in big data and business analytics (close to $17.0 B in 2016), stands to reap the largest benefits. The adoption of technologies that leverage machine learning and extract value from big data has unlocked a Pandora’s Box of information. Lifting the veil from consumer behavior and spending patterns has given way to valuable details on security, fraud, channel usage, and even product cross-selling. Banks from coast to coast are pouring millions into big data in order to derive utility across various spheres of their functioning, ranging from financial crime management, sentiment analysis, reputational risk management, regulatory compliances management, and more. Let’s not forget that big data also improves transparency. Deeper and more transparent analyses across national and global organizations are needed to meet the new regulatory and compliance requirements released every year.
Government leaders across the country are finding that big data is not a mere trending topic, but critical to nationwide innovation. Big data’s success in driving positive financial and constituent results has resulted in a surge of agencies making the move to access data throughout the public enterprise and using analytics to evaluate data, contain spending, automate services and drive real value to the constituents they serve. Although government decision-makers do not always know the key capabilities required to effectively draw insights from large volumes of data, federal agencies are accelerating the pace of change when deploying big data and analytics. Big data applications in the areas of transportation, education, poverty, and many others have the potential to foster public transparency, civic participation and even inter-departmental collaboration.
Architects, engineers and millions of professionals have been using data to track, analyze and solve problems for years. Many of today’s marketing and sales professionals use some sort of business intelligence application to collect data, analyze it, and create models to manage spend and increase profitability. One thing that most Big Data practitioners have in common is their employment of American professional services firms to lead the charge and smoothen the journey to big data analytics maturity. From a geographic perspective, IDC predicts over 50% of all big data and business analytics revenues will originate from the United States. It also forecasts that by 2020, the U.S. market for big data and business analytics solutions will reach more than $95 billion—but not without the professional services industry.
Explore Other Resources from Advoqt Technology Group
The blog created by Rapid7 concerns security orchestration and automation tools. The blog gives a definition of what these tools do. A SOAR implementation would begin with defining and understanding the security issues being faced by the organization and thinking...read more
Our fifth article is from Buyer’s Guide and is an article by Karen Scarfone of Scarfone Cybersecurity. This article gets into specifics concerning the top security information and event management (SIEM) systems. The tools collect security log data from many sources,...read more
Our fourth article is a blog by Rostam Dinyari, a strategic cloud engineer, and concerns how an organization needs to gather and prepare data for machine learning deployment. A list of guidelines is presented. The first phase in data collection is to define the types...read more