Big Data Trends

Global Survey of Business, IT and Big Data Professionals 

The realization of value from big data in on-premises environments has lagged expectations, largely due to complexity, cost overruns, steep demands for specialized talent, and need for powerful computing platforms. 

As a result, companies are rapidly moving big data processing to the cloud, where powerful, cost-effective self-service platforms and elastic computing are readily available. 

ML and AI

The move to the cloud has turned big data into a profit center with use cases centered around artificial intelligence (AI), machine learning (ML), and analytics. But as new big data opportunities emerge, so do new challenges. 

Sponsored by Qubole, Dimensional Research launched a survey in June 2018 of 401 data professionals with big data responsibilities in enterprises across the globe. 

This report provides insights into big data processing trends, challenges, and solutions across enterprises worldwide. 

Big Data

As the digital world provides an ever-rising flood of information, big data lakes in modern organizations are growing to immense sizes and will continue to do so. 

Forecasts call for future datasets that far exceed the sizes of today’s big data repositories. 

Big Data Processing 

Big data is a vital component of everyday information processing and a key asset for enterprises. Enterprises process big data continuously for multiple purposes, from analytics to ML, to application integration and more. Big data plays an essential role in delivering new insights, innovating, increasing productivity, and realizing new revenue opportunities. 

Enterprise Big Data 

Big data is being used across a wide and growing spectrum of departments and functions in modern organizations. The insights and intelligence provided by big data translate directly into operational efficiencies and competitive advantage. 

Sources of Big Data

Big data is pouring in from across the extended enterprise, the Internet, and third-party data sources. 

The staggering volume and diversity of information mandate the use of frameworks for big data processing. 

Big Data Frameworks

While no single software framework dominates the big data landscape, Apache Spark and Presto are showing impressive gains. Survey data also shows organizations moving from homegrown approaches to open-source technologies. 

Cloud Big Data

The overwhelming majority of organizations are moving their big data processing to the cloud to take advantage of its convenience, cost, integration, and performance benefits. 

Self-Service Analytics

By empowering users to build their own data pipelines to perform analytics and utilize machine learning, big data teams can control staffing costs and raise the productivity of business professionals across the enterprise. 

Big Data Tools

Three-quarters of survey respondents noted a sizable gap between the dedicated tools and resources available and the potential value of their big data projects. This mismatch triggers an inability to transform data into profits and reduce associated operational costs. 

The Shortage of Big Data Talent is Very Real 

As big data becomes a vital part of information strategies, IT organizations would like to grow headcount to meet project demands. But many IT groups have flat budgets and face trouble finding qualified big data talent. The answer lies in maximizing the productivity of existing big data teams. 

What is your current ratio of big data administrators to users? 

Big Data Admins Must Serve More Users 

Only 40 percent of big data administrators are able to support more than 25 users—a startling number, since today’s flat budgets call for admins to serve more than 100 users. As big data strategies and implementations mature, organizations require platforms that enable much higher admin-to-user ratios. 

Which of these challenges is your big data team experiencing today? 

Challenges Faced by Big Data Teams 

Big data strategies are not without their challenges, which most often are triggered by overwhelming data and computing demands that require special technologies and skill sets. Most big data teams face a variety of issues that need to be addressed with education, headcount, and better tools. 

What are the major obstacles to your current machine-learning objectives?

Machine Learning Obstacles 

Machine learning is being used in diverse initiatives that include crucial security, maintenance, customer-care, and lead-generation applications. But like most data-intensive solutions, machine learning presents a variety of implementation challenges.  

What is the top complaint you hear from executives and stakeholders about big data initiatives? 

Big Data Time-to-Value 

The most common complaints cited by executives and stakeholders focus on the time and costs required to derive real business value from big data initiatives. These comments illuminate the need for smarter,  more productive platforms, tools, and technologists. 

Do you need a faster, easier way to reap the rewards of big data in your organization? 

Cloud-Native Data Platform 

More than 200 organizations worldwide use Qubole to process over an exabyte of data per month for AI, machine learning, and analytics. Qubole’s cloud-native platform delivers: 

  • Fastest time to value from AI, ML, and analytics 
  • End-to-end data processing on a single, shared platform 
  • Self-service capabilities to support 10 times more users and data per administrator 
  • 50 percent lower cloud data processing costs than alternatives 

Since the company’s inception, Qubole has continued to push the limits of possibility in big data and cloud computing.