Big Data Means Big Prospects for the Channel

Big Data Means Big Prospects for the Channel

Does Your Organization Have a Big Data Initiative Under Way?

The vast majority are in some stage of big data deployment. Yes, operational and in production: 32%, Yes, under consideration: 31%, Yes, completed: 28%, Nothing planned: 9%

Big Data Budgets

Within three years, 50% say big data budgets will exceed $10 million. 2013: Greater than $1 million:68%, Greater than $10 million:19%. 2016: Greater than $1 million:88%, Greater than $10 million:50%.

Business Functions That Drive Big Data Investments

Line-of-business executives and risk officers are leading the charge. Sales/marketing: 70%, Risk management/fraud/security: 68%, New-product development: 64%, Research: 64%, IT and operational: 64%

Big Data Initiatives in Which Investments Are Made

Analytics applications and data integration top the list. Acceleration of analytical processes: 70%, Development of more sophisticated analytics: 70%, More effective integration of existing data sources: 69%, Creation of analytic sandboxes for data discovery: 65%, Migration of batch processes to big data: 57%, Improved fraud detection: 54%, Deployment of advanced analytics: 53%

Primary Business Benefits Expected

It’s also about bigger, better and faster data. Accelerate speed at which insight Is gained: 87%, Integrate a greater variety of data sources: 82%, Analyze larger volumes of data: 81%, Improve overall analytics capabilities: 80%, Analyze new sources of information in real time: 70%, Reduce the cost of the analytics application environment: 70%, Offload production systems: 62%

Was an ROI Analysis Required to Get Big Data Project Funding?

No, it’s a long-term strategic initiative: 50%, Yes, based on revenue, growth and savings: 20%, Yes, based on cost savings: 10%, No, ROI justification is not required in their organizations: 8%, No, for other reasons: 7%, Yes, ROI based on revenue growth: 5%

Factors That Drive Big Data Adoption

Executives are aligning big data projects to business objectives. Executive sponsorship: 83%, Clear definition of business objectives: 82%, Recognition of data as a shared asset: 68%, Consensus on importance of analytics: 65%, Enterprise information strategy: 64%, Organizational alignment: 64%

Steps Taken to Ensure Successful Adoption

Establishment of governance standards: 61%, Establishment of executive oversight committee: 56%, Establishment of big data lab or center of excellence: 52%, Designation of line-of-business project owner: 49%, Designation of C-level executive as owner: 49%

Does Your Organization Have a Chief Data Officer?

The study suggests it’s a job description that’s still evolving. No: 52%, No, but considering: 21%, Yes, within the last three years: 17%, Yes, within the past year: 7%, Yes, prior to 2010: 2%

What Is the Business Value of Big Data?

It takes the guesswork out of business. Better fact-based decision making: 80%, Discovery of new correlations and patterns: 74%, Reduced risk: 70%, New-product innovations: 66%, Improved customer experience: 66%, More efficient operations: 64%

The Primary Drivers of Big Data Projects

Integrating and analyzing data from existing sources: 86%, Integrating and analyzing data from new sources: 83%, Integrating and analyzing larger volumes of data: 80%, Ensuring greater accuracy: 65%, Integrating and analyzing streaming data: 55%

Big Data or Database Management Solutions Under Evaluation

Hadoop still dominates the conversation. Hadoop: 63%, Teradata/Aster: 43%, Cloudera: 36%, IBM PureEdge/Netezza: 33%, Oracle/Exadata: 29%, Microsoft SQL Server: 29%, EMC Greenplum: 24%

Analytics or Visualization Tools Under Evaluation

Relative newcomers have a strong presence. SAS: 77%, Tableau: 63%, SAP Business Objects: 52%, Microstrategy: 41%, Qlikview: 30%, Revolution Analytics: 28%, Tibco Spotfire: 24%

Data Domains of Most Interest

They are usually the areas that are the most opaque. Customer transaction data: 82%, Financial data: 75%, Market data: 68%, Social media data: 65%, Behavioral data: 65%, Fraud detection: 61%, External data sources: 61%

Respondents’ Big Data Talent Requirements

Retraining is preferred to recruitment. Need to invest in retraining: 68%, Combine retraining with recruitment: 46%, Actively recruiting data scientists: 30%, Have successfully recruited data scientists: 30%, Face extreme challenges recruiting talent: 21%, Have enough in-house expertise: 19%, No need for data scientists: 4%