WHITE PAPER:
Access the following white paper to uncover a business intelligence and analytics solution that can deliver the ability to intuitively collaborate actionable insights throughout the enterprise, analyze data and patterns within seconds, not days, and much more.
EZINE:
Companies aren't gaining enough value from big data. That's why there's a greater need to establish data handling policies and procedures that only good data governance programs can provide. Explore good use vs. abuse of customer data (Wells Fargo, Target, and Bank of America), 4 data governance concerns and best practices, and much more.
WEBCAST:
Access this live webcast on January 30, 2014 at 12:00 PM EST (17:00 GMT) to learn how you can use big data tools and technologies to extend security intelligence – allowing you to protect massive data volumes and uncover hidden threats.
Posted: 15 Jan 2014 | Premiered: Jan 30, 2014, 12:00 EST (17:00 GMT)
WHITE PAPER:
The following white paper explores the through the eyes of pioneering analyst, Tom Davenport, on the importance of the data scientist when it comes to success with big data and advanced analytics. Learn how they how they help, how to onboard or create them, and much more.
CASE STUDY:
Kennaw State University needed faster, easier data access, analysis and reporting. See how they transformed HR processes to eliminate errors, saved hundreds of hours annually on HRMS, benefits and payroll services, and how they leveraged self-service business intelligence to non-technical users.
EZINE:
The hype around data mining may have receded, but it remains a critical discipline for the data scientist. This issue of CW Europe looks at what exactly data mining is, how and when it is used – and why it should not be mistaken for business analytics.
CASE STUDY:
This case study reveals how the right appliance enabled leading oil supplier Petrol d.d. to dramatically decrease analytics query times – allowing the company to effectively use historical and transactional data to increase retail sales.
EBOOK:
Overcome the four biggest challenges of moving mainframe data to Hadoop: data integration, skills and expertise, security, and escalating costs.
