55% of big data projects are not completed because:
– 58% inaccurate scope
– 41% technical roadblocks
– 39% siloed data and non-cooperation
Only 6% of enterprises no not have big data in their top 10 IT priorities list.
Outside of big data, top priorities are mobile (48%), dash-boarding (45%) and website (47%).
When it comes to analytics projects the 2 reasons cited for failure are:
– Lack of expertise to connect the dots
– Lack of business context around data
Batch analytics and real-time analytics are almost equally important.
When it comes to phases of big data projects, these pose the most difficulty:
– 43% processing the data
– 42% ongoing mgmt of the data
– 41% analyzing the data
When it comes to big data projects, the most significant challenge faces is:
– 80% finding talent
– 76% finding the right tools
– 75% time
– 73% understanding the platforms
– 72% education
Top requirements of big data solutions
#1 Ease of management
#2 Ability to scale
#3 Flexible architecture
#4 Speed to deployment
IT teams believe that while it is important for executive management to have increased access to enterprise data, those who have the greatest ability to impact bottom line revenue as a result of increased access to enterprise data are:
Data scientists and analytics staff followed by application developers and business users.