A
For example, 70 percent of respondents say a “killer app” that would be “very useful” or “spectacular” for their business, but few could identify at this time what such an application may be. In addition, many cite business and technical issues that are holding them back from leveraging the incredible power of data.
Overall, the terms “content analytics,” “unified data access,” “semantic analysis” and “sentiment analysis” are generally understood by around half of the survey respondents. Specific big data technologies such as Hadoop, NoSQL and Map Reduce are unfamiliar for three quarters of those responding.
One of the biggest issues in pursuing big data analytics is lack of expertise. As I’ve mentioned in previous posts here at INN, there is an acute shortage of analysts and “data scientists” capable of sifting through massive stores of data and turning it into actionable insights for decision makers. The leading challenge, cited by 37 percent in the AIIM survey, is a lack of in-house expertise. The same percentage also cite the expenses of moving into a big data analytics effort.
Another challenge is a lack of tools and means to analyze the data and make it actionable. The survey finds, for example, that 39 percent of respondents say they have too much data that is currently incapable of being analyzed. Another 27 percent say there isn’t enough standardization across their tool sets, and 25 percent complain that they only can access structured data sources—while levels of unstructured data keep rising.
The survey also finds that many organizations (30 percent) have poor reporting and BI capabilities, and many others (26 percent) are still struggling to organize their content. “Both of these factors will affect priorities and the ability to roll out big data projects.” In addition, security within search and analytics is a major concern for 64 percent, including 19 percent who say it is a potential show-stopper.
Structured data types that already generate big data sets include financial transactions (75 percent), quality monitoring (21 percent), and customer data (20 percent). Unstructured data types in the big data mix include document repositories (31 percent), email (26 percent), and personal productivity files such as spreadsheets and PDFs (19 percent).
Interestingly, there isn’t a great deal of interest in deploying big data analytics within cloud settings – yet. A majority, 51 percent, prefer employing on-premises systems for big data, versus 28 percent looking at private cloud and only 4 percent considering public cloud services. “In theory, large and rapidly growing datasets and complex analytic products should lend themselves very well to Cloud and SaaS deployment,” the report states. “However, security is a big issue, and this plays against the current perceptions, particularly of private clouds.”
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
Readers are encouraged to respond to Joe using the “Add Your Comments” box below. He can also be reached at joe@mckendrickresearch.com.
This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.
The opinions of bloggers on www.insurancenetworking.com do not necessarily reflect those of Insurance Networking News.
