Big data project leaders still hunger for some key technology ingredients. Starting with SQL analysis, we examine the top five wants and the people working to solve those problems.
6 of 6
Wish 5: Network Insight Social networks are contributing to the scale and variability of big data. The social networks themselves use graph databases and analysis tools to uncover the web of user relationships by studying "nodes" -- representing people, companies, locations and so on -- and edges, the often-complex relationships among those nodes.
Mutual fund company American Century Investments uses graph analysis to predict the performance of the companies funds invest in. The company used the open source R statistical programming language and its iGraph package, with software and support from Revolution Analytics, to build a graph-analysis application that tracks revenue flows among manufacturers and their suppliers.
Apple, for example, has suppliers of chips and screens just as car manufacturers have suppliers of components and parts. American Century combines public and proprietary data on those buying relationships, and it applies graph analyses to get a clearer understanding of the likely performance of suppliers. These forecasts are more accurate than what could be developed with forecasts based on quarters-old public financial reports, according to American Century.
Other open-source technologies supporting graph analysis include Neo4j , a graph database developed and supported by Neo Technologies. Neo4j is used in IT and telecom network scenarios to resolve secure-access challenges, in master data management applications to see changing relationships among data, and in recommendation-engine apps to figure out what people want based on the behaviors of friends and connections. Other open source graph-analysis projects include Pregel (from Google) and Apache Giraph. It's not the stampede of solutions you see around Hadoop, but there's clearly growing interest in graph analysis.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
Top IT Trends to Watch in Financial ServicesIT pros at banks, investment houses, insurance companies, and other financial services organizations are focused on a range of issues, from peer-to-peer lending to cybersecurity to performance, agility, and compliance. It all matters.
Join us for a roundup of the top stories on InformationWeek.com for the week of October 9, 2016. We'll be talking with the InformationWeek.com editors and correspondents who brought you the top stories of the week to get the "story behind the story."