As we’ve discussed on this blog in previous posts, the growth of Big Data – data that is increasing in volume, rapidly changing pace, and includes varieties of data such as structured and unstructured – presents organizations with new challenges as well as new opportunities. One of the challenges organizations face is that as the rate of data creation and consumption continues to accelerate, so will the demand for business data analysis professionals. As explained in the McKinsey Institute Study, the demand for analysts will largely surpass supply. However, in addition to the challenges, a key opportunity remains for organizations to better understand Big Data on another level – creating actionable, integrated insights across multiple data sources. Key drivers of the demand for leveraging Big Data to improve decision-making can be addressed through greater focus on self-service analysis powered with visualization, along with methods for building and analyzing insights from disparate data sources.
For instance, the importance of creating integrated insights can be seen in recent studies that have shown that activities in social networks involving key individuals correlate with information transfers and investment activities in security markets. For example, consider the activities of a portfolio manager. Portfolio managers place larger bets on firms they are connected to through their social network, and perform significantly better on these holdings and on holdings relative to their non-connected holdings. Not surprisingly, VC firms who are better networked also experience significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or a sale to another company.
SNA is based in modeling relationships between groups of people. Social network analysis uses graph theoretic ideas and applies them with the premise that the structure of the graph can be used to understand and identify critical relationships and influential people. However, networks modeling real world relationships often have different characteristics compared to relationships used in SNA. For instance, to accurately “connect the dots” involving large quantities of diverse information and see patterns involving multiple aliases of people, different approaches are needed to handle the misleading, inaccurate, or incomplete information. One of the issues with traditional SNA is that the people and entities in a network are often not treated as adaptive and capable of taking action, learning, or altering their networks over time.
Recent advancements in network analysis involving complex network topologies with multiple relationships between nodes, network behavior that is based on uncertain information, and time-based change of networks, have enhanced the value of incorporating advanced network analysis techniques as a key part of an analytics toolset to aid in better understanding data relationships. More organizations are beginning to understand that with advanced visual analysis technology, they can build integrated insights across all of their available data, enabling them to better understand emerging opportunities and threats.
Using advanced analytics, businesses can study Big Data to understand the current state of their business and track customer behavior. Security and law-enforcement organizations can use advanced visual analysis to build integrated views of emerging threats from all available data sources to take timely action. The combination of advanced visualization techniques, together with social network analysis techniques, will help bridge the emerging gap between the vast amounts of available information in Big Data and the available resources to better understand them.
In the next series of blog contributions, we will continue to explore the incredible opportunities that Big Data represents, and new products and technologies that can contribute to better understanding and new insights.