Big data is in the foreground of current topics in most IT circles. Understanding big data, like any other emerging technology, requires that it be defined. While the definition is just the beginning of the conversation, ultimately it is the value that big data promises that makes it so intriguing and deserving of attention. The ability to use big data to drive better business outcomes defines its strong appeal.
What is Big Data?
Big Data is popularly defined using the Three “V”s, defined by Gartner1 as follows:
Volume: The increase in data volumes within enterprise systems is caused by transaction volumes and other traditional data types, as well as by new types of data. Too much volume is a storage issue, but too much data is also a massive analysis issue.
Velocity: This involves streams of data, structured record creation, and availability for access and delivery. Velocity means both how fast data is being produced and how fast the data must be processed to meet demand.
Variety: IT leaders have always had an issue translating large volumes of transactional information into decisionsnow there are more types of information to analyzemainly coming from social media and mobile (context-aware). Variety includes tabular data (databases), hierarchical data, documents, e-mail, metering data, video, still images, audio, stock ticker data, financial transactions and more.
Real world big data applications commonly address one or two of the “V”s. However there are many organizations with big data projects that do indeed incorporate all three; these usually involve high volumes of streaming data from a variety of sources.
1 Gartner Press Release, “Gartner Says Solving ‘Big Data’ Challenge Involves More Than Just Managing Volumes of Data”, June 27, 2011.
What is the promise of Big Data?
Different systems store data in different formats, even within the same company, making it difficult to aggregate data for analysis. As a result, an organization’s investment in data, one of its most highly valued assets, goes underutilized. Increased awareness of the value gained by analyzing data in a geographic context drives desire for the ability to discover location based patterns and relationships in big data, to enable improved decision making for better business outcomes.
Who Uses Geospatial Visualization and Big Data?
GIS Analysts: Big data technologies provide access to unstructured, machine generated, web generated, and NoSQL data. Map visualization and geoanalytics on this data can reveal patterns and trends that are beyond the capabilities of traditional databases, spreadsheets, and files. Access to more data types can also refine existing analyses resulting in greater confidence in business decisions.
Non-GIS Users: Business analysts, researchers, and data scientists benefit from map visualization and geoanalytics. Anyone interested in creating actionable information from big data and other enterprise business applications can gain valuable insights by exposing and exploiting the geographic dimension of that data using maps as a visualization tool. Organizing data by location also provides access to other location referenced data, such as Esri’s Business Analyst, which further enriches data analyses, improves collaboration, and enables more rigorous decision-making.