Big Data refers to the mass amounts of raw information available in the Internet. This information not only includes base content such as emails, photos and ads, but also includes metadata: data about the content. A professional, usually a data scientist, must first seek the relevant information and organize it before analyzing it. This is no small feat given the volume of data available. Think about a recycling scheme to obtain all the plastic water bottles spread throughout a landfill spanning acres. Sorting through manually would take copious amounts of time; so does sorting through data, programming codes notwithstanding.
Following collection, the data is categorized as structured or unstructured. Unstructured data is information that has been collected into a database but not given any qualities. Conversely, structured data is information that has been given attributes. These attributes qualify the data so it may be used in any type of analysis a professional may need to carry out. Without this process, any information that is collected cannot be used to draw models, patterns or conclusions. The following figure, used during the panel, illustrates the difference between relevant, structured data and not useful, unstructured data.
Big data can be particularly useful in preventing financial crime. The field of forensic data analytics, for instance, focuses on “identifying, preserving, recovering, processing and analyzing structured, standardized and/or codified digital information for the purpose of generating evidence that may be used as such in an investigation, and that may ultimately serve as legal action support in litigation.” Additionally, protecting data from outside threats adds a layer of complexity. The recent data breach at Target Inc. makes a good case for taking preemptive, preventive approaches to data security. With global privacy laws on the rise, this issue is becoming increasingly important.
Because tackling a phenomenon of such scope is difficult, it is in the interest of financial institutions and other businesses to carry out cost-benefit analyses and consider options beyond in-house analytics operations, such as contracting and outsourcing. This was the overarching recommendation offered to the audience.
Will Heilbut is an MBA/NPTS student at MIIS, specializing in International Finance. He grew up in Bogotá, Colombia during the height of the 90s drug trade and as a result he has an interest in financial crime, compliance and anti-corruption measures.