A by-product of the Information Age is a myriad of unstructured data created by companies aptly termed by data managers as “big data.” The sheer volume of the data makes managing it very costly, and it is difficult to derive any use from this data without the right technological tools. However, there are analytical tools available that allow companies to identify and investigate important patterns within their big data that may help bolster their customer service offerings.
Sources of Big Data Collection
Instead of looking at big data as an unwieldy, expensive to manage headache waiting to happen, the advice from data experts is to treat this group of data as a veritable gold mine. The first steps to gaining benefit from a gold mine is identifying the veins of gold and extracting the gold from the mine. Companies apply this concept to big data analytics by identifying the sources of all their big data and the appropriate tools for extracting the data for use by analysts. Here are a few examples of big data sources and popular associated methods for extraction.
#1 Social Media Data
Right now social media is one of the hottest components of a company’s internet marketing strategy because of its widespread use by customers. Since many businesses realize that their activities should focus on creating a great customer experience, there is perhaps no better way to capture the lifestyle habits of customers than by tapping into their social media profiles, demographic information, and network of like-minded “friends.” This type of data is cleanly mined through the use of a social media network specific application program interface (API) integration that allows data from one system to be ported to another system for use by data analysts.
#2 Publicly Available Internet Data
There are many sources of free and readily available public data relating to public records, demographics, and financial services. Some of the sources of this information are found within U.S. Census Bureau reports, EDGARS Securities and Exchange Commission database, and the Bureau of Labor Statistics reports. Since the data available from these sources are already semi structured, using a data parsing tool helps further organize the data into useable formats for human analysts.
#3 Network Packet Data
A company’s network is one of the richest sources of big data that it possesses because nearly all business transactions travel across the network. Companies that employ network analyzers may capture and investigate all or part of the network traffic transversing the network at any given time. A common and free network packet analyzer or “sniffer” is Wireshark which allows captured network data from a variety of network platforms to be displayed in a useable format.
#4 Archived Documents
Companies should not overlook the value of old documents and correspondence among the company, its vendors, and its customers. The great thing about this type of data is that it already comes in a semi structured format. To help organize and gather the relevant data from these documents, data analysts use parsers to comb through documents.
Benefits Of Analyzing Big Data
Big data analytic results fuel a variety of decisions made by companies. After analysts correctly format and organize big data, they then determine patterns within big data that they use along with more refined data points and information to establish pricing strategies, tailor plans for company growth, and strengthen negotiation positions.
Often data analysts derive the information needed to propel a company past its competition from raw big data that is readily available but found in distributed locations. Armed with the right technological tools, skilled data analysts turn big data into customer relationship management gold.