Customer Relationship Management (CRM) is a method to manage a company's interactions with current and future customers. It involves using technology to organize, automate, and synchronize sales, marketing, customer service, and technical support information—all ever-changing and evolving—in real time.

Emerging technologies include the technical innovations that represent progressive developments within a field such as agriculture, biomed, electronic, energy, manufacturing, and materials science to name a few. All these areas typically deal with a large amount of research and/or test data.

Knowledge discovery and data mining is the process of collecting, searching, and analyzing a large amount of data in a database (or elsewhere) to identify patterns or relationships in order to drive better decision making or new discoveries.

Disaster recovery (DR) refers to the process, policies, and procedures that are related to preparing for recovery or the continuation of technology infrastructure, which are vital to an organization after a natural or human-induced disaster. All types of information is continually examined to help put control measures in place, which can reduce or eliminate various threats for organizations. Different types of data measures can be included in disaster recovery, control measures, and strategies.

The business of virus protection involves the ability to detect known threats and identify new and unknown threats through the analysis of massive volumes of activity data. In addition, it is important to strive to keep up with the ever-evolving security threats by identifying new attacks or threat profiles before conventional methods can.

As discussed earlier in this chapter, this is the concept of connecting machine generated big data with an organization's enterprise or master data. Connecting this data can have the effect of adding context to the information mined from machine data, making it even more valuable. This "information in context" helps you to establish an informational framework and can also mean the presentation of a "latest image" (from real-time machine data) and the historic value of that image (from historic data sources) at meaningful intervals.

There are virtually limitless opportunities for the investment of enrichment of data by connecting it to a machine or other big data, such as data warehouses, general ledger systems, point of sale, transactional communications, and so on.

Project management is another area that is always ripe for improvement by accessing project specifics across all the projects in all genres. Information generated by popular project management software systems (such as MS Project or JIRA, for example) can be accessed to predict project bottlenecks or failure points, risk areas, success factors, and profitability or to assist in resource planning as well as in sales and marketing programs.

The entire product development life cycle can be made more efficient, from monitoring code checkins and build servers to pinpointing production issues in real time and gaining a valuable awareness of application usage and user preferences.

Software solutions that are firewall applications will be required to pour through the volumes of firewall-generated data to report on the top blocks and accesses (sources, services, and ports) and active firewall rules and to generally show traffic patterns and trends over time.

Enterprise wireless solutions refer to the process of monitoring all wireless activity within an organization for the maintenance and support of the wireless equipment as well as policy control, threat protection, and performance optimization.

What is Hadoop anyway? The Hadoop technology is designed to be installed and run on a (sometimes) large number of machines (that is, in a cluster) that do not have to be high-end and share memory or storage.

The object is the distributed processing of large data sets across many severing Hadoop machines. This means that virtually unlimited amounts of big data can be loaded into Hadoop because it breaks up the data into segments or pieces and spreads it across the different Hadoop servers in the cluster.

There is no central entry point to the data; Hadoop keeps track of where the data resides. Because there are multiple copy stores, the data stored on a server that goes offline can be automatically replicated from a known good copy.

So, where does Splunk fit in with Hadoop? Splunk supports the searching of data stored in the Hadoop Distributed File System (HDFS) with Hunk (a Splunk app). Organizations can use this to enable Splunk to work with existing big data investments.

This is an exciting area. Media measurement can refer to the ability to measure program popularity or mouse clicks, views, and plays by device and over a period of time. An example of this is the ever-improving recommendations that are made based on individual interests—derived from automated big data analysis and relationship identification.

Today's social media technologies are vast and include ever-changing content. This media is beginning to be actively monitored for specific information or search criteria.

This supports the ability to extract insights, measure performance, identify opportunities and infractions, and assess competitor activities or the ability to be alerted to impending crises or conditions. The results of this effort serve market researchers, PR staff, marketing teams, social engagement and community staff, agencies, and sales teams.

Splunk can be the tool to facilitate the monitoring and organizing of this data into valuable intelligence.

Geographical Information Systems (GIS) are designed to capture, store, manipulate, analyze, manage, and present all types of geographical data intended to support analysis and decision making. A GIS application requires the ability to create real-time queries (user-created searches), analyze spatial data in maps, and present the results of all these operations in an organized manner.

Mobile devices are commonplace in our world today. The term mobile device management typically refers to the monitoring and controlling of all wireless activities, such as the distribution of applications, data, and configuration settings for all types of mobile devices, including smart phones, tablet computers, ruggedized mobile computers, mobile printers, mobile POS devices, and so on. By controlling and protecting this big data for all mobile devices in the network, Mobile Device Management (MDM) can reduce support costs and risks to the organization and the individual consumer. The intent of using MDM is to optimize the functionality and security of a mobile communications network while minimizing cost and downtime.