Big data, unstructured or structured, fast or slow, in multiple contexts or one, is a beast to manage. Big data is growing fast fuelled by the democratization of data and the IoT environment. Jim Sinur and Dr Ed Peters tell Forbes that often, organizations simply control what they know they get results from and then store the rest for future leverage. In fact, most organizations use less than 20% of their data, leaving the remaining 80%, and the insights it contains, to be left outside to the operational and decision-making processes.
Fortunately, there is hope as this is where Big Data can start to rely on AI and engage in a “cycle of leverage”. Presently, the interaction between AI and Big Data is in the early stages, and organizations are discovering helpful methods, techniques, and technologies to achieve meaningful results. Typically these efforts are neither architected nor managed holistically. Jim Sinur writes that their work has shown there is an emerging “Cycle of Big Data” that they would like to describe and share.
Big Data Cycle
The “Big Data Cycle” is the typical set of functional activities that surround the capture, storage, and consumption of big data. Big data is defined as a field that treats ways to manage, analyze and systematically extract information from, or otherwise deal with, data sets that are too large and complex to be managed with traditional software. The “Cycle” is, in short, the process of leveraging big data into desired outcomes.
Data management is a process that includes acquiring, validating, storing, protecting, and processing the required data to ensure the accessibility, reliability, and timeliness of the data for various users.
AI can assist here in several ways, including assisting with hyper-personalization by leveraging machine learning and profiles that can learn and adapt. AI can also help in recognition of knowledge from streams of data through Natural Language Processing(NLP) categorization and relationship capture.
Organizations need to keep their pulse on incoming signals and events to stay in tune with the current state of the world, industries, markets, customers, and other constituents while sitting out distracting noise events. While savvy organizations that employ strategy planning actively look for specific patterns of threat and opportunity, unfortunately, most organizations are reactive suffering at the whims of events. Both types of organizations should be continually looking for “patterns of interest” from which to make decisions or to initiate actions that are already defined and stored for execution.
AI can help by recognizing both expected and unexpected signals, events, and patterns to recognize anomalies that might warrant attention potentially.
The understanding of data can often change with the context from which it is viewed and outcome for which it can be leveraged. The “subject” of data can mean something slightly or significantly different in one context versus another. Understanding the context is as important as understanding the data itself.
AI can learn the subtle differences and context-specific nuances to track the evolution of the data’s meaning in multiple contexts, whether it is “interacting” or not. This is particularly useful in understanding conversations and human interactions with NLP as interpretation grids often differ.