One of the emerging ideas in Big Data handling is one of merging self-learning machines with autonomous analysis to create a sort of Star Trek computer for trends analysis and event-driven architectures. The possibilities are growing daily as technology improves and Big Data gets bigger. It may be possible for such learning machines to eventually formulate their own hypothesis regarding the data and present new ideas to business leaders on their own or even be authorized to act on their findings automatically.
Currently, it’s possible to leverage information discovery in such a way as to detect relationships between what appear to be unrelated events or data sets. This is being used in many ways, from government tracking terrorists to academic researchers correlating mulch-disciplinary findings into new hypotheses. Using all types of information, such as unstructured data from disparate social networks to electronically stored but non-analyzed data from science, things can be pulled together by such machines and data farms to find new avenues of information use.
Retailers are rapidly learning to analyze consumer data from a huge variety of sources in order to measure customer satisfaction. What used to take days or weeks of data culling and survey-taking can now be done in seconds as market analysis, consumer trends, purchase histories, and more are all analyzed simultaneously and continuously by machines.
Business users are becoming much more savvy about the potential of Big Data and are demanding smart information exploration and analysis. Vast amounts of information is being re-explored and quantified by IT departments and their systems. Monetizing that data is big business and few are willing to miss out on the dividends. Companies that aren’t re-evaluating data collection, storage, and use are losing to companies that are. Few bits of data are now considered trash-worthy and everything becomes mission critical.
As the growth of self-learning machines and Big Data coincides, the previous weeks of analysis and hypothesis building and proving will give way to minutes or seconds as the computers themselves begin to do it for us. The business person or the sales team will no longer have to hope they understand what’s contained in the system to ask the right questions, but will instead be informed of what those questions are and the answers to them with little input from them. Instead of asking specifics, such as “If Customer X lowers their purchase volumes, would it be because they’re buying from Competitor Y or because they’re seeing a drop in sales or a change in business?” Instead, the data center will have already noticed the drop in sales and asked the question and delivered an answer, so the sales team’s question will become “How do we help Customer X improve?” and “Can we make up for the loss elsewhere?” Even those questions will become redundant as the computer begins following the trail on its own.
Near- and real-time information will become the driver of business. No longer will trends for the past weeks, months or years matter. Instead, focus will be on the trends of the past few days, hours, or even minutes. As artificial intelligence improves, so will the services provided by Big Data systems.
This will be the primary trend of the 21st century as technology grows to keep up with data.