As the widespread adoption of Big Data reaches its fifth year, we consider its limitations, changes, future and why Artificial Intelligence (AI) is the next disruptive technology.
Digital data – the kind found in web pages, blogs, and social media – has unarguably altered how we do business. Not only has it changed how people generate and consume information, it?s become a leading part of the Big Data influx. After all, what makes Big Data so big? It?s the volume, variety, and velocity of the data that today?s systems must process. And digital data is a huge tributary of the Big Data river.
It?s only been about five years since the 2012-2013 business year saw Big Data begin to dominate. Today, it?s certainly an accepted part of sales, manufacturing, marketing, data analysis, and operational activities (to name just a few). We?re seeing interesting and previously impossible leaps forward in video and text analytics, fraud detection, and churn prediction. But while Big Data has solved many problems, it?s also bringing its own conundrums.
The Big Data Questions
Even though there have been significant advances in data storage and analysis over the past decade, the underlying structure of Big Data systems has started to pose some problems. Businesses are asking:
- Can existing systems keep up? Making sense of massive volumes of data is the need of the hour. And even though many Big Data systems are not chronologically that old, they haven?t aged well. They are becoming redundant or saturated, and unable to respond to growing demands.
- Is Big Data dependent on human intervention? Technology is supposed to save time and effort. Right now, all Big Data processes are implemented and supervised by humans. But what if tedious tasks like segregating and refining data could be turned over to an intelligent machine? What if other Big Data processes could be put on auto-pilot and self-managed? This could reduce errors and make businesses run smarter and smoother. And this day might be coming sooner than we realize, thanks to Artificial Intelligence (AI).
AI and the Future of Big Data
If Big Data is adept at ferreting out valuable information, then AI excels at helping us find and use the insights this information produces. AI does this in several ways; it can automate business processes; it can self-learn and optimize its own performance. Most importantly, though, AI can be an ideal platform for human-machine interaction.
This is going to be particularly noticeable in processes relating to sales and marketing. Along with a focus on CRM and market research, AI-enhanced Big Data tools look like they will revolutionize salesmanship over the next few years. There are a lot of horizontal technologies that use AI, which means that AI can be easily integrated with other functions, products, and services. Companies that have deployed AI-enhanced tools have seen dramatic results in their sales and marketing effectiveness.
The point here is that AI and Big Data need each other. They are mutually dependent, in terms of technology. And in terms of practical results, they produce great ROIs. The river of digital data isn?t going to slow down. There is and will be an enormous amount of data to utilize. But the effective utilization of data is down to the working relationship between Big Data systems and AI. Big Data will store, sort, and refine its reams of information; AI will help analyze the data and present effective solutions to sales and marketing teams.
To data scientists and analysts, this next wave of AI-enabled Big Data is exciting. To businesses, it?s full of promise. But like other new processes, it comes with a warning: companies need to prepare now. Otherwise, it could be a disruption rather than an opportunity. If current systems are struggling to adapt to the velocity and volume of today?s Big Data, it?s time to re-examine their usage.
