Big Data tomes sit on a number of business reference shelves. Machine Learning, Analytics, and Edge Computing books compete for space in our minds, on our computers, in the cloud, and on the shelf. Over the past year, we’ve talked about the Data Scientist shortage, what Web Analytics mean to businesses, how AI will work hand-in-hand with humans and, if you’re looking for a career, how to stand out from the crowd.
As the year comes to a close and we look to the new year, we wonder what 2019’s trends will be. What will change exponentially? What and who is lagging and leading? And how to navigate the soon to be third stage of ubiquitous data. Data is everywhere and, in some instances, can be too much to wade through. So, in a world of juxtapositions, the next wave of trends is to make Big Data small which will ultimately utilize AI more efficiently.
Biting Off More than We Can Chew
Much like the idea of music in your pocket with the introduction of the iPod, the latest trend in Big Data is to make it small, bite-sized, and navigable. So, how do you make Big Data small
The tsunami of data we encounter on a daily basis is staggering and overwhelming. As data teams become unsiloed, so too, does data. As vendors, digital leaders, business executives, and data professionals come together into a centralized team, data is being streamlined into a single view within a hub. Open source sharing, collaborating, and use of enterprise data catalogs within the hub add more value to businesses and can help to drive data management strategy.
But, though education, training, and apprentice-like experiences, even the best data professionals can have trouble navigating the swathes of data they encounter each day. Enter AI. These systems are intended to cut through the data, filter the information based on algorithms it’s given and, when needed, “learn” what it needs to know to process information, and accurately share what it has discovered. From there, humans can take the information and analyze how it can be of benefit to the business and what actionable insights can, and should, be implemented.
One of the more nefarious predictions of the past few years has been the fear that robots and AI would take over jobs. But, just as the dishwasher and laundry machine were developed to ease time at those chores, AI is the answer to how to increase productivity, not take over.
Though AI has the capability to handle a range of tasks, it cannot replace hands-on, human-centric tasks. In retail, for example, AI might be used to make the process of shopping and buying more streamlined while freeing up the salesclerk to offer more focused customer service. A restauranteur could create the perfect ambience setting based on data about noise level, food preferences, busy vs slow times, and in so doing develop a customer base with whom they could discuss where the food comes from, offer classes, and more. AI is intended as a partnership to humans. Assisted productivity to free up time for more creative and complex pursuits.
Beyond the industry executive, 2019 is predicted to be the year AI enables IT to move past routine automation tasks and proactively streamlines processes. With the assistance of AI, people will be able to work smarter, not harder, be more effective, and more productive.
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