6. More, better NoSQL
Alternatives to traditional SQL-based relational databases, called NoSQL (short for "Not Only SQL") databases, are rapidly gaining popularity as tools for use in specific kinds of analytic applications, and that momentum will continue to grow, says Curran. He estimates that there are 15 to 20 open-source NoSQL databases out there, each with its own specialization. For example, a NoSQL product with graph database capability, such as ArangoDB, offers a faster, more direct way to analyze the network of relationships between customers or salespeople than does a relational database. "These databases have been around for a while, but they're picking up steam because of the kinds of analyses people need," he says. One PwC client in an emerging market has placed sensors on store shelving to monitor what products are there, how long customers handle them and how long shoppers stand in front of particular shelves. "These sensors are spewing off streams of data that will grow exponentially," Curran says. "A NoSQL key-value pair database such as Redis is the place to go for this because it's special-purpose, high-performance and lightweight."
7. Deep learning
Deep learning, a set of machine-learning techniques based on neural networking, is still evolving but shows great potential for solving business problems, says Hopkins. "Deep learning . . . enables computers to recognize items of interest in large quantities of unstructured and binary data, and to deduce relationships without needing specific models or programming instructions," he says.
In one example, a deep learning algorithm that examined data from Wikipedia learned on its own that California and Texas are both states in the U.S. "It doesn't have to be modeled to understand the concept of a state and country, and that's a big difference between older machine learning and emerging deep learning methods," Hopkins says.
"Big data will do things with lots of diverse and unstructured text using advanced analytic techniques like deep learning to help in ways that we only now are beginning to understand," Hopkins says. For example, it could be used to recognize many different kinds of data, such as the shapes, colors and objects in a video -- or even the presence of a cat within images, as a neural network built by Google famously did in 2012. "This notion of cognitive engagement, advanced analytics and the things it implies . . . are an important future trend," Hopkins says.
8. In-memory analytics
The use of in-memory databases to speed up analytic processing is increasingly popular and highly beneficial in the right setting, says Beyer. In fact, many businesses are already leveraging hybrid transaction/analytical processing (HTAP) -- allowing transactions and analytic processing to reside in the same in-memory database.
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