If technology had a Zodiac, 2015 would be the year of big data.
More businesses used data of all forms to make decisions, and more consumers learned more about their life and their habits with the help of data.
With this momentum, the year ahead will be a big one for the information economy. Here are five trends in data usage that we expect to see in 2016.
1. Data as a corporate asset
It’s one thing to acknowledge the value of data in theory, and it’s another to act on that value. Gartner’s Doug Laney said that organizations have been giving it lip service, but not behaving as though they see data as an asset in terms of the way they’re collecting, generating, managing, and deploying it. However, that is changing in that more organizations are thinking about data as a corporate asset.
The problem is that information is “not a recognized balance sheet asset,” Laney said. And, that inhibits truly engaging data in the business.
“As with any asset, all forms will be collected, maintained, and utilized,” said Rob Thomas, vice president of product development for the analytics platform at IBM. “Not just the traditional structured data, but unstructured, text, Internet of Things (connected devices), etc.”
2. Deeper customer data
One of the most valuable types of data available to businesses is customer data. Whether it is consumer retail customers or enterprise clients, knowing how they approach the product is crucial. However, customers are approaching businesses from different angles and different platforms, and companies are finding it difficult to keep up.
“In 2016, we’ll see more focus on a combination of deterministic (log-in / authentication) and probabilistic (data management platforms) methods to bridge this gap,” said Brent Dykes, evangelist for customer analytics at Adobe. “With growing privacy concerns from consumers, brands will need to be careful about how they use this cross-device customer data in their marketing efforts.”
3. Growing data variety
Customers are still one of the driving data forces for many brands. But, the types of data collected on customers is expanding well beyond where they clicked on a webpage or what their perceived demographic is.
“As mobile becomes the primary digital channel for many brand interactions, it offers a rich, new type of data–location data at macro (GPS) and micro (LE Bluetooth beacons) levels,” Dykes said. “Location data is a valuable arrow in marketer’s quivers that can be used for location-specific, in-context offers and personalization.”
In addition to capturing customer information, businesses are moving into a host of other areas to capture the data associated with it.
“There’s been an awakening, if not yet a complete shift in focus, on the value of data that streams from things–whether it’s cars, or refrigerators, or drones, or whatever that is,” Laney said. “That’s the next wave of big data.”
Additionally, certain industries are looking at other unique data points and how they affect the business. For example, Laney said, some lenders look at social media to determine potential risk in an investment. Also industries like manufacturing, distribution, and retail look at weather data and its effect on their organization.
Moving beyond next year, we’ll likely see even more data types being accounted for. Laney said, within the next decade, he wouldn’t rule out the use of biometric data or even DNA. And, as the variety of data increases, vendors need to do more to accommodate it.
4. Data by industry
As with many tech trends, certain industries get a head start as early adopters in the space. Big data is no exception. Financial services, technology companies, retail, and telco are all leaders in leveraging data in their workflows.
“Because they are further down the maturity curve, they will be generating significant benefits from their early-adopter investments in technology, people, processes, and culture,” Dykes said.
In 2016, these leaders will likely continue to widen the gap between themselves and the laggards in other markets. Although, there are industries and verticals that are taking an alternative approach, or looking at different types of data, and gaining value from it.
“We’re also seeing major breakthroughs in industries such as automotive, aerospace, electronics, industrial products, oil and gas, and energy and utilities where the larger focus is on analyzing equipment and machine-generated data to predict potential maintenance issues and optimize manufacturing productivity,” Thomas said.
5. Dealing with privacy
With privacy concerns at an all time high, the data collection practices of many organizations are continually coming into question. In many instances, the issue that is raised has to do with who owns the data that’s collected.
“I think it’s the idea that people think there’s such a thing as data ownership, and, because data is so fungible and replicable, that idea of data ownership really is nonsense,” Laney said. “It’s all about data rights and privileges.”
As such, Laney said, organizations that are producing or capturing data needs to focus on defining rights and privileges associated with that data.
Additionally, because most people don’t want to be governed or policed, they’ll need to position data governance in a way that showcases improved value in the data.