Data Dump: 3 Ways That 2017 Has Redefined Data Intelligence

The weather’s cooling down and the leaves are starting to fall, which means that 2017 is on its way out. What a year it’s been—the marketing landscape has seen some drastic shifts over the past year, many of which were related to big data, advanced analytics, and the use of cognitive machine learning to deepen the insights marketers receive from their campaigns.

With so many exciting tech updates on the marketing horizon, I thought this would be a great time to review three of the big data trends that have defined 2017, courtesy of research done by IBM. Let’s dig in.

1. Acceptance That More Data Isn’t the Answer

In our information-hungry society, we tend to think that data is the answer to everything. It’s not. Not even close.

Most companies have only begun to scratch the surface of all the data available to them—IBM’s research estimated that 88 percent of all available data was considered “dark” to most organizations, meaning that it existed in unstructured forms such as images, natural language, or generalized behaviors that can’t be measured by traditional analytics. Think about that—88 percent of data untouched, never mind the constant influx of new data that we have coming in from all channels.

In response, businesses are changing the way they look at data and realizing that more is not better. Instead, 2017 has been about looking at new ways to assess the data we have on hand derive more meaningful insights from the noise. From the volume of data to its variety, to the speed of data collection, this past year has marked a shift towards data quality over data quantity.

2. Cognitive Insights for Consumer Connections

The point of all data collection vis-à-vis marketing is to learn more about our customers and deepen the connections we make with them. Traditional analytics have shown some success at this goal, but in 2017, we’ve seen the industry take a more drastic shift toward the use of AI and machine learning tools to assess broad data sets. Part of this has to do with the fact that most businesses have an unmanageable amount of data to dig through. IBM noted that by 2016, 90 percent of the world’s data had been created in the past 12 months alone. And this trend is showing no signs of slowing in 2017.

Basic performance analytics can’t begin to crack “dark” data or derive any type of meaningful insights from huge and disconnected data sets, but with AI-based analytics and machine learning algorithms becoming common tools for big data analysis, we expect more businesses to tap into this wealth of information to create more meaningful connections with their consumers.

3. AI’s Role in Influencer Marketing

Influencer marketing as we know it today has been around for a few years, but its use is soaring in 2017. Forbes predicted that 2017 would be the year of influencer marketing, but what Forbes didn’t predict was the role that AI would play in connecting those influencers to brands. It’s no secret that companies that successfully leverage influencer marketing gain valuable brand exposure. But on the other side of the coin, the rise of influencer options makes it more critical than ever that businesses connect with the right social media personalities.

Companies are beginning to understand this and apply more powerful AI algorithms into their influencer search in hopes of finding the perfect candidates. Given that there are millions of influencers out there to choose from, we expect an influx of advanced influencer matching tools to enter the marketplace within the next few years.

Forecasting the Future of Big Data

The biggest data trend that we’ve seen in 2017 so far is the realization that data production has far outpaced data analysis. We have too much junk and no way to organize it—and business owners are tired of trying. In response, organizations have pushed development of new cognitive analysis tools that can assess these data sets in more advanced ways. And while the fruits of these labors have yet to be seen, we’ll be eagerly watching to see what the future has in store for big data.

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Mark Olson