What’s Next for Big Data?

What’s Next for Big Data?

What’s Next for Big Data?

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In just a few short years, Big Data has already transformed the way companies do business, and we’ve only just begun to scratch the surface. As companies have learned to gather all sorts of data, they’ve begun to see the potential in what lies ahead for putting that data to good use.

Some transformative companies are finding that their data could actually be their biggest asset. Not only are these data-savvy companies able to learn about and better serve their customers through insights gained from data, but they are also finding ways to monetize their data by selling it to partners and downstream vendors. For example, services like Uber and Lyft are gathering tremendously insightful data about customers’ travel habits, as are sites like Airbnb, VRBO and others. Meanwhile, Fitbit and other companies that offer fitness trackers have discovered tremendous value in the health and activity data their users monitor and upload. Even Apple, which certainly isn’t in the business of health care, now has unprecedented insight with its native Health app.

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In theory, this massive treasure trove of data opens a whole new world of opportunities for both B2B and B2C companies to gather and act on insights in ways they never imagined. But, because of some significant technical and financial obstacles, not every company has figured out what’s next. They’ve dipped their toes into the data mining waters, but haven’t yet devised a solid strategy for how to move forward.

One of the biggest obstacles to realizing the promise of Big Data is the massive financial investment required. So far, most successes have come through multimillion-dollar projects like @WalmartLabs, Walmart's dedicated data innovation lab. But, this is the world’s largest company, with very deep pockets and virtually endless resources. Of course, this sets a standard that very few companies can hope to achieve.

What makes actually leveraging Big Data so resource intensive? There are three primary reasons:

Data is coming in faster, and from a rapidly increasing number of sources: mobile, cloud applications, the Internet of Things—from RF tags that track inventory and equipment to household appliances, everything, it seems, is now “online”—and, of course, there is real-time data from social media.

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Almost all of these new sources deliver data in unstructured or semi-structured formats, which renders conventional relational database management—the basis for SQL, and nearly all modern database systems—virtually useless. In addition to collecting and storing challenges, privacy and regulatory compliance requirements create a significant new layer of complexity, with constantly-evolving standards that need an entire team, along with advanced technology,  to manage and maintain.

As Big Data has gotten more complex, the technologies for managing data have also grown increasingly complex. Open source tools like Hadoop, Kafka, Hive, Drill, Storm, MongoDB, Cassandra and more, plus a litany of proprietary spin-off and competing solutions, all require deep technical expertise to operate and apply in a business setting. And these resources are scarce and both difficult and costly for most non-Fortune 500 companies to acquire.

It’s easy to see why the vast majority of companies are struggling to merely manage and mine their data stores, let alone actually use that data to their advantage. There is a tremendous void in practical, useful and realistic tools that enable the average business to effectively capitalize on their data. To be clear, there’s hardly a shortage of Big Data tools—but efficient, effective solutions that don’t create data silos and giant inter-dependent loops that are extremely difficult to maintain are sorely lacking.

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