Building a Platform: Big Data Considerations

Organizations that embrace data and analysis will do far better than those that don't.

In my previous post, I examined the data that the powerful platforms of generate–aka Big Data. Today, I’ll provide four tips for managing and using this information.

The Big Question: Is Your Organization Ready?

Forget relatively new technologies like Hadoop, NewSQL, and NoSQL. No, you can’t store petabytes of unstructured data in a relational database. A dysfunctional culture can torpedo even the most promising technologies and initiatives.

Amazon, Facebook, Netflix, and Google all do amazing things with data, but technology alone does not explain their success. Rather, each organization fosters a culture that emphasizes data and analysis.

Big-Data Tools Don’t Cleanse Bad Data

Your organization has downloaded and deployed Hadoop and even hired a bunch of data scientists. Congratulations. But do you know who your customers are, or is that a hornet’s nest? As Brad Stone wrote in his excellent book The Everything Store (affiliate link), Amazon knows exactly who its 300-plus million customers are. Many organizations believe that the next shiny new thing will purify suspect records, de-duplicate data, and provide for accurate master records. Nothing can be further from the truth.

Think Free Speech, Not Free Beer

Yes, many NoSQL databases are free to download and fork. That doesn’t mean that they ship with a free team of dedicated support specialists. Effectively garnering insights into customers and employees takes significant resources and may require hiring new employees or training existing ones. Big Data isn’t easy. Actual human beings are still required to find the signal in the noise.

Visualization Is Essential

A spreadsheet or simple bar graph can only do so much. We see things much faster when presented in visual formats. That’s the premise behind The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions. In order to make sense of Big Data, new data visualization tools are required. Simple dashboards, reports, KPIs, and analytics simply don’t encourage the level of data discovery required today.

It’s Not Just for Big Companies

There’s no shortage of myths around Big Data.

There’s no shortage of myths around Big Data, and one of the most pernicious is that an organization needs billions in revenue. In my piece for Harvard Business Review, I make this very point. Small companies can avail themselves of some pretty neat, cost-efficient services:

Take Kaggle for instance. Founded in 2010 by Anthony Goldbloom and Jeremy Howard, the company seeks to make data science a sport—and an affordable one at that. Kaggle is equal parts funding platform (like Kickstarter and Indiegogo), crowdsourcing company, social network, wiki, and job board (like Monster or Dice). Best of all, it’s incredibly useful for small and mid-sized businesses that may lack a phalanx of tech- and data-savvy employees.

Anyone can post a data project by selecting an industry, type (public or private), participatory level (team or individual), reward amount and timetable. Kaggle lets you easily put data scientists to work for you, and renting them is almost always much less expensive than buying them.

Brass tacks: It’s never been easier to “get a little bit pregnant” with Big Data. Why not roll out a pilot program in one department and communicate victories throughout the organization?

Simon Says: Embrace Big Data and Platform Thinking

No organization goes from “zero to Google” overnight. Larry and Sergey have built their data machine over the past fifteen years. Still, as I’ve shown in this three-part series, plenty of lessons can be gleaned from the Gang of Four.

More than ever, it’s essential to embrace platform thinking and Big Data. Those that fight current realities will quickly find themselves less relevant or even extinct.

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