Big Data is not the latest jargon that has crept into executive meetings, it’s becoming an essential business practice used by most organisations today. Over the years, businesses have become aware of the insights that they can gain from data analytics and are collecting increasing amounts of data. Yet, many businesses do not have a proper data strategy in place and are simply collecting data in a frenzy. There is a difference between Big Data and having lots of data. Collecting data just for the sake of it in hopes of using it in the future is not only bad business practice, it leads to potentially costly problems for your company.

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Just before 11am New York time on Oct 16th, Twitter’s stock started showing unusual activity on the Flockpit Dashboard. Twitter had traded sluggishly in the morning, hence such an abrupt rise in activity was a strong indicator for an upcoming price movement and a quick check revealed that a tweet allegedly posted by Steve Ballmer, claiming that he had bought 4% of the company, was the hot topic.

Like any commercial agreement, data product sales are bound by terms and conditions between a data vendor and data buyer. These T&Cs include service level agreements for uptime and response time, authorizations for use and resale, and technical support for bug fixing and API integration. If a vendor is new to the data economy, these T&Cs may be a daunting task and require significant institutional change.

The following post if part of a five part series detailing how to successfully bring a data product to market. To learn more, download the free “Visionary Leader’s Guide to Data Monetization.”

Direct Sales vs. Chanel Approach:

Direct:

If the primary consumers of a data product are easy to define, segment and access, then vendors may find it useful to employ a direct sales force. This is especially true if data sales are a primary component of the vendor’s business model.