Data monetisation is a hot topic these days. Especially for people like me watching the movements of early adopters – companies who are using data to create new revenue streams or even create new businesses to capture those revenue streams.

DataStreamX is a notable start-up whose sole business is cashing in on the data monetisation market. Founded in 2014, the Singapore-based company is a data broker, enabling an open market for data value generation.  Their mission is to provide more than insights to customers – they provide a platform that facilitates data exchange. As a stickler for data quality, I’ll note that DQ is one of their founding principles … a smart strategy in a market that offers few second chances.  Data Republic is a similar example in the Australian market.

So you’ve read my post on three ways to monetise your data, and now you’re keen to learn how? Here are my three P’s for developing a successful data monetisation strategy.*

Platform
We all know that platforms must be timely, reliable and accurate. But what about standardised and automated? And while you’re at it, throw in flexible, agile, scalable and high-performance -- and suddenly you’re wondering if you need a new platform! And you very well might. Lucky for you, a variety of open source, cloud and traditional platforms exist today, putting data monetisation tools well within reach for most businesses.

Perception
How does your organisation perceive innovations such as data monetisation? Your great idea is only half the battle. If your organisation isn’t willing to put any skin in the game, your chances of success are slim.

Finding and hiring true innovators is a difficult task; innovators must be identified and grown within the business. To that end, how many organisations do you know that teach their employees to be truly innovative – and then are willing to put traditional revenues streams on the line to test those innovative ideas? The companies monetising data place a high value on innovation; they’re willing to fail fast, and in time, gamble on innovative ideas that have a high probability of success.

Personalities
While data monetisation requires technical engineers, data scientists and people with data management skills, innovators are perhaps the most important employees of all. You must have people on your team who can identify the value in your data and possible markets for monetising it. As explained by Hamel and Tennant in The 5 requirements of a truly innovative company, innovative thinkers are those who have:

• An inquiring mind … always looking beyond the obvious answer.
• The desire to go where nobody has gone before.
• A contrarian attitude … willing to ignore precedence and ingrained beliefs.
• The ability to identify trends as they are forming, removing guesswork.
• A view of the organisation that sees skills and assets instead of silos and structures.
• A relentlessly curious, anthropologist streak. Since childhood, they never stopped asking “why?”

Having these skills allows you to identify and execute on your data’s value. Additional skills such as data storytelling can help bring your vision to life for internal stakeholders. And further, artistic skills can help you view the world through a different lens, seeing data for its rhythm and flow rather than from a business perspective only. Over time, additional skillsets will emerge as the science of data monetisation evolves.

Each of these P’s could be essays unto themselves – all fascinating and complex. And all requiring an organisation that is united behind a goal and willing to accept the risks to achieve it. Nail these and you might be ready for the big time.

Next time, I’ll share more around establishing the framework for success. Until then, take a look at the research undertaken jointly by MIT, CISR and SAS called Foundations for Data Monetization, for more on the foundations of a successful data monetisation strategy.

*Citing B.H. Wixom, J.W. Ross, C.M. Beath, and C.A. Miller, “Capturing Value from Big Data At comScore Through Platform, People, and Perception,” MIT Sloan CISR Research Briefing, Vol. XIII, No. 11, November 2013.