What once was a hyped term and a far out concept has now become a widely adopted practice in organizations big and small. The Big Data revolution began as companies started making investments towards adoption of data management and analytics. However, with new terrain come new challenges and companies are still struggling with how best to create a big data strategy and extract meaningful insights and value from the data.
As we approach the second wave in the age of Big Data, companies are now scrambling to identify just how to measure the ROI of their investments and over what period of time. The new question to ask now is “How fast can we generate returns from our big data?”
It’s no surprise that companies who have already adopted a big data strategy are now looking to data monetization to answer a piece of this question. We all know that big data, when leveraged correctly, can provide valuable information and insights to improve operational efficiencies as well as drive better business performance. What companies are seeing now is that their data is not only valuable to their company, but may also be valuable to other companies as well as their customers.
A great example of what I mean is found in the relationship between retailers and suppliers. As the retailers collect POS, customer loyalty and inventory data they can in turn share this information with the suppliers allowing them to gain a better understanding of the customer’s experience with their brands.
For many organizations, data monetization has opened up the door for new revenue streams, aided in differentiation among an ever increasing competitive market and at the very least helped balance out the cost of their big data investments. But keep in mind, the companies that are most successful in this area are very mature in their data strategies, so, be sure if you are looking into data monetization that you have done your homework. Having a clear understanding of privacy, ownership and compliance is the foundation on which you have to build your platform. You will then need to take a data inventory and carry out an audit so as to have a clear understanding of your data assets, identify your target market and the needs you can address with your data, and finally understand the aggregation, mining, and enhancement processes necessary to transform your data assets into solutions or products. Its best to work with a big data partner with solid experience and expertise in this field in order to insure that all your bases are covered and you are deriving the most value you can from you data. The key here is not to just sell big data for data’s sake, but to sell knowledge and insights beyond just the information. If you can help your target audience do their job better and create better business for them… you just may be on to something.
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– Thaig Loganathan, President, Big Data Insights Division