With the exponential growth of IoT and M2M, data is seeping out of every nook and cranny of our corporate and personal lives. However, harnessing data and turning it into a valuable asset is still in its infancy stage of development. In a recent study, IDC estimates that only 5% of data created is actually analyzed. Thankfully, this is set to change as companies now have found lucrative revenue streams by converting their data into products.
Impediments to Data Monetization
Many companies are unaware of the value of their data, the type of customers who might potentially be interested in those data, and how to go about monetizing the data. To further complicate matters, many also are concerned that the data they possess, if sold, could reveal trade secrets and personalized information of their customers, thus violating personal data protection laws.
Dashboards and Applications
The most common approach for companies who have embarked on data monetization is to develop a dashboard or application for the data, thinking that it would give them greater control over the data. However, there are several downsides to this approach:
- Limited customer base
- The dashboard or application is developed with only one type of customer in mind, thus limiting the potential of the underlying data to reach a wider customer base.
- Data is non-extractable
- The data in a dashboard or application cannot be extracted to be mashed up with other data, with which valuable insights and analytics can be developed.
- Long lead time and high cost to develop
- Average development time for a dashboard or application is 18 months. Expensive resources including those of data scientists and developers are required.
Data as a Product
What many companies have failed to realize is that the raw data they possess could be cleansed, sliced and diced to meet the needs of data buyers. Aggregated and anonymized data products have a number of advantages over dashboards and applications.
- Short lead time and less cost to develop
- The process of cleaning and slicing data into bite size data products could be done in a 2-3 month time frame without the involvement of data scientists.
- Wide customer base
- Many companies and organizations could be interested in your data product. For example, real time footfall data from a telco could be used in a number of ways:
- A retailer could use mall foot traffic to determine the best time of the day to launch a new promotion to drive additional sales during off-peak hours.
- A logistics provider could be combining footfall data with operating expenses to determine the best location for a new distribution centre.
- A maintenance company could be using footfall to determine where to allocate cleaners to maximize efficiency, while ensuring clean facilities.
- Many companies and organizations could be interested in your data product. For example, real time footfall data from a telco could be used in a number of ways:
- Data is extractable
- Data in its original form could be meshed and blended with other data sources to provide unique competitive advantages. For example:
- An airline could blend real time weather forecast data with customer profile data to launch a promotion package prior to severe bad weather for those looking to escape for the weekend.
- Real time ship positioning data could be blended with a port’s equipment operation data to minimize downtime of the equipment and increase overall efficiency of the port.
- Data in its original form could be meshed and blended with other data sources to provide unique competitive advantages. For example:
Monetizing your data does not have to a painful and drawn out undertaking if you view data itself as the product. By taking your data product to market, data itself can become one of your company’s most lucrative and profitable revenue streams. By developing a data monetization plan now, you can reap the rewards of the new Data Economy.
