This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.
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 the data is turned into analytics.
Impediments to Data Monetisation
Many companies are concerned that the data they possess, if sold, could reveal trade secrets and personalised information of their customers, thus violating Personal Data Protection Act Laws.
Many are also 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.
Dashboards and Applications
The most common approach for companies who have embarked on data monetisation 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:
- Long lead time and costly to develop: Average development time for a dashboard or application is 18 months. Expensive resources including those of data scientists and developers are required.
- 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 meshed with other data, where valuable insights and analytics could be developed
Data as a Product
What many companies have fail to realise is that the raw data they possess could be cleansed, sliced and diced to meet the needs of data buyers. Aggregated and anonymised data products have a number of advantages over dashboards and applications
- Short lead time and less costly to develop o The process of cleaning and slicing data into bite size data products could be done in 3-4 months 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 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 best location for a new distribution centre
- A maintenance company could be using footfall to determine where to allocated cleaners to maximize efficiency while ensuring clean facilities
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