Data monetization is all about selling the data which in turn is about revealing certain characteristics about the business like:
Making a process run more smoothly and efficiently
Cashing in on certain types of behaviors
Bringing out the true value of the product or asset.
Data monetization depends on how individual company approaches the concept but there are certain five steps which remain core to the data monetization no matter the company or their individual approach.
Instead of starting with data the right approach is to start with the staff asking them which questions must be answered in detail and on time might impact the performance most. Later, these questions can be used to check whether the currently available data is enough or in time more data will be needed.
Next, it comes to the way in which data is being analyzed and signals are extracted. Does the company have the analytical capacity when it all comes down to answering the questions. Inspiration for data monetization generally comes from questions, from data and at last but not the least from the analytical methods.
It is imperative that the identifying the trends in the big data can lead to the data monetization. A whole bunch of opportunities lies in:
Velocity of the data
Newer forms of the precision and
In determining the opportunities for combining the different data sets.
Velocity plays a greater role when the information is valuable for the relatively shorter period of time. For example, if the shopper if a shopper is your app to check the prices inside your shop the whole point lies in identifying the shopper and offering him in-store offer to make the sale.
It refers to examining the data at a more granular level than ever before. By bringing the data under the microscope it is possible to create the high-resolution models which can provide the greater insights and this is how doctors are able to find infections in premature babies a day earlier.
Fusion refers to the idea of combining the many sources of information to create more clear and valuable view of the data. Rea-estate companies combine data from many sources to access the real value of the property.
In fusion where different data sources are combined, t only makes sense to add more external sources which will enrich the data and its relevance. Each organization must dedicate one team member for searching the valuable external data and this search does not need to be limited to open data but collaborations can also be made with the partners where both parties can share and discuss the data.
Poking the data warehouses with the traditional SQL queries will yield less information and that too very slowly. Technologies like Machine learning and advance analytics are required to profile the big data sets and extracting their signals.
An organization can play only one role in data monetization. It can either be a consumer of the data, an aggregator of the data or the creator of the new data. By understanding which role fits the organization best, one will be able to find the ways to monetize the data.