In a recently concluded Canada’s big data consortium organized by Ryerson university ( in June 2018), a white paper was published regarding data monetization. The purpose of the white paper was to help Canadian industries in understanding the use cases of data monetization. There were participants from many industries including government of Canada. Below are the highlights of the paper.
More than 2.5 quintillion bytes of data are produced everyday now. Only 30% of companies are using data analytics and many are unsure of how to convert data into source of wealth. Data can be used to increase revenue by improving customer acquisition, customer experience, brand loyalty, fraud and threat detection, operational efficiency and other revenue generating areas of a business.
Organizations are pursuing following four main data monetization strategies:
- Leveraging proprietary data for internal growth
- E.g., manufacturing data, customer analytics, etc.
- Trading data with business partners
- E.g., insurance companies and car leasing companies can exchange driver information captured from vehicles
- Make data open to increase access to information and benefit from other’s efforts on analysis
- E.g., open source software data and government’s data
- Making use of external data to increase revenue
- E.g., use open data sets or commercially available datasets to customize products or services for unique population clusters
This white paper discusses case studies for monetization from eight industrial sectors: real estate, marketing, security, health care, social media, energy and mining, finance, government and manufacturing. For example, imagine of analyzing financial conference call transcripts automatically by machine. If you don’t know what a conference call transcript is, then it is a method used by companies to disclose quarterly performance to public. Billions of dollars in trading rely on the financial conference calls. The white paper shows a case study where researchers have applied natural language processing techniques on these transcripts to extract key text and the trained a machine learning classification algorithm on historical sentiments associated with stock prices to automatically predict sentiments of future conference calls. This results in saving valuable time of analyst in reading hundreds of transcripts and improves the efficiency of analysis.
Many other case studies and examples are also discussed in the white paper related to the eight different industrial sectors. The readers are referred to the white paper below which is hosted by Ryerson university, it can be downloaded from the following link, and this effort was lead by one of our team member, Dr. Shariyar:
