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Data Monetization

Deciphering data monetization with business intelligence

Data monetization is redefining traditional business models and pushing enterprises to overcome boundaries to achieve differentiation. For instance, Retail companies started adopting this strategy to personalize their offerings to customers thereby increasing their sales indirectly. For this, they need to broaden their data sourcing ecosystem with other industry partners to generate valuable insights. Such a data-backed business model calls for an ocean of opportunities for service providers in the areas of cloud, data warehousing, analytics, etc.

Data is the new gold. Like gold, data must be cleaned and processed for creating business value. Data thus has become a perennial asset that enterprises monetize, either by creating new business models or by reducing costs.  Telecom, Auto, Retail and Hi-Tech businesses are highly active in their data monetization initiatives either by adding new-services or selling their data.

There are 2 paths of data monetization that enterprises pursue to achieve their desired monetary outcomes:

  • Internal – Leveraging data sets to optimize enterprise value-chain operations, products and services, thereby enabling personalized solutions to customers
  • External – Selling a wide array of business data to customers, partners, and other industry players to enable new business models

While the former is leading to extensive digitization, the latter aims at positioning an enterprise as a digital leader enabling wakes of cross-industry innovations. Noteworthy is the fact that these data monetization paths are not mutually exclusive, and only few companies are donning both these paths.

Telecom players like Verizon use data to optimize their internal operations and client relations. They also sell their data to enable new use-cases for customers and cross-industry partners. Some such cross-vertical use-cases enabled by Telecom companies include Geofencing, Geotargeting, Fraud detection, etc.

Enterprises have lately realized the critical need to explore data commercialization across vertical, horizontal or cross-industry apart from gaining a tactical advantage. Auto, Telecom, Insurance, and Healthcare industries have been highly active in exploring these data commercialization models on the following lines:

  • Vertical delivery- Where data insights are tailored to specific industries (e.g., prescription data for the pharmaceutical companies)
  • Functional delivery- Where the same set of data is being used by a business function across various industries. (For instance, economic indices and indicators are used for demand forecasting in retail sales as well as real estate sales)
  • Cross-industry value delivery– Where data extracted by the industry for a use-case can enable different use-cases for an adjacent industry. (e.g., Driver monitoring data captured from vehicles by auto for safety enables usage-based pricing for insurance and vehicle leasing)

To enable such delivery models, it is highly critical to eliminate data boundaries. The free flow of data can be restrained by boundaries such as–IT infrastructure, business processes, application portfolios, enterprise policies, and industry practices. A boundaryless data platform breaks down the system, technology, process, and organizational barriers, and integrates the data chain – from creation to consumption.

Boundaryless data platforms maximize data harvested by the enterprise. An integrated platform to source, clean, aggregate, analyze, and visualize data will help monetize data within and beyond the enterprise. Therefore, enterprises are partnering with data aggregators and service providers to develop such data monetization platforms that encompass the following attributes:

  • Aggregation and management of internal and external data sources
  • Managing structured and unstructured data using MongoDB, Cassandra, etc.
  • Identifying usable/monetizable data sets
  • Deploying advanced analytics models
  • Transforming advanced analytics to enable wakes of use-cases or business models

Draup’s ecosystem intelligence is extremely efficient in analyzing the data monetization opportunities for service providers. Our digital tech-stack, outsourcing signals, and startup ecosystems along with the funding and acquisition data on the platform enable companies to identify, analyze, and predict the major technology opportunities.