“Harnessing the potential of data monetization is imperative for telecom companies. They possess a wealth of data that, if properly harnessed, can yield substantial returns. However, the decision to pursue data monetization can be approached in two ways: individually as a single telco or collaboratively as an aggregation of telcos, what do you think? In the era of data monetization, telecom data holds the potential to generate significant revenue. However, the question arises: should each telco venture into this domain individually, or should they unite their data resources as an aggregation? Pursuing data monetization individually might lead to misleading outcomes, given that most Nigerians use multiple phones.
Gaining insights from a single source may offer an incomplete view of the customer, resulting in machine learning models with very limited accuracy. Curated data accuracy is significantly enhanced with larger, cleaner datasets.
Data serves as the bedrock of digital businesses, and whether this transformation proves to be transformative or disruptive hinges on the strategy adopted by firms. When contemplating data and storage solutions, a common architecture that accommodates multiple platforms and data models concurrently is essential to realize its full impact.
The telecom data landscape encompasses various categories, including but not limited to: network usage data (e.g., calls, text messages, and data usage), billing/payment data, customer service data, roaming data, customer feedback and survey data, fraud and security data, network infrastructure data, app and social media usage data, subscriber data, location data, call detail records, competitive data, and more. Numerous businesses stand to benefit and new business could potentially be birthed from the development of innovative digital business models and products derived from this data.
These encompass but not limited to: credit and financial services, identity resolution services, social behavior analysis, media services, audience segmentation solutions, e-commerce platforms, business operations optimization, sales and lead identification, customer journey analytics, and more.
The potential value derived from these endeavors hinges on the quality of machine learning models built upon curated data. The upstream and downstream of data will be a discussion for another day, however, successful data monetization also involves addressing upstream processes such as data discovery, collection, cleaning, and storage. Equally important is the downstream aspect, where we must consider how to leverage this data effectively.
To unlock the full potential, we must pose cross-pollinated questions, beyond unit/departmental silos, beyond industry verticals, this will challenge existing assumptions and lead to valuable insights. We are at a critical juncture that marks the moment when informed decision making becomes paramount.
With the necessary technological infrastructure in place and a crucial investment in talent acquisition, we lay the foundation for future success.
The talents we cultivate today will be instrumental in shaping our decision-making capabilities tomorrow. Those who hesitate risk falling victim to data Darwinism, as the digital landscape evolves swiftly.”
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