Subhadip Mitra

This paper presents an in-depth exploration of Data Monetization for Enterprises that would enable them to effectively transform their data assets into significant economic value. The paper introduces: (a) Data Monetization Framework - that encompasses key strategic technical capabilities, collaborative Data Product development, agile experimentation, product incubation, delivery model, pricing model and privacy/security by design; and (b) Activation of the Data Monetization Strategy - a 12-month long structured approach is discussed to implement, operationalize, scale, and sustain the strategy-through a Data COE. Data Product Development, Pricing Strategy, Delivery Mode and prioritization is also discussed through a Value-effort study. Technology strategy is discussed in detail with MoSCoW scoring. The paper delves into the role of emerging technologies such as generative AI (large language models), big data analytics, artificial intelligence, and machine learning in enhancing data monetization efforts. It also addresses the challenges and opportunities presented by these technologies, including issues related to data privacy, security, and ethical considerations.

A privacy aware open protocol to prove without repudiation an entity’s point-in-time presence, participation and location in physical or virtual space. OLP-protocol.org is a planned implementation node demonstrating the protocol.

In the current connected world - Websites, Mobile Apps, IoT Devices collect a large volume of users’ personally identifiable activity data. These collected data is used for varied purposes of analytics, marketing, personalization of services, etc. Data is assimilated through site cookies, tracking device IDs, embedded JavaScript, Pixels, etc. to name a few. Many of these tracking and usage of collected data happens behind the scenes and is not apparent to an average user. Consequently, many Countries and Regions have formulated legislations (e.g., GDPR, EU) - that allow users to be able to control their personal data, be informed and consent to its processing in a comprehensible and user-friendly manner.This paper proposes a protocol and a platform based on Blockchain Technology that enables the transparent processing of personal data throughout its lifecycle from capture, lineage to redaction. The solution intends to help service multiple stakeholders from individual end-users to Data Controllers and Privacy Officers. It intends to offer a holistic and unambiguous view of how and when the data points are captured, accessed, and processed. The framework also envisages how different access control policies might be created and enforced through a public blockchain including real time alerts for privacy data breach.

In this paper, I introduce a novel Cascading Algorithm that leverages Bayesian inference to facilitate arbitrage-free pricing of financial options. Our approach uniquely integrates prior market knowledge with sequentially updated price information to create a convergent pricing model that continuously refines the estimates of option prices under the principle of no-arbitrage. By employing Bayesian updating mechanisms, the algorithm accounts for uncertainties inherent in market data and dynamically adjusts to new information, ensuring robust convergence towards equilibrium prices. We detail the mathematical framework that underpins our model, emphasizing the convergence properties of the algorithm through both theoretical analysis and extensive simulation. The Cascading Algorithm is tested against standard datasets, demonstrating superior performance in achieving faster convergence and higher accuracy in price estimates compared to traditional models. This work not only provides a significant advancement in option pricing but also proposes a framework that could be extended to other financial instruments for achieving efficient, arbitrage-free market predictions.