Data Monetization & Sharing Data on AA

Facts and Figures

Example 1: Nihilent

  • Nihilent is a global consulting and services company using design thinking approach to problem-solving and integrated change management.
  • Nihilent has developed a recurring process* that you can adopt to move forward with AA initiatives.
  • An agile iterative process for exploring, defining and operationalizing an advanced analytics data solution. High-quality data feeds the model as you explore and refine your use case, generate features for customers, and deploy the model. It requires a continuous feedback loop throughout, to tune the data model. Both human interventions and automated, machine-learning driven processes can be incorporated to continuously improve the outcomes
  • Nihilent can translate your vision into your next great innovation, by developing your data offering and approach to monetize your data to generate new revenue streams.


Recurring process: an Agile iterative process. An incremental process is one in which software is built and delivered in pieces. Each piece, or increment, represents a complete subset of functionality. … Scrum and Agile are both incremental and iterative. They are iterative in that they plan for the work of one iteration to be improved upon in subsequent iterations. more


  • Infosys is a global leader in next-generation digital services and consulting.
  • It builds industrialized capabilities to monetize data, beginning by creating an integrated blueprint of opportunities –unique to their business– for data-led value creation.
    • Thereon, it charts the roadmap to incrementally build the capabilities firms need to deliver on the blueprint.
    • Example: recently, Infosys worked with a retail mortgage bank to generate credit scores for potential customers and process applications in real time.
      • Infosys reengineered their credit acquisition decision engine…
      • And transformed their legacy mainframe environment to improve its agility.
      • Today, the bank generates applicant credit scores in less than 50 milliseconds.
  • Addepto is a Data Science and Business Intelligence solutions provider.
  • It offers customer segmentation and churn prediction using Machine Learning in Loyalty & Marketing (as part of Customer Relations Management)
  • It helped one of the leading Loyalty companies to enrich the platform with self-learning Machine Learning components for:
    • Automatically drawing Analytics conclusions from CRM data
    • And taking the best business actions with the help of Machine Learning.

Sources: The above text is a creative synthesis elaborated from the following sources: Overview Edwin Lisowski  Nihilent’s experts

These sources have been selected from a total of 15 articles on the subject matter. Which in turn are the result of sifting through 99 articles.


  • IDC’s (IDC Search Consulting) definition points out that Data Monetization occurs when “individuals or other organizations are trading something of value for the data that they collect, store, analyze and manage
  • In data monetization, the merging of major investments into:
    • Data infrastructure, data management capabilities and Analytics
    • Even up to a perceptible shift toward Artificial Intelligence
      • Is considered not only a cost center…
      • But could generate new revenues and a positive ROI.
  • Data Monetization can be achieved by applying AA, which provides:
    • Deep insights into customer behavior, operational efficiency and business models.
    • Cognitive services such as sentiment analysis, voice to text translation, and vision recognition.
    • Automating decision-making based on sound business rules derived from historical data.
  • How Infosys charts the roadmap to build firms capabilities:
    • Modernizing the core to prepare for digital transformation.
    • Building intelligence systems that interpret data cognitively to discover new signals and connect the unconnected.
    • Leveraging Artificial Intelligence to nurture a learnable and adaptable enterprise that evolves at digital speed.
    • Modernization entails releasing data and insights hidden in the legacy landscape to create a flexible mesh of foundational services.
    • This foundation can then be decomposed into components that can be dynamically organized in several ways and automated to deliver against an evolving context.
    • This also often necessitates divesting or modernizing the legacy systems that lie at the core of the enterprise.
    • 2019 is the year to broaden insurance’s horizon and break free from the traditional mindset by building a larger insurance marketplace and ecosystem.
    • In the 2019 edition of Asian Financial Services Congress (AFSC), IDC Associate Vice President Michael Araneta explained:
      • How Asia-Pacific banks are generating new value from data
      • Talked about the seven models * for using data and Analytics in the world of open banking.


*Insurtech + seven models Insuretech: convergence of Insurance and Technology. Seven models: seven use cases for insurance companies:

  1. New (cyber) insurance product for a new client segment.
  2. Better deal engines.
  3. Improve personalized advice.
  4. Optimize claims management.
  5. Up to date customer records.
  6. Expanding from service proposition to providing accounts.
  7. Digital identity verification.
    • Mobile gaming companies gather hundreds of thousands of records on users in-app activities.
      • Use Machine Learning for prediction in mobile gaming.
      • Artificial Intelligence algorithms speed up the data monetization process by:
        • Quick analysis of millions of customers.
        • And finding rules that are used for prediction.
      • These algorithms are cyclically learning on new data and adapting to current behavior patterns by computers.

Sources: The above text is a creative synthesis elaborated from the following sources: Michael Araneta (Open-Tec); J. Albert Gamboa (Business); 

These sources have been selected from a total of 14 articles on the subject matter. Which in turn are the result of sifting through 48 articles.


  • As companies search for ways to generate new value from their data, they will look at the basic foundations of data within their institution:
    • The data infrastructure and the business processes that support how data is collected, stored, updated, managed, secured, analyzed and used throughout the enterprise.
    • There will also be fundamental discussions on governance, especially in the collection, integration and use of data across different business units and product siloes.
    • Inevitably, the organization will have to consider who owns and controls the data:
      • Recent regulatory guidelines in Asia/Pacific makes it clear:
        • The customer has ultimate ownership and control of his/her data.
      • However, generating new value from data can and should proceed:
        • Data-fueled intelligence is permeating all aspects of business
        • Other companies from other industries are generating new value from it…
          • Financial services cannot be the hold-out from this.
        • Several new principles on data utilization may drive or hinder value generation, depending on how they are interpreted. These include:
          • Privacy impact assessment.
          • The right to be forgotten.
          • Consent and
          • And data portability.
  • With Data Monetization, organizations would achieve:
    • Increasing operational efficiency.
    • Empowering their employees.
    • Re-envisioning how they engage customers.
    • Transforming their product offerings.
    • And will create new revenue opportunities by leveraging their data assets.
  • Several Artificial Intelligence models will solve business problems in an expert-assist, near-fully automated mode.
  • People will be able to:
    • Make connections unseen before.
    • Find problems unarticulated before.
    • Build solutions that solve the unsolvable
    • Through collaborative relationships that benefit consumers in ways unexplored.
  • Araneta: “If we get the Analytics of our own data right, we do not even have to consider other forms of data monetization”.
    • He pointed out the following regional trends to watch in the near-term:
      • Integration of data from social media, telecoms and locational intelligence firms.
      • Data augmentation from Analytics-as-a-service companies to validate marketing leads…
  • By identifying bots with ML, you will:
    • Reduce customer acquisition costs.
    • This helps in segmenting new users
    • So you choose only clients that generate relevant revenue in the future.
    • This will increase customers’ satisfaction from the game, contributing to higher income.


  1. Internally oriented value creation: firms can generate value out of their data by using Analytics:
  • To ameliorate cross-selling other products and services to existing customers.
  • To upsell products and services availed by the customer to:
    • Upgrade customer experience and customer usage of the product…
    • To look for secondary customer needs that can be solved by additional features of the product.
  • To increase brand visibility and ameliorate marketing effectiveness
    • To attract those not using the product.  
  1. Externally oriented data monetization: firms can use several models:
  • Acquiring data: data augmentation for ‘richer” data, by working with other organizations as sources of data and insights.
  • Providing data: real-time data as an asset…
    • This includes bartering internal data with another firm in a direct exchange or in special exchange of value/service.
  • Producing but externalizing distribution: a hybrid model of data monetization…
    • As a referral or registration fee to third-party data platforms, websites, and other channels.
  • Co-innovating and partnering with start-ups and the developer communities:
    • Third parties can use the organization’s data to:
      • Build non-core capabilities
      • And innovate in the firm’s digital offerings based on Application Programming Interfaces (APIs).
  1. The team leading the initiative to monetize data should see it as a real build-a-business endeavour.
  2. The firm has to launch this as a real business: in keeping with the new ways

of doing business in this vastly different, data-driven marketplace.

  • To achieve successful data monetization, you have to:
    • Formalize your vision
    • Refine your marketplace niche
  • Some examples with their consumer benefit and business impact:
    • In the financial services industry, selling credit transactions to merchants or payment networks, consumers benefit from:
      • Transaction information for precision marketing.
      • Fraud detection and credit risk management.
      • Leading to a business impact:
        • Lower customer-acquisition lifetime value per customer…
        • Which reduces revenue/costs and improves profitability of credit products. (ESTO SIGUE SIN ESTAR CLARO: REVENUE NO ES COST: SOLO PUEDE SER UNA DE LAS DOS, PUES SIGNIFICAN LO CONTRARIO)
      • In the manufacturing industry, offering condition-based or preventive, maintenance based on monitoring of embedded sensor data (IoT)…
        • Continuity of business leads to minimized unplanned downtime.
      • To improve results, we can integrate:
        • Human interventions
        • And automated, Machine-Learning driven processes.
  • Infosys solution framework uses Machine Learning models to improve the recommendation logic for their real-time product promotions.
  • It includes the following offerings:
    • Data Monetization to discover and realize new insights-led opportunities, which:
      • Are unique to the business
      • To improve the work they are already doing…
      • And also innovate in unexplored dimensions.
    • Data Modernization to build a boundary-less data landscape, powered by:
      • Cloud-based data platform architectures to scale data
      • And pervasive analytics to democratize its consumption.
    • Analytics to cover the whole spectrum of Analytics work done in an organization:
      • Insights
      • Modeling
      • AI & ML
    • Data Operations to build automation-driven, scalable platforms for optimized data operations and managed services.
    • GDPR to enable organizations to be GDPR ready, by leveraging our end to end framewok:
      • Assess, Define, Administer and Manage.
  • Arpita Mitra (research manager at IDC Financial Insights) showed several use cases for Artificial Intelligence in the insurance industry, based on connected insurance and embracing the insurtech revolution.
  • Insurance has become customer-centric instead of product-oriented
    • With new types of risk coverage like ride-sharing insurance and “sachet” or bite-sized risk prevention
  • Anuj Agrawal (research manager at IDC Financial Insights) invented a future roadmap of digital banking that would…
    • Transform the corporate banking space
    • And provide access to liquidity management and treasury.
  • Michael Yeo (research manager for IDC Financial Insights) showed the real-world applications of blockchain technology that have gone online in a variety of sectors, including:
    • Agriculture
    • Education
    • Land titling
    • Identity verification
    • Law enforcement.
      • Example: the Security Token Offering (STO), like a new asset class from Singapore’s stock exchange.
  • Solutions for user monetization in the gaming industry are:
    • Identify fraud in player acquisition:
      • To gain qualitative customers, minimize the harm of fraud.
    • Customer lifetime value prediction (CLTV or LTV):
      • LTV ML models can predict how much money a particular customer will spend in your application.
    • Customer churn prediction:
      • Churn ML models predict which customers are at risk of stopping use of your application.
    • Recommendation engine using ML in mobile gaming:
      • You have to personalize the offer & content.
    • Automate and integrate ML processes in mobile gaming industry:
      • To identify the best solutions with CRM (Customer Relations Management) integration:
        • Create personalized offers.
        • Retain the most risky
        • And, as a result, monetize
      • Visualize insights on a business-friendly tool:
        • With Business Intelligence (BI) tools such as Tableau, Power BI or QlikView, you can:
          • Make any dashboard in few minutes
          • Set up alerts on preferred events
          • Or be emailed when something unusual is happening.