Focus on

Focus on

HOW IT ALL COMES TOGETHER

Data Monetization

  • Data Monetization can be achieved by applying AA, which provides Deep insights, Cognitive services and Automating decision-making. –i.e. Nihilent–
  • 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.
  • To achieve successful data monetization, companies must formalize their vision and refine their marketplace niche.
  • Some examples of positive business impact:
    • In the financial services industry, selling credit transactions to merchants or payment networks
    • In the manufacturing industry, offering condition-based or preventive, maintenance based on monitoring of embedded sensor data (IoT)…
  • To improve results, companies must integrate:
    • Human interventions
    • And automated, Machine-Learning driven processes.
  • Infosys is a global leader in next-generation digital services and consulting:
    • It offers a solution framework that uses Machine Learning models to improve the recommendation logic for their real-time product promotions.
    • It builds industrialized capabilities to monetize data…
      • Example: recently, Infosys worked with a retail mortgage bank to generate credit scores for potential customers and process applications in real time.
      • Today, the bank generates applicant credit scores in less than 50 milliseconds.
    • 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.
    • Infosys includes the following offerings:
      • Data
      • Data
      • Analytics and Data Operations to enable organizations to be GDPR
  • Mobile gaming companies gather hundreds of thousands of records on users in-app activities, using Machine Learning for prediction and Artificial Intelligence algorithms to speed up the data monetization process.
  • These algorithms are cyclically learning on new data and adapting to current behavior patterns by computers.
    • Example Addepto:
      • A Data Science and Business Intelligence solutions provider, offering customer segmentation and churn prediction using Machine Learning embedded in CRM systems.
    • By identifying bots with ML, companies will reduce customer acquisition costs by:
      • Segmenting new users.
      • Choosing only clients that generate relevant revenue in the future.
      • Increasing customers’ satisfaction from the game…
      • Thus contributing to higher income.
    • Solutions for user monetization in the gaming industry are:
      • Identification of fraud in player acquisition
      • Customer lifetime value prediction (CLTV or LTV).
      • Customer churn prediction.
      • Recommendation engine using ML in mobile gaming.
      • Automating and integrating ML processes…
        • To identify the best solutions with CRM integration,
      • Visualize insights on a business-friendly tool:
        • With Business Intelligence (BI) tools such as:
          • Tableau, Power BI or QlikView.
  • 2019 is the year to broaden insurance’s horizon and break free from the traditional mindset by building a larger insurance marketplace and ecosystem.
  • “If we get the Analytics of our own data right, we do not even have to consider other forms of data monetization, Michael Araneta (Asian Financial Services Congress (AFSC), IDC Associate Vice President) said.
    • 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 lead.
    • Some use cases by research managers at IDC Financial Insights:
      • Several use cases for Artificial Intelligence in the insurance industry, based on connected insurance and embracing the insurtech revolution, by Arpita Mitra:
        • A future roadmap of digital banking that would transform the corporate banking space
          • And provide access to liquidity management and treasury, invented by Anuj Agrawal
        • The real-world applications of blockchain technology that have gone online in a variety of sectors, including: Agriculture, Education, Land titling, Identity verification and Law enforcement, showed by Michael Yeo.
          • Example: the Security Token Offering (STO), as a new asset class from Singapore’s stock exchange.
  • In data monetization, we are seeing the merging of major investments into:
  • Data infrastructure, data management capabilities and Analytics
  • As companies search for ways to generate new value from their data, they will look at the basic foundations of data within their institutions:
  • The data infrastructure and the business processes that support how data is collected, stored, updated, managed, secured, analyzed and used throughout the enterprise.
  • Collection, integration and use of data across different business units and product siloes.
  • 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.
  • Different models:
  • Internally oriented value creation: firms can generate value out of their data by using Analytics.
  • Externally oriented data monetization, using several models:
  • Acquiring data.
  • Providing data.
  • Bartering internal data with another firm.
  • Producing but externalizing distribution
  • Co-innovating and partnering
  • The team leading the initiative to monetize data should see it as a real build-a-business endeavor:
  • The firm has to launch this as a real business.

Management Approaches Focused on Advanced Analytics

  • Organizations with a well-planned Enterprise Project Management Office (EPMO) report are:
    • Successful in meeting 48% higher than their initial objectives & goals.
    • 42% of the projects attained their targets.
  • Between the current trends in PM, we find:
    • AI
    • Hybrid Methodologies
    • IoT
    • Standard Cibersecurity Solution
    • Special focus on the PMO (Project Management Office)…
      • To ensure organizations’ pre-defined objectives & goals.
    • With respect to Analytics and Project Management…
      • Analysts could benefit from Project Management Techniques linking Analytics phases with general PM phases to bridge the gap in communication.
      • In companies with PM:
        • AI will change the overall Project Management workflow and increase the productivity of the project outputs.
        • Hybrid project managers will be capable of handling several multiple methodologies.
        • Trending technology in searching for an effective communication over a wide range of platforms.
        • Comprehensive Data Analytics –like task reports and predictive analytics– will help PMs identify risks.
        • Probabilistic data analysis techniques will provide a range of possible outcomes for each set of data…
          • And facilitate decision making when encountering data that throws up uncertainties.
        • To ensure the success of a business, it is essential that the Project Management team should:
          • Be aware of and embrace the latest trends and strategies:
            • AI and Machine Learning
            • Hybrid PM methods
            • Kanban Boards (Kanban Methodology)
          • Prioritize workflow efficiently and avoid multitasking.
  • Data literacy is the most important of the skills business leaders will need in their workforce in the coming years. The Data Literacy Index found out that:
    • More data literate companies have a higher business value of between 3-5%.
    • This represents around $320-534 mill $ of the total market value of each business.
  • To become a data literate enterprise, what is needed is
    • Top-down vision, support and
    • To make solid logical inferences
      • And ensure their implementation in the real world is in line with the data-literate workers…
      • It is critical to produce transparent explanations that business users can easily understand, verify and act on.
    • Augmented Analytics greatly reduces the need for data literacy to extract insights
      • But does not obviate the need for learning how Analytics can be misleading.
    • Augmented Analytics:
      • Promises to bring BI to a much larger audience of business users.
      • Will reduce the burden on subject-matter experts to understand the data model used to display trend lines on a graph.
      • Business and data science will get closer and business-oriented data scientists will become increasingly important.

Advanced Analytics in India, China, Japan

  • The Indian telecoms sector has been going through a consolidation phase:
  • The major players are vying for the data services market, worth 950 billion rupees (13 bill $) by 2020, and growing at an annual rate of 21%.
    • Today, India:
      • It is 1 in mobile data consumption…
      • And well on its way to becoming 1 in mobile broadband penetration in the coming months.
      • It has overtaken USA and China in mobile data usage.
      • With a population of 1.3 billion India is a humongous telecom market.

     

     Telecom Companies in India:

    • Vodafone India has launched an integrated solution known as “Super IoTinvolving:
      • Vehicle tracking
      • Mobile asset tracking
      • People tracking (for both employees and students):
        • Employee safety & Student safety solution for schools.

     

    • Airtel rolled out its digital innovation program Project Next
      • To ‘leverage Amdocs’ innovation centers, delivery expertise and its ecosystem of startups…
    • Airtel plans to launch several digital innovations to simplify and enhance interactivity of their customer experience…

     

    • Reliance Jio is on a mission to include the 500 million feature phone users of India (out of 780 million) who have been left out of the digital revolution.
    • Jio’s network connectivity enables its customers to participate on multiple digital platforms.

     

    • Telecom companies are adopting technologies like virtualization, SDN-NFV (Software Defined Networking / Network Functions Virtualization) and
    • Indian telecoms are already using AI to solve issues related to customer care, network coverage, billing and service/product offering.
      • AI, robotics, IoT & Big Data Analytics will be the enablers of a new wave of wealth and employment creation.
      • AI may play a significant role in:
        • Seamless integration of technologies…& Automating the networks.
      • AI adoption is also set to increase in India with the introduction of the much awaited
      • Indian telecoms are beginning to make AI deployments through strategic partnerships.

     

    • Western companies must take into account that almost 100% of India’s commercial transactions are digital already…
      • To be ready to take advantage of all the generation of data to come in the near future in this market.
  • Survey conducted in March by Qlik:
  • Only 11% of employees from Chinese companies are data literate.
    • Higher than the 6% data literacy rate in Japanese firms.
    • But lagging far behind the 45% rate in Indian companies and the 33% rate in US companies.
  • A low data literacy rate might point to a top-down decision-making mechanism in firms.
  • China lacks the skills to fully leverage its information resources. At a time when its data is burgeoning it is “worrying“…
    • But the country’s data literacy rate will improve in the next few years.
    • Only when penetration of data usage is deeper –meaning more lower-level employees are engaged in it– can a company fully flourish.
  • Instead of a company allowing only its IT department to do the job of collecting and analyzing data…
    • It is a better approach if all the company’s departments are given the opportunity to analyze and use data themselves to meet their own needs.
    • Data usage is not always about compiling data reports
      • What’s more important is insights provided through the data, such as…
        • Revealing where the market blind spots are…
        • The fundamental reasons behind selling success or non-success.
  •  
  • The growing trend of Data Science in Japan
  • 2015 survey by Nikkei Asia:
  • 62% of firms say their biggest challenge is finding talent.
  • Most companies surveyed rely on educating internal employees to handle analysis needs.
  • 39% of firms use tools that can be conducted by employees without specialized skills: Excel, for example.
  • Japan shows that2% of job posts are for data scientists…
  • While in the US it is about 1.1% of job listings.
  • Searching for talent
  • The market rate for AI educators has increased over the last decade:
  • Japanese data scientists make 12 million yen (109,000 $) a year.
  • The average Japanese high school professor earns 4.32 million yen per year, while the figure rises to 52 million for college instructors.
  • This is less than the 1 million yuan (148,000 $) earned in China.
  • And the 180,000 Singaporean dollars (133,000 $) in Singapore.
  • Only 2,800 students complete a Master’s degrees in AI research.
  • Japan will try:
  • To increase the growth of talent proficient in Artificial Intelligence (AI) to 250,000 people a year.
  • Having 600,000 post-secondary graduates per year, the goal is for 250,000 of them to possess advanced AI expertise.
  • The target of 250,000 talent proficient in Artificial Intelligence per year may seem like a high bar…
  • But this is the level necessary to ease the labor shortage in AI experts
  • With the gap reaching 300,000 by the end of 2020.
  • To bring Data Analytics into corporate practices, the biggest challenge for firms in Japan and the US is attracting and retaining talented data scientists.
  • Japan will raise its competitive advantage if 1,000 top, world-class talent is produced out of 100,000 people.
  • If college and technical students learn Data Science, they will apply the technology of AI to sales and projects once they start working.
  • An AI training program, for people both inside and outside the company, is being provided by Sompo Holdings since 2017.
  • The lopsided demand from businesses could limit the number of AI instructors available to teach college students:
  • If you do not give them a remuneration at or above what they will receive in the private sector, it will be impossible to secure AI talent for the classrooms (as teachers).