Facts and Figures


Agile Data & DataOps

  • Soaring demand.
  • Massive shortage of talent in emerging technologies & specialist fields.
  • The dearth of talent is driving up industry salaries.
    • A cloud architect in the US can make around 130,000 $, given:
      • The right industry experience and…
      • Technology

Adjuvant Technologies

  • New technological element to reinforce Agile Data: strong reasons to pool data in Credit Unions:

    • Credit Unions are smaller than banks.
    • They lack the quantity of data banks collect from their customers.
    • Data pooling is the key to overcome this problem:
      • 95% of Credit Unions in the US are below 3 bill $ in assets.
      • They do not have enough data to build accurate predictive models.  

Use case: Lentiq combines Data Lakes with Edge Computing:

  • Goal: to give access to data to as many people as possible within an organization…
    • To perform Analytics and Machine Learning in a friendly manner.
  • The service comes with:
    • Data and metadata
    • Application
    • Notebook
    • Data
    • Infrastructure
    • Budget

New technologies will enable and extend digital transformation projects at the edge. And this is only the beginning. Two examples:

  • Verizon announced an increase in its use of Edge Computing
    • Having tested Edge functions in its 5G network in Houston…
    • Result: massive decrease in network latency.
  • Not much real-time remote control at Edge sites:
    • Most of the remote activity is basic monitoring.
  • There is demand already for private wireless networks at the far edge
    • And vendors today are providing it ad hoc.

Automation & AI

  • China is already positioning itself as a global leader in AI:
  • AI research has accelerated, increasing globally by more than 12% per year over the last five years.
  • China’s academia attracts more AI talent than it loses.
  • China is close to a leading position in AI research (Elsevier report).
    • Teradata targets Chinese clients’ need for AI.

    As AI becomes a driving force in many industries, firms are looking for AI expertise and technologies:

    • Teradata’s revenue in the US fell by 11% year-on-year to 830 mill $ in first three quarters of 2017…
    • But its international revenue surged to 700 mill $.
    • A survey of 260 multinational corporations in 2018,showed:
      • 80% of companies are investing in AI.
      • Firms are investing 47 mill $ on average in AI technology.
      • Firms in Asia-Pacific spend 25 mill $ on average.
  • Machine Learning and AI generate higher quality predictive insights:

    • Accuracy is important in Data Science and Machine Learning.
    • Improving recommendations and predictions is the name of the game.
    • The explainability of the predictions is another factor that comes into play.
      • Deep Learning’s explainability challenges will limit its use by firms.
      • Firms do not trust what they cannot understand
    • Machine Learning (ML) algorithms strengthen search results, monitor medical data and influence our admission to schools, jobs and even jail.
    • Deep Learning (DL) gives unparalleled accuracy to certain types of inference problems, such as:
      • Identifying objects in images and understanding linguistic connections.
    • Japan Airlines (JAL) used dotData’s software as part of a promotional campaign for trips to Hawaii.
    • Fujimaki’s software helped JAL analyze more than 20 customer data points:
      • Customers’ flight history, point history, use of credit cards and Web site access history.

AI: Actionable Analytics / Natural Language Processing (NLP)
• Gartner Research found that more than 90% of customer conversations are phone based, generating a huge amount of valuable data to companies.
• Customer phone calls add up to 56 million hours daily, 400 billion words spoken.
• AI-powered Natural Language Processing (NLP) & Analytics can be combined with data visualization to offer valuable insights to firms from the huge trove of phone based customer interaction.