Facts and Figures

MOST INTERESTING FACTS YOU OUGHT TO KNOW

Agile Data & DataOps

Agile Data aims to increase quality, speed, collaboration and continuous improvement supported by data.

  • The tools to achieve these aims are:
    • Big Data
    • Analytics
    • Data Lakes
    • Data scientists

it combines an integrated and process-oriented perspective on data with automation and methods from the Agile approach.

  • Companies need to coordinate their data and Analytics structures to cater to the increasing demands of the business.
  • This makes organizations more efficient: providing data faster and with better quality, to render operations more steady and reliable.
  • This is not easy to do: data landscapes are complex and fast moving.
  • DataOps means: “A continual pipeline of curated and processed data, from its remote source (social media, internal operations, open data banks…) to the dashboard used to make any decision”.
  • That for every company the moment to invest in AI is now.
  • 4% of firms say they will invest in AI and Machine Learning (ML) in 2019.
  • We have not yet reached the highest business maturity in Big Data and AI:
    • By “business maturity” we mean the level of success in obtaining business value from Big Data and AI.
    • The latter are well-known technologies already, not a challenge any more in themselves.
    • The challenge now is translating into real business value.
    • 2% of executives report positive results from investments…
    • Which means that 1 out of 3 do not obtain positive results. Why?
    • The problem lies with people, not with technology:
      • 95% of executives say people and business process are the key problems to implement AI.
      • It is easier to change and deploy the technology itself, than to change the way people think to exploit the possibilities of technology.

Adjuvant Technologies

  • When there is close collaboration between IT and non-IT teams:
    • 89% of teams overcome Digital Transformation obstacles
    • Only 55% do the same when they don’t collaborate closely.
  • 66% of private and public sector enterprises admit they purchase new systems and solutions without including IT teams.

IT Departments propose a new technological element to reinforce Agile Data: Data Pools.

  • A Data Pool is a micro-data lake which is independent and isolated.
    • A Data Lake consists of one or more Data Pools belonging to the same company but managed separately.
  • Data Pools have independent administration and resource allocation:
    • Thus helping to consider budgets and resources individually, depending on project requirements and making project costs more transparent.
  • Data Pools represent the idea of “divide and conquer” applied to large, difficult-to-manage Data Lakes.

Example: Private vehicle insurance companies.

    • Health care treatments caused by accidents involve astronomical costs for both insurance and medical companies (among others).
    • Thus, they work together in road safety programs, driving support systems, smart roads, etc.
    • They could share all relevant data through a single Data Pool, dividing the expenses.

Automation & AI

  • We should introduce a new term: “The Industrial Internet of Things (IIoT)

    • Which means deploying and exploiting technology beyond the standard IoT (sensors).
    • IIoT provides many functional and diagnostic tools, like:
      • Smart processors integrated into valve drivers and network nodes.

    Customers 2020 survey:

    • 86% of buyers would pay more if there is a better customer experience
  • Experts tend not to rely neither on algorithms nor on other people’s predictions but trust their own opinion.
  • Knowing that an algorithm has made a mistake, people lose trust in it.
  • For humorous advice, people prefer friends to algorithms.
  • People distrust algorithms when it comes to moral decisions (self-driving cars or medicine).
  • Introducing Artificial Intelligence at our own homes and companies.

Examples:

  • Amazon has 100 million smart devices deployed worldwide.
  • Google has 10 times more than that…

So they are really influencing customer experiences worldwide.

  • Stitch Fix, which uses data-driven algorithms, refers to them as “your partner”, “stylist”, etc., describing the service as “personalized”.
  • Apple, Microsoft and Amazon all have virtual assistants with human names: Siri, Cortana, Alexa.
  • Worldwide expenditure in AI: over $100 billion by 2025.
  • And China is leading:
    • AI Investments in China: over 30 bill $ for state-owned companies.
    • In 2018, Chinese venture investments surpassed the 9.3 bill $ venture funding in the USA.
    • Beijing is investing 2 bill $ in an AI industrial park.
    • Tianjin contributes 16 bill $ to its local AI industry.