MOST INTERESTING FACTS YOU OUGHT TO KNOW
Agile Data aims to increase quality, speed, collaboration and continuous improvement supported by data.
- The tools to achieve these aims are:
- Big Data
- 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.
- 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.
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.
- 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.