I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive and prescriptive analytics.
Analytics vendors and non-technical employess are democratizing data science. Organizations are looking at every employee as a data scientist so that they can bring their expertise on a business problem.
Most industry analysts are highlighting the increased role of citizen data scientists in organizations:
- IDC big data analytics and AI research director Chwee Kan Chua mentions in an interview: “Lowering the barriers to allow even non-technical business users to be ‘data scientists’ is a great approach.”
- According to Gartner, data scientists create models that use advanced diagnostic/predictive/prescriptive analytics. Their primary job function is outside the field of statistics and analytics.
Why are there more citizen data scientists now?
These trends support democratization of analytics:
- increasing need for analytics due to increased belief in data driven decision making
- easier-to-use analytics tools.
What are the tools used by citizen data scientists?
Citizen data scientists first need to access business data from various systems. For example, Kloud.io is a self-service data reporting tool which allows employees to pull data from various databases for easy analysis and automated reporting. We have listed kloud.io and other solutions for reporting.
Spare yourself the trouble and delay learning anything about quantum computing until 2020 eoy unless you are working on:
- a problem that is not solvable in reasonable time with current computers (e.g. building deep artificial neural networks with millions of layers or simulating molecular interactions). Such problems are common and almost all Fortune 500 companies could benefit from quantum computers
- Cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets
- quantum computing (sorry had to be MECE)
If you are in one of these fields, quantum computing has the possibility to transform your field in a few years. If not, check back in 2020 eoy, technology may have progressed to the point that others may also need to learn about quantum computing.
As non-technical corporate leader, what should I do about quantum computing?
If you are working on cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets, stop relying on cryptographic methods that rely on factoring large integers. There are already quantum-ready alternatives as we discuss in the use cases section.
Most successful organizations treat data-driven decision making as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.
In an AI-first company, data-driven decision making means
- Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
- Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
- Operational decisions which can not be automated with good accuracy are delegated to humans.
- If data is lacking, opinion based decisions are made.
- If data exists and has been analyzed, decision maker relies on analysis.
- If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.
Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you:
Affective computing systems automatically identify emotions. Affective computing is also called emotion detection, emotion AI, artificial emotional intelligence or affective AI. Understand affective computing in detail:
- What is affective computing?
- Can software really understand emotions? What is the theoretical foundation for emotion recognition?
- Why is affective computing relevant now?
- How does it work?
- What are affective computing use cases?
- What are alternatives/substitutes to emotion recognition?
- What are leading companies in emotion detection?
What is affective computing?
Affective computing systems auto-recognize emotions. See a longer definition below:
Affective computing is the development of systems that can recognize, interpret, process, and simulate human feelings and emotions.
It may seem strange that machines can do something that is so inherently human. However, there is growing research supporting the point that human emotions are recognizable using facial and verbal clues.