What are the watch-outs and risks?

Question

Data Privacy / GDPR.
Risk 1: Lack of proper cybersecurity practices.
Risk 2: Scarcity mentality — “If AI-based systems are used, I will lose my job.”
Risk 3: Being too late — every company will do it, so better be the first. Customers remember first moments.
Risk 4: Choosing the wrong leaders or suppliers.
- It is really hard to find a good, experienced data scientist these days.
- Both choices have cons:
  - Outsourcing to big IT & consulting companies (e.g. IBM Watson): expensive consulting fees and technology dependence.
  - Open source (R, Python): high salary & dependence on the technically savvy person.
- Not everyone is a data “scientist” (note the difference between a data “scientist” and a data “engineer”).
- You may not really need a full-time data scientist — maybe just a data engineer at lower cost, who will grow into a data scientist.
- The first person you hire for big data should have at least 5 years of BI experience and a technical background.