What is the future of data analytics going to look like? What science fiction-type advancements do you think we will see in our lifetimes?

Scince fiction type predictions... you asked for it.
- in 2035 a digital twin will be tried in court for violation of GDPR regulations for inadvertantly accessing data. 
- in 2035 a pet's digital twin will be at the center of a custody battle in a separation lawsuit.
- By 2035 an AI avatar will be a sitting member of corporate executive comittees and will support executive decision making in 1 out of 3 companies
- By 2035 there will be so much synthetic data, fake data, that third party agencies will be providing trust certificates for data as a means to authenticate and verify the source and accuracy of certain critical data
Maybe my vision of the future is a bit too dystopian :) 

 

Real time decision making done completely by analytics is the future.  How long is answered by how long will it take to have extremely consistent, high quality data that supports that level of analytics and automation.  We have made great strides in that direction, but still have a long way to go.  The analytics/data science are ready in many business domains ... the data is not.   Where we are most mature today, we have domain experts review the output from analytics, prior to final decision making.   When the data is consistent and of high quality, that step can be eliminated.  Will the data ever be that good is perhaps a more interesting question?

In future I think our analytics teams will spend far less time on data sourcing, transformation and mining, and far less time configuring views.  These will be achieved through simple commands (or maybe requests?) to AI.  Analysts will therefore need to have more creative flair and bring organisational systems thinking to the day to day challenges of their organisation, so that they can direct AI on where to look for correlations, what insights will be interesting to Leaders, what new information connections might yield value.  Of course AI can also make those suggestions but even AI resources will have limitations (cost, time, processing power) so cannot be expected to explore every possible outcome every time.  AI analytics will still need a navigator if not a driver, and the human analyst brings that connection from human leaders, translating their requirements to something the AI can efficiently and effectively deliver.

With many wealthier countries in population decline we may see pressures from both talent scarcity and health and safety perspectives driving organisations to invest in more and more use of sensors and biometric data everywhere from the hiring process to operational areas.  Such data would trigger a range of ethical practice and data confidentiality questions for analytics leaders to consider.

For example AI could scan candidates in real-time to assess whether certain subject-matter triggers a stress response.  Analysts could compare interview biometrics from past successful or unsuccessful hires to calibrate what constitutes the 'ideal' candidate.  AI could perhaps even assign a personality index to a candidate based on both the biometric information and interview responses which could help recruiters make good hiring decisions.  From the employers perspective this would serve to ensure they are selecting and developing the right people who are likely to stay in the role and 'yield' a net benefit to the organisation.  

For existing workforce biometrics could be deployed to optimising operations by monitoring fatigue, responsiveness, stress, etc and ensure productivity and safety are well managed.  From an employee perspective some of this analysis is designed to support in your physical and mental health, trigger rest periods and safety procedures, monitoring health indicators in your work environment, even trigger medical examinations - perhaps linked to your medical benefits plan. 

Biometrics would yeild huge data volumes and present new challenges in future data analytics.