World of Analytics - Trends during and post COVID-19

As we evolve, either as a technologists, Leaders or Humans we learn from every interaction in our lives. How we respond to those incidents is purely our choice. COVID-19 has changed many lives around in many ways but it also showed us in our life times that we humans survived every situation be it pandemic or natural calamities. what we learn from this is what adds to our wisdom pool. 

How does the pandemic change the World of Analytics?

It all ties back to the  Survival of the fittest, term coined by Charles Darwin.

The vaccine companies during the pandemic conducted more than 500 clinical trails for a potential treatment, they have collected and curated data from trial registries and other sources to predict the spread and treatments along with clinical management of the pandemic.

What did they use apart from the chemical drug manufacturing process? -Data and Analytics

How does this emergency situation correlate to other companies and Industries? 

Now lets talk how Data, Analytics combined with AI will be the next generation of weaponry to predict, prepare and proactively lead the race in this unprecedented market shifts; Never before than now, Analytics leaders to game-up and roll their sleeves up to increase their pace and scale of analysis in processing data quicker, innovate and forge new ways to stay ahead in a competitive corporate world.

Few Trends that are surfacing or will Surface in the field of Analytics

1. Every company is warming up to get a piece of the pie in their respective market share, data becomes crucial so is the toolbox to sculpt the product. Skilled resources and beating the market becomes the key. AI techniques with Machine learning and Natural Language Processing (NLP)  will give an edge to provide insights to proactively act rather than reactive mode. Hardware chip architecture are evolving, from past processing CPUs to neuromorphic hardware for edge devices to accelerate computations and workloads and reducing the reliability on hub and spoke architecture of centralized systems and reducing the latency and load on bandwidth.
2. Gone are the days of dashboard reporting, more automated dynamic story telling will be the trend. The in-context data stories ( most relevant insights) will be broadcasted to users based on their mapping roles. Data Leaders will need to regularly evaluate their BI tools and innovate their stack to meet the growing demands of the changing business requirements.
3.  A shift from Business Intelligence to Decision Intelligence: Companies needs to shift the paradigm and prepare their mindsets to Decision Intelligence which are more decision modeling, decision management, with agile complex adaptive computation systems which combines the multi-disciplinary methods that Analytics leaders needs to get their hands dirty. This is a trend which already has its seeds planet and sprouting began.
4. We are stepping into the world of X- analytics which is a Gartner coined term which encompasses all kinds and ranges of data. Thought leaders should incorporate the mentality of X analytics to prepare for future analytics problems NOW. Pandemic is a great example why preparedness is the key to survival just like an Earthquake kit in California or Storm room in Ohio. Survival is the key but if that is to endure, then X-Analytics is the new norm to adopt along with Augmented machinery using your own operational data to consolidate the architecture with automation.
5. The buzz word of cloud should not the shiny tool anymore; In the world of economics of scaling, cloud is given, let it not be a your goal, it must be your means to achieve the X-factor!
6. As like data, tools and technologies, the resources(process and people) needs to be revamped as far adapting to changing technologies. With AI taking over using many deep learning techniques, humans are challenged even more to learn and keep up to the ask. There is a huge gap to meet the upcoming demand in the next five years.
7. If we take a pause, and look around the companies will be divided into four parts:
1. sellers of data
2. buyers of data
3. sellers of technology
4. consumer companies
8. Technology, people, process(like governance) all are hidden in all the above categories. For e.g. take an example of Blockchain, which leverages: data, its lineage ,and transparency of the transaction, with network topology. Companies who dive into it will make faster, easier business decisions than others. The key take away is data and analytics is not all about the tools but the thought processing and provoking art of the analytics leaders. 
9. Let the trends of the market determine the right question you should be asking as analytical leader and that business problem narrow your tool search. Not the other way.

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