Data science is a fascinating technology that is rapidly evolving. In order to keep up with the industry, one has to be prepared to spend some time researching and brushing up on skills and knowledge. Whether you’re a professional working in the field of data science or an aspirant who is just getting started, it is always considered to be a good practice to be connected with resources that keep you in touch with the current trends.
Here’s a listing of top 9 trends for executive reading in no particular order
1. Data Science as the Driver of Scenario Planning. As unprecedented change becomes the reality of life and businesses especially in these COVID times, organisations have prioritised Scenario Planning across functions. Enterprises have deployed Data Science to make this process intelligent. Key focus areas have been cost savings, supply chain resilience, inventory optimisation, manufacturing planning.
2. Automated Data Ops: with extreme speed being the name of the game, organisations are expediting Data Ops programs to land data from source to the data warehouse / data lake through staging, cleaning, de-dupe, transformation stages in a speedy, automated manner. The key is to make strategic, clean data available for analytics, insights generation and decision making with extra speed.
3. Platforms as the Drivers of Intelligence: This year the shift to a platform-oriented approach continues to accelerate to create greater organizational leverage. As more organisations across industries become software companies, they are realising that they need to graduate from doing soloed projects to building leverage able products. The basic shift is in designing each technology capability on a ‘build-once, leverage across the enterprise’ model.
4. Prudent Use of Data and Data Science: With new regulations rapidly emerging around the world , businesses need to exercise extreme prudence in data acquisition, storage and use of data and data science, particularly in consumer space. Given the sharp rise in relevance of healthcare use cases, service providers need to be especially careful. Monetary penalties through lawsuits can wipe out all gains.
5. Accelerate In-house Data Science Talent Growth: New sources of value will be uncovered across all functions through experimentation with Data Science. This requires business teams to be skilled in data-led decision making. They need to work with Data Science talent embedded in their respective functions. Hence, organisations will continue to focus on building their own Data Science talent.
6. Data Science will need to Prepare for Multi-cloud: Cloud Platform adoption will grow and data science will be applied to consumer data, marketing data, operations data, and supply chain data. Each of these data sets may well reside in different clouds (Salesforce, Google, SAP, AWS, Azure, and Oracle). A robust data architecture which enables data-led decision making will need to manage this multi-cloud environment.
7. Healthcare Opportunity: In this segment, Data Science will be looked at to provide accelerated value. In addition to searching for COVID-19 vaccines and cures, new cases of data science-led use will continue to appear as patients prefer remote consultation. Conversely, remote healthcare on digital mediums is seeing massive acceleration in reach to impact the poor in less well-off nations.
8. Data Science Infusion in Processes: It is a mainstream expectation across enterprises to take data science from experimentation and value discovery to production through use of Machine Learning in very short cycles. Data Science as a PowerPoint slide output is no longer sufficient. Insights and intelligence have to be infused into the everyday processes to make them more intelligent.
9. Need for Explainable AI: With all the good that Data Science and AI are driving, fear around the negative consequences like bias and wilful manipulation is also growing. The ask to make AI and data science more explainable will continue to grow.