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2020 will not be easy for all walks of life, and the new crown virus has changed every aspect of our lives. A major trend in data science caused by the epidemic is the acceleration of integration of data scientists and data-driven analysis with large-scale company operations. To survive and thrive in chaos, data is essential.
This trend is even more pronounced in the retail industry. As one of the most impacted industries this year, the retail industry has begun to turn to data science for solutions.
Data science, or business science, is becoming more and more important, and the data drive of this industry is important to every data scientist. It is not only for people who focus on supply chain, pricing, or other retail-related applications. Data science trends in the retail industry herald the trends of the entire world in the post-epidemic era.
The following are the five major trends that will dominate data science in the retail industry in 2021. Every data scientist must prepare for the new year and respond positively.
1. Automatic analysis and analysis
Almost all businesses are inseparable from big data. Big data has become a necessity in the modern retail industry. The next step is to develop automation. In the coming year, more companies will carry out automated analysis in data collection and analysis. Data quality can only play a role if it is able to use its opinions autonomously.
For many years, I have been committed to promoting the development of autonomous analysis systems. McKinsey pointed out that automation is a key trend in consumer goods and fashion in 2021. Automation is the future of retail, which means that data scientists need to shift their focus to automation models and analysis.
2. AI matures
With the rapid development of artificial intelligence applications last year, 2021 is likely to become a watershed for the maturity of artificial intelligence. In other words, more models will provide the best results expected by the application. According to Gartner’s AI maturity model, many businesses will enter the final two stages, and the application of AI in many organizations will eventually turn to systematization.
For data scientists, the maturity of AI technology is exciting, because AI can act like a real business scientist, summarizing accurate and feasible insights from models. This is also an adjustment to the model development method. Optimizing a mature model is a new challenge that many people will face next year.
3. Agile model and supply chain
The most important thing for us in 2020 is to plan ahead. Whether it is the new crown epidemic, natural disasters, or any crisis to be faced next, it may have unexpected impacts on the supply chain.
A good AI model can automatically digest any relevant information, map micro patterns and micro trends, learn from its own mistakes, and constantly adjust. It can react like humans, while being more efficient and more constrained by verifiable data.
This means that once new data is available, even with limited observations, the AI model can learn and react to the changing environment. The effect is much better than that of a human management team relying solely on Excel and intuition.
In 2021, as retailers plan for the next disaster, the agility of the supply chain will only become more important. Therefore, companies will require data scientists to provide more agile predictive models. Data scientists must prioritize the creation of models that can more flexibly deal with data flaws, model drift, and excessive deviations from historical patterns.
A more agile supply chain will become the first choice of every retailer, and a more agile model will become the ultimate goal of every data scientist serving it.
4. Consumer-driven products and time lag elimination
The new crown virus has officially stifled the traditional, top-down retail sales model. Successful retailers must now let customer preferences and needs drive product supply. Quick response is essential to meet demand. Product life cycles will be shortened and supply chains must be adjusted accordingly.
What does this mean for data scientists? Speed is of the essence.
The model must run faster, the supply chain must be optimized to the second level, and the system must recognize and respond to even the smallest changes in consumer demand. For every development, data scientists must work harder to increase data speed. By 2021, every data scientist will push their model closer to real-time data and analysis.
5. Popularize self-service AI optimization applications
During the epidemic, companies rushed to apply artificial intelligence, and artificial intelligence applications increased exponentially. Despite this, a report by the Boston Consulting Group and MIT Sloan Management Review stated that only 11% of companies using artificial intelligence have achieved a substantial return on investment. why? Because some companies don't know how to use AI effectively.
The epidemic revealed that a major problem with AI is the poor user experience. The average retail executive is reluctant to use tools designed for other data scientists. This is why I support business science: unless your knowledge brings a practical point of view, data science is useless.
In 2021, data scientists will work to design more user-friendly AI optimization tools and applications, and launch more self-service and easy-to-access applications at a higher frequency. Simple optimization tools are no longer a secondary goal, but the main focus of every data scientist designing a model to optimize any element in the supply chain.
In fact, it is for this reason that Evo has made the launch of a self-service portal a priority in 2021. Even small retailers that cannot afford personalized services and data scientists customized models should have the opportunity to optimize their supply chains for competition in the post-epidemic era. In order to keep up with the times in 2021, data scientists must change the way they design applications.
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How to stay ahead of data science trends in 2021
In the coming year, these five trends will profoundly affect the way data scientists work. Now is the time to adapt to these trends. They exist in the entire business community and will only advance and not lag behind. I look forward to 2021 and a post-epidemic world. With the end of the epidemic, expect data scientists around the world to turn these trends into reality. Many data science trends in 2021 will help us become better business scientists.
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