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Strategic moves to win post COVID-19 with Data Science 

The COVID-19 pandemic has shaken up 2020 for many. This year marks a period where uncertainty is suddenly much more common than certainty and it seems like it’s here to stay.

In business, we see that although some thrive in uncertainty, many more require thorough recalibration to many possible variants of the new normal. In the field of Data science, we thrive in uncertainty while we rely on statistics and AI as a golden compass to navigate through the fog. In the fog, we are dealing with changing, dynamic consumer demand, while physical customers are more distant than ever before. This counts for many industries and markets: supply chains will operate at a different pace, and capacity management in hospitals is a completely different ball game than before. Having a strong data science capability will be of value to adapt and thrive as a business. For some that means starting at absolute zero, for all it will mean that making the right moves is essential.

This article gets you going with the five key strategic moves to win in the post COVID-19 game. Furthermore, it lists several must-know techniques and data science terminology that will kickstart you into having the right conversation with your lead data scientist.

1) Gather the troops and move into formation

Even if you already have a team in place, now is the time to review their positioning. Often, the impact of the team is optimal when positioned close to the board room where strategic decision making takes place. From there they can be deployed on high-impact projects where data science will be of additional value besides traditional analyst and BI roles.

Last but not least, educate all within your company on different levels of data literacy. Look inside the company for anyone that can be retrained now that internal and external demand is shifting. Leverage scale and what is already there, MOOCs deliver great value at a small investment. Build a culture that finds its foundation in data and mathematic reasoning, by demanding insight as key ingredient (instead of just some seasoning) of decision making. The post-COVID-19 world will be different from the one we knew, so make sure your people leverage data to act on now instead of experience in a no longer relevant pre-COVID-19 world.

2) Plug in the data exhaust

So what does this mean? It means that today’s cross-department data should be available for your data science team, and the rest of the organization. Democratization of the data that you already have will increase transparency and enhance collaboration over departments. To make that work in terms of technology, move (part) of your data to the cloud and consider NoSQL solutions like Apache Cassandra or MongoDB to make your data available to many interfaces. Furthermore, actively search and uncover for ‘dark’ data that is only available to some, or never tapped into despite being extremely valueable. Last but not least, measure quality and optimize for speed in your data infrastructure such that applications & data science models generate timely insights that make a high impact.

Additionally, we see that COVID-19 changes society and individual values and norms. To stay ahead, you will need to rethink what data to consume and record within the company and experiment accordingly. Do not be evil, but rethink and tune your ethical approach on data collection to improve your products and services constantly to the ‘new normal’.

3) Look beyond the fog

First of all, make sure that your models are properly implemented. The business and your data science team need to know as soon as possible when accuracy drifts. Suggest your lead data scientist to implement automated retraining of models using solutions such as Kubeflow, Azure ML, or AWS Sagemaker. Although some human intervention may sometimes be required, it ensures that your models are updated regularly using the latest data.

Second, implement and apply models that require less data or do not need any data. With low data, use simple machine learning models to avoid inaccurate models by e.g. overfitting. Overfitted models fit very well to a small training dataset, but also feature relations that are non-existent in the real world. Talk to your data science teams about RidgeRegression, KNN, or Naïve Bayes to work with less data.

Third and last, consider generating scenario data that might replicate the future ahead. Together with the business, your data science team might be able to generate several future scenarios. If your data science capability is more advanced, support these scenarios with data by generating the data using a generative deep learning technique (GANs). Previously GAN-driven synthetic data only seemed applicable in the areas of renewables¹, but the lack of data that COVID-19 causes will propel this novel application. Going forward, you can leverage machine learning to determine which scenario resembles the current state of your company, and thus gives the most accurate predictions about the future.

4)  Automate for efficiency

Data science powered decision-making helps cutting costs by shifting FTEs away from repetitive tasks to more areas where they can be more valuable. Furthermore, it allows you to scale operations faster, which may be beneficial to sales & operations teams needing to outrun your competitors. Automation is a marriage between technology and data science, but beware of the complexity monster. Machine learning is not a silver bullet, most automation problems are solved for 90% by simple data engineering or by evaluating decisions using simple business rules. If you are looking for tools that are strong in workflow automation, discuss the implementation of Airflow, or look into the new-kid-on-the-block: Prefect.

Automating can be a safe bet if focused on long hanging fruit, where the business case is strong. The complexity is most often in the open identification of these cases. Furthermore, make sure that you educate your staff, and that best practices are being shared. Finally, make sure people win in automating their own responsibilities. Only then they will not fear to lose influence and their joy of work, which is a must in making automation work post-COVID-19.

5) Build high quality distant customer services and relationships

You can strengthen your digital relationship by interacting on a more personal level with your customers, powered by data science. Practically this may mean predicting which services and products will match personal needs or optimizing availability to specific times and locations. Or, communicating in the language that appeals most to your customers. By plugging in the exhaust (step 2) on customer data your data science team will be able to segment customers using clustering techniques. Going forward, you can apply carefully set up A / B experiments to quickly learn about and optimize online services.

Getting this to work means querying data science models in real-time e-commerce like conditions. Being fast is crucial. In order to do this, you will need streaming analytics to process and evaluate data in motion, while events are happening⁴. Streaming analytics opens up applying data science models in environments where we were too slow previously, waiting for the data to arrive or the model to generate an answer. Although the streaming analytics set-up of today is more complex than a traditional set-up, this is definitively an area to watch and apply where no other solution will work.

What you should be doing next

In conclusion, evaluating how each of the five steps can be relevant to your company and position can be of great help to navigate these challenging times. Each step should inspire you in some way and generate material for discussion within your company. We’ve written this article generate more questions but especially to foster discussion. As a first step you might want to reconnect with your lead data scientist to explore next steps. We’re happy to facilitate if you need help.

Furthermore, we look forward to hearing what your experiences will be in a pursuit to drive success using data science in a post-COVID-19 world. Good luck!

Contact Tobias Platenburg E:

This article has also been published on Towards Data Science 


[1] C. Jiang et al., Day-ahead renewable scenario forecasts based on generative adversarial networks (2020), TechrXiv

[2] Walter Frick, How to Survive a Recession and Thrive Afterward (2019), Harvard Business Review May-June 2019 issue

[3] Jasper van Rijn, The breaktrough of online shopping as the new standard (2020), IG&H blog series: How Retailers can rebound from the Corona crisis

[4] Databricks, Streaming Analytics (viewed 2020), Databricks Glossary


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