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Companies need to acquire new skill-sets in order to be competitive

The physical entry exam into the British army in the early 1900s was so difficult that only 5% of current soldiers were able to pass it. That comes as no surprise, because as wars change so do the weapons, and consequently, soldiers’ training and skill set. The same has happened to the way we do business and the strategies companies use to get competitive advantage; requirements and skill-sets have changed, and are in fact changing more rapidly than ever before. 

If your company is not employing data scientists you’ll soon be in for an unpleasant surprise. Data scientists are about to become your most important soldiers. Here’s why.  


This is a fact many business leaders don’t realise. Data is basically the documentation of business processes, facts, outcomes of different programs, and whatever actions the company is undertaking. As of now, companies are collecting more data than ever before from almost all organizational activities, and much of this data is not used to its full advantage. In order to stay competitive, information is key and it’s critical to understand what your data is telling you. In order to do that, a certain skill-set is required.  

Data scientists collect all data, clean it and organize so it becomes analyzable. Then they use programming languages such as R and Python in order to transform the data into information, and then load the information into one or more tools like Microsoft Power BI for generating understandable and actionable information, i.e. information organizations can use to make real business decisions.

Data scientists can explain past business processes and actions. Furthermore, they can use historical data and make accurate predictions about the future and accurate demand forecasts that can streamline your supply chain. All this information creates transparency and can identify areas for improvement and optimization. More importantly, this information can be critical for the company’s success, but without the data scientist the decision makers would never have known. Hence the saying, “you don’t know what you don’t know”.  


More and more companies are adopting artificial intelligence to work faster, cheaper, and smarter. Obviously, the organizations that don’t do it will soon start lagging behind. But to start using machine learning techniques, such as deep learning and regression trees in your business, you obviously need people who know how to maneuver within this new world of complicated technology. Your IT guy is not trained to do it, despite his extensive IT skills and education. Data science is a relatively new phenomenon and data scientists are educated and trained specifically to deal with the science of data. 

Organizations are taking notice and in the 2020 LinkedIn Emerging Jobs Report the top emerging job in the US is Artificial Intelligence Specialist. And what are the skills unique to such a flamboyant title one might wonder? That is Machine Learning, Deep Learning, TensorFlow and Python, and Natural Language Processing. And what sort of people know all that? Yes, you guessed it right: Data Scientists. 

In the 2019 LinkedIn Most Promising Jobs report Data Scientists took the number 1 spot. This year Data Science is the number 3 top emerging job in the US where the skills detailed as unique to the job are Machine Learning, Data Science, Python, R, and Apache Spark. Obviously, since data scientists are taking both number 1 and number 3 top emerging jobs in the US, they have become the most sought after employee. No wonder Harvard Business Review called data science the “sexiest job of the 21st century”. 

Just this should have organizations that don’t have any data scientists to stop and wonder why all the others are employing those sexy scientists. The reason is quite simple: Most companies are already adapting towards a rapidly changing business environment where data and information is the most powerful weapon companies can utilize to stay competitive. 

It should be obvious to anyone that with increased demand the salaries increase also. LinkedIn reports that Median Base Salary for Data Scientists in the US are $130,000 so obviously it’s not for all companies to add such a high-paying role. But then again, it’s not necessary for companies to directly employ data scientists themselves. There are plenty of data science agencies that offer data services for a fraction of the cost of hiring or setting up this sort of department in-house.  


If your organization is not employing directly or indirectly data scientists, chances are that your company is missing out on some major cost saving opportunities and increased revenue. 

As an example, the food processing industry is a rather low margin industry, averaging around 5%, and therefore lean operations can be critical for success. A major food manufacturing company in Spain has five different production facilities with a total of 14 independent production lines. Everytime they experience a machine breakdown they start losing money; the whole production line stops and dozens of workers wait and do nothing while the maintenance department fixes this one machine that stopped.

By hiring data scientists they were able to use artificial intelligence in order to predict what machine was likely to stop next, and when. Furthermore, the maintenance department was provided with a prioritized list of machines likely to fail which gave them the tools to maintain the right machines before they actually stopped. This reduced machine breakdowns by 55% organization wide and saved the company millions of euros. 

Another example is a Spanish steel company with a number of production facilities in Europe. They need to secure raw materials, they need to produce well ahead in time for different clients, and they need to warehouse both raw materials and finished goods. The problem they experienced was lack of demand forecasting, i.e. they had problems with buying the right raw materials at the right time and producing the right quantity at the right time, and more importantly, distributing the right quantity to the right warehouses across Europe. 

By hiring us at Sumo Analytics, they got extremely accurate demand forecasts and supply chain optimization tools that allowed them to buy more accurately the different raw materials, both in terms of quantity as well as at the right time, i.e. when the price is good. Secondly, they could plan their production well ahead and therefore significantly lower production costs. And thirdly, they were able to plan inventory levels across multiple geographical locations with the highest accuracy possible and lower their inventory and logistics costs tremendously. 

A medium sized olive oil company in Seville, Andalucia, has utilized multiple analytical techniques in order to streamline its operations with the aim of lowering costs. Data science techniques were applied to their crops, their procurement, production, distribution and sales, and within 12 months they had managed to increase their profit margin by 23% and were already outperforming their competitors in various fields.


Data science is opening up enormous opportunities for companies to transform their business operations with the aim of lowering costs, optimizing supply chain, increasing revenue and becoming more competitive, overall. It’s not just something for large enterprises, but more and more SMEs are employing data scientists as well. The benefits of competing on analytics is simply too much to ignore. 

Interested in learning more about how data science and analytics can help your company? Get in touch! We’re happy to help.

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