Among the most important pillars of leveraging data science in your organization is to enable your employees to make data-driven decisions. Such a learning journey helps business executives to build the foundation of value creation from data. In the following we will provide a valid example on how to educate your employees in data science. Before we do so, let’s underline the importance of the data science in your organization.

Why Data Science is important

Data Science – AI in particular – is not new. The AI term was first coined in 1956 by Prof. John McCarthy at the Dartmouth conference. AI has taken a long journey since then, but we finally have capabilities to consume it at its full extent and already apply it across many industries. That is why it is an integral part of understanding the working of many industries, however complex and intricate.

There are already many practical examples of what could be achieved by data science implementations to transform, disrupt and innovate organizations and industries (see Figure 1). The question remains, however, how does my organization get to a position to exploit the opportunities in data science.

Figure 1: Sample value cases supporting the data to value journey

Data science will be the key for achieving goals for companies, especially in the coming years. Below are three main points why data science should be implemented in your organization.

Data Science:

  1. helps to understand the main patterns in your demand (within large data sets) and react much better to the upcoming fluctuations. For instance, supply chain planners could proactively increase buffers in anticipation of the seasonal spike in demand or decrease stocks in case of falling demand. This is helpful for identifying trends, which ensures smoothness in operations and ultimately increases the brand value since service levels improve.
  2. substantially improves business decision making. Moreover, it is a field that is constantly growing and evolving. With so many AI solutions being developed, data science is helping companies to solve complex business problems in an effective and strategic manner. Implementing the latest IT know-how and modern platforms ensures a leading position in the market for your business.
  3. can be applied to almost any industry, such as logistics, supply chain, pharma, chemicals, consumer packaged goods – focus industries of Camelot – among others. Understanding the implications of data science can help these sectors in the long run by addressing their challenges in the most effective way.

 How to start a learning journey

As already discussed in the Artificial Intelligence as a Key Enabler for Business Digitalization whitepaper, the workforce empowerment and an active cross-team collaboration are important aspects in the digital value chain transformation. Effective workplace communication is the foundation for innovation and collaboration, and a driving force for companies in this digital age.

One other consideration you need to think of is making sure that people in your company get the opportunity to gain the necessary knowledge and understanding in the area of data science. It is beneficial to not only hire experienced data scientists, but also grow the capability within your own staff members, who know and understand your data and your business. In the end, they are able to make data driven decisions and will have a massive impact on how successful your company will be.

Furthermore, the data science learning journey would be relevant not only for your IT team, but also your leaders. To ensure that your learning path and selected courses on the data science are leading to more efficient outcomes and measurable successes, you must follow the following principles:

  • Always be up-to-date and follow the latest trends in AI & Data Science.
  • See the AI learning journey as a project. Having a strategy for the project and the contents of future learnings will narrow your focus to those areas where there is a need for AI as well as certain tools and techniques to apply it.
  • Apply the subject matter knowledge that helps to define the problem, assess the relevant data, guide data analysis and interpret results.

Camelot is already leveraging a data science hub where the focus lies on scaling up the skills in data science and data-driven technologies. Achieving a worldwide impact, we need to train and retain our best talents, while new digital talents have to be hired. This is on the agenda of many companies, and it is particularly difficult to develop experienced data scientists who require a mixture of skills.

Important: Data Science trainings

For Camelot’s aspiration, a data scientist requires a strong focus on value chain topics, enterprise IT know-how and the link to advanced analytics, Machine Learning, and AI. Thus, we have designed and enabled an attractive learning program dedicated towards the field of value chain management & AI targeted towards different groups of our staff – from IT to C-level executives. We follow three guiding themes when designing the individual learning paths for our staff (see Figure 2).

First, we need to properly communicate a vision from different perspectives. Here we put emphasis on the active cross-team collaboration and participation in AI knowledge trainings. Second, learning journey requires understanding the Data Science essentials combined with practice. As such, the best way to teach applied data science is learning-by-doing, which means during the real customer projects. And lastly, we actively seek for measurable value and evidence driven decision making when facing the customer.

Figure 2: Three guiding themes of Camelot AI Hub

The future of nearly all businesses is all about coping with digital transformation. New information technologies, AI applications, and operational technologies are exploding around us and bringing rapid change to what is possible in business. No one should expect business as usual for much longer. Thus, you must ensure that both your company and employees are able to create value from data.

We would like to thank Frank Kienle for his valuable contribution to this article.

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