There is a stereotype that only IT departments, programmers and mathematicians work with big data. In fact, this young industry includes quite a few professions: from an engineer to a data storytelling specialist. As part of the Masters of Future special project, together with IE Business School T&P, we talked with Joseph Curto, analyst, business consultant and entrepreneur, about multifunctionality, the ability to influence global processes and big data in agriculture.
Big Data Specialist – Who Is This?
Of course, it is difficult for one person to know everything at all, so we most often work in teams – this is much more productive. For example, a colleague of mine is only a data visualization and storytelling specialist. She’s an amazing infographic that can tell any story in numbers. The main thing is to have a 360-degree angle of view, which comes with experience. It took me almost 15 years to do this myself.
What is the best background to have if you want to work with big data?
There are many different roles in data science analytics consulting: for example, you can be a Big Data Engineer (that is, an engineer) or an analyst, and these are completely different functions. Basic things are knowledge of mathematics, statistics and computer science.
Data Science Specialist Tasks
The tasks differ from company to company. In large corporations, a datasheet specialist works in several directions. For example, for a bank, he can solve the problem of credit assessment and engage in speech recognition processes.
The stages of work on a task for datascientists from different fields are similar:
- clarification of customer requirements;
- solving the fundamental question “Is it advisable to solve the problem using machine learning methods?”;
- preparation of data, their markup;
- adoption of metrics for evaluating the effectiveness of the model;
- developing and training a machine learning model;
- protection of the economic effect from the implementation of the model;
- implementation of the model in production processes and products;
- accompanying the model.
Each new iteration allows for a better understanding of the business problems, clarification of the solution. Therefore, each step is repeated over and over to develop the model and update the data.
The next step is making a list. We are discussing the future strategy of the company. The implementation of Big Data is not just about hiring one specialist, it is a change in the mindset of all employees. It is very important that everyone understands what the guy who calls himself a Big Data specialist is doing. It is very important to dispel the myth that Big Data is just some part of the IT department. After defining the strategy, we suggest ways to implement it.
Read more info here: https://data-science-ua.com/ai-development-company/
Resiliency is central
Organizations must seek new approaches to maintaining a competitive advantage in a volatile environment. As you know, life is a stable state of instability, that is, it is impossible to create a system once and for all, put it into operation and constantly use the results of its activity. Companies will need mechanisms to ensure this constant state of volatility – digital resiliency. IDC experts predict that already in 2022, enterprises focused on digital resiliency will adapt to disruptions 50% faster than enterprises focused on restoring existing business patterns and the status quo. As in real life, and in information systems, digital resiliency cannot be ensured without artificial intelligence, which makes it possible to assess how the current situation differs from the one before the crisis, and in which direction you need to move in order to come to a new equilibrium state.