If you really want to develop data expertise in your business and believe that data could be your future competitiveness, it’s time to really acquire expertise right now. Because data is data, regardless of company and industry, it means that the number of data experts will grow globally but unfortunately slower than their need. U.S. The Bureau of Labor Statistics predicted that by 2026, the need for data professionals will increase by as many as 11.5 million jobs (from 2020), and on last year IBM found 700 000 data related jobs open worldwide.
How do you know what kind of data-making skills you need when the experts are under the rock and there are many different roles? A brief summary of the different roles needed to implement a data strategy is below, and even if you don’t need all of it, it’s good to have a vision of what is needed now and what may be in the future. If data is a key asset for your company in the future, you will not be able to manage it with the resources that are then available in the market, but strategic recruitment and needs analysis must begin now.
Data Analyst: Everyone knows what Data Analyst does. His/her job is to analyze and visualize the available data.
Data Engineers: One of the key roles in designing and developing technical solutions for how data is collected, managed and utilized in the various functions and needs of your company.
API architect: In order to have a sufficient amount of data for your use and from appropriate data sources, API architect has a key technical role to play in this. Even without this role, it is difficult to be involved in the API economy.
Data Product Owner: Just like any other service sold, data services and data products sold in the data economy must have an owner who is responsible for their life cycle, development, and everything else.
Database Administrator: A technical person who ensures that data models are also implemented on a practical level in the data warehouses and databases of different systems.
Machine Learning or Artificial Intelligence Engineer: A person tasked with developing and implementing machine learning or AI models for business needs.
Data Scientist: an analytics expert who analyzes data to form trends and insightful perspectives.
Information / Data Architect: A role whose importance we cannot stress enough. The central task of this role is to model the entire data model structure into a single derivable entity.
Statistician: a professional who uses a variety of statistical methods and models to model a company's business.
Business Analyst: His/her core value is to analyze business data and provide a different understanding of key business metrics.
A few roles may be missing from the list and the list does not mean that the right people could not overlap multiple tasks. Perhaps more important is understanding how much to do with holistic data making and thereby taking adequate professional resourcing seriously.