Article
Microsoft Fabric: A new Microsoft offering to empower the modern data workforce
May 24, 2023 · Authored by Chris Wagner
The traditional data workforce
When discussing the structure of data related work, the data landscape is often categorized into one of three areas – data engineering, data analytics and data science.
- Data engineering – the process of designing, building and maintaining the infrastructure required to manage and process large volumes of data.
- Data analytics – the process of analyzing and interpreting large volumes of data to identify trends and insights that can be used to inform business decisions that can help organizations optimize operations, improve products and enhance customer experience.
- Data science – the process of using scientific methods, algorithms and systems to extract insights from structured and unstructured data to analyze and interpret complex data sets and solve real-world problems.
Traditionally, each of these categories would have their own set of data workers that play separate and unique roles in the data management landscape of an organization. Having an abundant knowledge of the data landscape allows these professionals to understand how to build out and use the advanced tools necessary to properly collect, manage and analyze the data for their organization.
And while data engineers, analysts and scientists are still the reigning force throughout the data landscape, workers in other functional areas have begun to engage with their organization’s data in a way many have never done before. People working in a variety of nontechnical roles, such as accounting, sales and human resources, have begun to leverage forms of data work in their daily job functions, with some even taking the next step to leverage low code/no code design solutions.
The utilization of data by nontechnical workers empowers your entire organization to make faster, more efficient and more informed decisions. So how can your organization make working with data more accessible to those outside of the traditional data landscape? How can you ensure nontechnical workers know what tools to use to accomplish their variety of tasks while ensuring data is being accessed responsibly in a platform that can be managed and governed?
