The process of extracting, transforming, and loading data is a challenge as old as information technology itself. Both business users and IT professionals have historically struggled with the challenge, and numerous software tools have been developed over the years to help deal with the challenge such as SQL Server Integration Services (SSIS) and Alteryx.

However, many of these tools were complex and not easily portable. The M language and Power Query were created to help solve these issues.

While there might be some more speculative history regarding the origins of M, we can at least definitively trace M back to a project originally code-named Data Explorer. Data Explorer was an Azure SQL Labs project circa 2011 that aimed to simplify the process of accessing, cleaning, and preparing data from various sources. The query language was thought of as a mashup language (hence the M for mashup).

In 2013, Microsoft released Power Query as an add-in for Excel. Power Query introduced a user-friendly interface, allowing business users to perform data transformations via a visual editor. Behind the scenes, Power Query utilized the M language as the underlying formula language to drive the data transformations, and as such, these data transformations became repeatable. Instead of, for example, business users continually performing the same manual data transformations on source data received as comma-delimited files, that process could now be effectively automated.

Following the success of Power Query in Excel, Microsoft included Power Query as part of its new product, Power BI Designer, which eventually became Power BI Desktop. As Power Query gained popularity, there was a need to standardize the underlying formula language. In 2016, Microsoft submitted the Power Query Formula Language specification to the European Computer Manufacturers Association (ECMA), an international standards organization. This effort established a formal specification for the language, ensuring compatibility and interoperability between different implementations.

While the language was formally referred to as the Power Query Formula Language, it became commonly known as M among the user community. The informal name M gained widespread acceptance and is now widely used to refer to the language.

Microsoft continues to enhance and refine the M language as part of its ongoing investment in data integration and transformation technologies. New functions, features, and improvements are periodically introduced to provide users with more powerful and efficient ways to manipulate and prepare their data. In addition, Microsoft continues to introduce M within additional software tools and platforms, such as data integration within Microsoft Power Platform and dataflows within Power BI and Fabric.

Today, the M language is a key component of Microsoft’s data transformation and integration toolset. The proliferation of M, as well as its versatility and extensibility, make it an invaluable language for today’s modern data professionals.

Let’s now turn our attention to who should learn M.

M is a powerful tool for data professionals and individuals who work with data on a regular basis. The versatility and capabilities of M make it a valuable language to learn for various roles, including the following:

• Data analysts: Data analysts who deal with data extraction, transformation, and preparation tasks can greatly benefit from learning M. It provides a comprehensive set of functions and operators that enable data analysts to efficiently shape and manipulate data from diverse sources. By mastering M, data analysts can automate repetitive tasks, handle complex data transformations, and ensure data quality, leading to more accurate and reliable data analysis.
• Business intelligence professionals: Professionals in the business intelligence (BI) space can greatly enhance their skills by learning M. It is a core component of Power BI. By understanding M, BI professionals gain the ability to connect to various data sources, perform complex data transformations, and create reusable data preparation workflows, enabling them to provide actionable insights and drive informed decision-making.
• Data engineers: Data engineers involved in the design and implementation of data pipelines and data integration processes can really benefit from learning M. It allows data engineers to efficiently extract, transform, and load (ETL) data from different sources into data warehouses or data lakes, specifically within Power BI and Microsoft Fabric. M also provides the flexibility and power to handle complex data formats, define custom transformations, and create efficient data processing workflows. By mastering M, data engineers can streamline data integration processes and ensure data consistency and quality.
• Data scientists: Data scientists who perform exploratory data analysis, model development, and advanced analytics can leverage the capabilities of M to efficiently prepare their data. M provides a robust set of functions for cleaning, shaping, and aggregating data, allowing data scientists to focus on the analytical aspects of their work. By incorporating M into their data preparation workflows, data scientists can streamline the pipeline of turning raw data into insights, spending less time on data cleansing and preparation and more time on data modeling and analysis.
• Power users: Power users in Excel and Microsoft’s Power Platform who work extensively with data and perform complex data manipulations can benefit from learning M. It is integrated into Excel through Power Query, empowering users to perform advanced data transformations within the familiar Excel interface. In addition, M is integrated into the Microsoft Power Platform via data integration, allowing data to be transformed and mapped between systems. By mastering M, Power users can expand their data manipulation capabilities, automate repetitive tasks, and enhance the accuracy and reliability of their analyses.
• Individuals in data-driven roles: Beyond the specific roles mentioned above, individuals in various data-driven roles, such as project managers, consultants, researchers, and domain experts, can benefit from learning M. Mastery of M provides the ability for individuals in these roles to independently handle data-related tasks, extract meaningful insights, and make informed decisions based on reliable data.

M is a valuable language for a wide range of data professionals and other individuals who work with data. Whether you are a data analyst, BI professional, data engineer, data scientist, power user, or someone in a data-driven role, learning M empowers you to efficiently ingest, transform, and prepare data for analysis.

Now that we understand what types of individuals would want to learn M, let’s next explore where those individuals can leverage their mastery of M.