What is Power Query and how is it used in analytics?

From Excel to Sharepoint, the Microsoft ecosystem is both vast and detailed. One application that has grown in significance in recent years is Power Query, an all-in-one data pre-processing interface that affords a user the capability to seamlessly edit the structure of a dataset and arrange data to fit their objective.

With the growth of unstructured and structured data engineering as a discipline, pre-processing the data you wish to use is a necessary and often cyclic step for analytic workstreams. Power Query expedites the data engineering process by simplifying these datasets whilst also allowing flexibility to clean the data with low-level programming language ‘M’, in other words without requiring advanced coding skills.

Power Query is a data transformation and query tool that is part of Microsoft Power BI and Excel. It allows users to connect, transform, and reshape data from various sources before importing the data into a spreadsheet or a visualization tool like Power BI.

With Power Query, users can perform various data transformation tasks like removing duplicates, filtering data, merging or appending tables, splitting columns, and pivoting data. It also has a rich set of functions for data cleaning and formatting, such as removing empty rows or columns, replacing null values, and converting data types.

Power Query can be used in analytics to clean and prepare data for analysis, saving time and effort compared to manually manipulating data in Excel. It can also help to automate data preparation and transformation tasks, making it easier to update and refresh data from different sources. By using Power Query in analytics, users can focus more on data analysis and insights instead of spending time on data preparation.

How is Power Query used?

PowerQuery sits between the data ingestion and dashboarding process, often used in PowerBI but also within Excel. The tool can be split into two layers of pre-processing: a foundational layer and a complexity layer.

• The foundational layer involves ingesting the data sources, using the data sources tab, we can use generate datasets from a vast number of data sources, design the connectivity requirements and arrange the data to suit requirements.

• The complexity layer is where data can be further generated and arranged with greater granularity using the programming language M. This allows the end user’s calculations and formulae to be easily generated and automatically applied to a dataset, minimising manual workload. Additional uses can be incorporated from PowerBI or the cloud hosting service.

What are the pros and cons of Power Query?

Pros

1. User-friendly: Power Query is a very user-friendly tool that allows users to easily transform and merge data from different sources, making it a great option for users without advanced technical skills.

2. Time-saving: Power Query can save a lot of time compared to manual data transformations. This is because it automates many of the repetitive tasks involved in data cleaning and transformation.

3. Reusability: Once a data transformation is created in Power Query, it can be easily reused for other data sets or updated with new data.

4. Flexibility: Power Query offers a wide range of data transformation options, including merging, filtering, grouping, and pivoting, which can be customized to meet specific needs.

5. Integration: Power Query integrates well with other Microsoft Office tools, such as Excel and Power BI.

    

Cons

1. Learning curve: Although Power Query is user-friendly, it can take some time to learn the interface and the various transformation options.

2. Performance: Power Query can be slow when dealing with large datasets or complex transformations.

3. Limited functionality: Power Query has some limitations when it comes to advanced data transformations or working with certain data sources.

4. Compatibility: Power Query is only available in certain versions of Microsoft Excel and not available in other data analysis software.

    

When is it used?

As a cornerstone of PowerBI, QuantSpark frequently deploys Power Query within dashboard development for our clients, alongside building cross-analytic capabilities through merging data sources and automating complex formulae.

PowerQuery is a straightforward way of bringing a hardcore calculation capability to PowerBI in Excel without needed to code that calculation into a Python script. It can also be a good way for teams to create a temporary prototype to test the solution, taking care not to build permanent processes on top of a temporary fix.

Practically speaking, PowerQuery has helped our clients:

• Analyse the performance of marketing channels alongside customer spending by merging into different datasets and generating cost-per-order calculations

• Identify user trends within KPI reports involving creating live connections to excel reports

• Generate dynamic time series reports on financial performance by developing advanced filtering on underlying data