Research directions in data wrangling - Microsoft Research 2026

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  1. Click ‘Get Form’ to open it in the editor.
  2. Begin by reviewing the abstract section. This provides an overview of the research directions and key challenges in data wrangling. Take notes on any specific areas that resonate with your work.
  3. Move to the 'Keywords' section. Here, you can highlight important terms related to your research focus. Use our platform's highlighting tool for easy reference.
  4. In the main body, identify sections discussing data quality issues. Utilize text boxes to annotate your thoughts or questions directly on the document.
  5. As you progress through each section, use checkboxes or radio buttons available in our editor to mark completed readings or tasks related to each research direction.
  6. Finally, save your annotations and modifications by clicking ‘Save’ before exporting or sharing your filled form for further collaboration.

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Also known as data munging, it involves tasks such as handling missing or inconsistent data, formatting data types, and merging different datasets to prepare the data for further exploration and modeling in data analysis or machine learning projects.
Data wrangling is the process of cleaning, structuring and enriching raw data to be used in data science, machine learning (ML) and other data-driven applications.
Data Analytics focuses on interpreting and deriving insights from data to support decision-making, while ETL is essential for preparing and structuring data for analysis. Each function has its unique importance, and their integration can docHubly enhance the efficiency of data-driven processes.
Data wrangling is the process of transforming and structuring data from one raw form into a desired format with the intent of improving data quality and making it more consumable and useful for analytics or machine learning. Its also sometimes called data munging.
Data wrangling is the act of extracting data and converting it to a workable format, while ETL (extract, transform, load) is a process for data integration. While data wrangling involves extracting raw data for further processing in a more usable form, it is a less systematic process than ETL.

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Below, we are going to take a look at the six-step process for data wrangling, which includes everything required to make raw data usable. Step 1: Data Discovery. Step 2: Data Structuring. Step 3: Data Cleaning. Step 4: Data Enriching. Step 5: Data Validating. Step 6: Data Publishing.
Data Cleaning is an important part of the overall ETL process. It is the process of analyzing and identifying relevant data from the raw organizational datasets to make security decisions. Data Cleaning in an ETL process ensures that only high-quality data passes through and loads into Data Warehouse.

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