Cloud Data Quality

Cloud Data Quality – Editor’s Note: Today’s post is from Trifacta’s Bertrand Cariou and presents some steps you can take in Dataprep to clean up your data for later use in your analyzes or when training machine learning models. Data quality is a critical part of any analytics and machine learning initiative, and unless you’re working with old, heavily controlled data, you’re likely to encounter data quality issues. To illustrate the process of converting unknown, inconsistent data into reliable values, let’s use the example of a forecasting analyst in the consumer staples retail industry. Forecast analysts must be extremely accurate in planning the exact scope of adjustments. Supplying too much product results in a waste of resources, while supplying too little product means they risk losing profits. In addition, an empty shelf puts the consumer at risk of choosing a competing product, which can be detrimental to the brand in the long run. Finding the right balance between reasonable product inventories and razor-thin margins requires forecasting analysts to refine their analysis and forecasting, using their own internal data and third-party data over which they have no control. Any business partner, including suppliers, distributors, department stores, and other retail businesses, may provide data (such as inventory, forecasts, promotions, or historical transactions) in different formats and levels of quality. A company may use pallets instead of cartons as a unit of storage, use pounds versus kilograms, use different brand names and designations, use different date formats, or have multiple product SKUs that are internal and external. There are combinations of other suppliers’ ID cards. . Also, some data may be missing or entered incorrectly. Each of these data issues poses a significant threat to reliable forecasts. Predictive analysts must cleanse, standardize, and gain confidence in the data before they can accurately report and model it. This post provides an overview of the key techniques for cleaning data with DataPrep and introduces new features that will help you improve your data quality with minimal effort. Basic Concepts Cleaning data with Dataprep resembles a three-step process: Assess the quality of your data to resolve or fix issues, validate the cleaned data, scale DataPrep consistently with that data You open the grid interface. And start preparing the data. With Dataprep’s real-time active profiling, you can see the impact of each data cleansing step on your data. The resulting profile is summarized in column headings with key data points to show important characteristics in your data in the form of an interactive visual profile. By clicking on one of these profile column headers, Dataprep will suggest some changes to correct random or missing values. At any time you can try a change, evaluate its effect, select it or tweak it. You can always go back to a specific previous step if you don’t like the result. With these basic concepts in mind, let’s cover Dataprep’s data quality features. 1. Assess the quality of your data Once you open a dataset in the grid interface, you can access data quality signals that can help you assess data issues and clean up the data.

Quick Profiling You’ll likely scan your column headers and identify potential quality issues to understand which columns require your attention. Based on data types based on random values ​​(red bars), missing values ​​(black), and uneven value distribution (bars), you can quickly see which columns require your attention. In this particular case, our predictive analyst knows that he needs to drill down on the Material field, which has some strange and missing values. How should these data constraints affect its forecasting and replenishment model? Intermediate Data Profiling If you click on a column heading, some additional statistics are displayed in the right pane of DataPrep. This is especially useful when you expect a specific format standard for a field and want to identify non-standard values. In the example below, you can see that Dataprep recognized three different format patterns for order_date. You may have follow-up questions: Can empty order dates be forecast? Can incorrect data be corrected and how can you correct it?

Cloud Data Quality

Cloud Data Quality

Advanced Profiling If you click “Show More” or click on the column header menu and “Column Details” in the main grid, you will be taken to a comprehensive data profiling page with some details about unique values, value distributions or info out there. You can also navigate to the Pattern tab to find the data structure in a specific column.

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These three data profiling capabilities are dynamic in nature, in the sense that DataPrep reproduces data in real-time at each stage of a transformation to always present you with the most up-to-date information. This helps to clean your data faster and more efficiently. The value for the predictive analyst is being able to make immediate corrections as they go through the process of cleaning and transforming the data to be in the format their downstream users expect them to be. 2. To solve data quality problems, dynamic profiling helps you to assess the quality of the data at hand and it is also the entry point to start cleaning the data. Chart profiles are interactive and suggest changes as you interact with them. For example, if you click a field with missing values ​​in a column header, suggestions for changes such as deleting values ​​or resetting values ​​to default values ​​appear.

Fixing Incorrect Patterns You can efficiently fix incorrect patterns in columns (such as repeated date formatting issues in sort_data columns) by accessing the Patterns tab in the Column Details screen. Dataprep shows you many examples. Once you have selected a target conversion format, DataPrep will show some conversion suggestions in the right pane and convert all data to fit the selected patterns. Watch the animation below and try it yourself:

Highlight data content Another interactive way to clean your data is to highlight part of the value in the cell. Dataprep suggests a series of changes based on your selection, and you can refine the selection by highlighting some additional content from another cell. Here’s an example that subtracts the month from the order date to calculate each month’s quantity:

Format, Replace, Conditional Functions, and More Many of the functions you use to clean up data can be found in the Format or Modify section of the Column menu, or in the Conditional Formulas toolbar shown below. All of this can be useful for converting product or category names to upper case or shortening names that are commonly referenced after importing from a CSV or Excel file.

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The extract function can be particularly useful to extract a subset of values ​​within a column. For example, from the product ID “Item: ACME_66979905111536979300 – PASTA RONI FETTUCINE ALFR” you want to extract each ingredient by dividing it by the value “-“.

Conditional functions are useful for flagging values ​​that are out of range. For example, you could write a formula that flags a record when a quantity is over 10,000, which wouldn’t be accurate for the order sizes you typically encounter. If none of the visual suggestions give you what you need to clean your data, you can always change a suggestion or manually add a new step to the dataprep recipe. In the search box, type what you want to do and Dataprep will suggest some changes that you can then edit and apply to the record.

Standardization Standardization values ​​is a method of grouping similar values ​​into a single, consistent format. This problem is particularly common with free-form entries for products, product categories, and company names. You can access the quality feature via the column menu. In addition, Dataprep can combine similar values ​​individually or by pronunciation.

Cloud Data Quality

Tip: You can use a mix-and-match standardization algorithm. Some values ​​can be standardized using orthography, while others are more sensitively standardized using international pronunciation standards. 3. Large-Scale Validation The final, critical step of the typical data quality workflow in Dataprep is to verify at scale that there is not a single data quality issue in the dataset. Using Patterns to Clean Data Sometimes the entire set of a record doesn’t fit in your browser tab in Dataprep (especially when using BigQuery tables with millions or more records). In this case, Dataprep automatically samples the data from BigQuery into your local computer’s RAM. This potentially leads to the question: how can you ensure that you have standardized all of the data from one column (e.g. product name, category, region, etc.) or that you have deleted all of the date formats from the other? ? You can customize your sampling settings by clicking the sampling icon in the top right and choosing the sampling technique that best suits your needs. Select Random to leave all data for one of multiple columns random or missing

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