How can you effectively clean data for Machine Learning in human resources?

When beginning a data analysis or machine learning project, it's crucial to identify your data sources and types. This involves determining where your data originates and categorizing it based on its structure and content. For instance, your data may include structured information from HR systems like employee records or performance reviews, as well as unstructured data from sources like resumes, social media, or feedback. Identifying data sources and types helps you choose the appropriate tools and methods for accessing, extracting, and transforming your data to support your project's objectives.

Effectively cleaning data for Machine Learning in human resources begins with a technical exploration of data sources and types, an insight emphasized in expert discussions. Understanding the diversity of data originating from sources like HRIS or recruitment platforms requires a nuanced approach. Technical expertise in data profiling, using tools like pandas or SQL queries, aids in uncovering anomalies. Ensuring consistency in data types and formats is paramount for preprocessing. This technical acuity, as shared in expert discourse, forms the foundation for a meticulous data cleaning strategy, essential for fostering accurate and reliable machine learning models in human resources applications.

With HR data it is critical to consider the data privacy laws that govern any use of the data. This may introduce the need for additional layers of security such as separate databases that hold data prior to processing.

Effectively cleaning data for Machine Learning in human resources necessitates a thorough exploration and understanding of the dataset's intricacies, a technical endeavor discussed in expert circles. Insights emphasize employing statistical techniques, like summary statistics or data visualization, to uncover patterns, outliers, and missing values. Utilizing tools such as Pandas or NumPy at this stage enables a more technical examination of data structure and consistency. Exploratory Data Analysis (EDA) techniques, often highlighted, empower practitioners to discern potential biases or anomalies.

This activity is really time consuming but it’s crucial to feed the model with high quality data. Considering the specific field of application, co-managing data cleaning and wrangling with an HR professional is crucial: in that way you can easily identify general rules to check and find inconsistent data (I.e., compensation ranges, performance evaluation scale, etc.)

When dealing with missing personnel data, don't go straight to imputing with a standard statistical measure like the mean. First consider ML based approaches that approximate values based on other columns that hold influencing variables.

Addressing missing values requires a thoughtful strategy, whether it be deletion for minimal impact, imputation for informed estimation or ignoring for cases where data gaps are acceptable. Dealing with inconsistent data involves standardization, normalization or encoding, ensuring uniformity in formats and structures.

Mixed feelings on that! I personally prefer to remove observations with missing data-points (to be used in the model), especially because of the number of observations: in mid-size companies you have to deal with small-data (rather than big-data), so ensuring high quality is crucial. Furthermore, using statistical techniques to input missing data can remarkably mitigate the effect of existing phenomena.

To build a data cleaning practice able to bring real value beyond the model, your process should also ensure a direct connection with HRIS team, favoring a direct fixing of data also within the different sources: HR analytics are commonly based on offline DBs downloaded / extracted from several siloed sources (generally with poor integration capabilities), so data cleaning activity might be performed on work DBs only. On the contrary, you can leverage on data cleaning practice to improve both the quality of your HR data sources and the quality of your model’s inputs.