What are some challenges of data cleaning without standards and conventions?

As per my professional experience, following challenges occur without adherence to standards & conventions Inconsistency??: Managing erratic data formats and structures has proven to be a time-consuming task, requiring meticulous attention to detail. Ambiguity??: Interpretation and resolution of ambiguous data entries become recurrent challenges, necessitating scrutiny, and potential error correction. Interoperability??: The absence of conventions often hampers interoperability between datasets, complicating integration efforts and hindering seamless analysis. Quality Control??: Upholding data quality standards becomes a complex endeavor as the lack of predefined benchmarks makes it challenging to identify and rectify anomalies effectively.

Data cleaning without standards and conventions is challenging due to the lack of documentation. Documentation is crucial for understanding data sources, structure, variables, and handling outliers. Sometimes the dataset owner may have limited understanding, making documentation even more vital. Prioritize documenting data sources, transformations, and validations. Consistent naming conventions enhance clarity. Remember, there's no such thing as asking too many questions. Seek clarification from stakeholders to gather valuable information. Documentation and inquiry ensure accurate analysis.

Challenges of data cleaning without standards and conventions include: Inconsistency: Lack of uniformity leads to inconsistent data formats and structures, complicating integration and analysis. Errors: Increased risk of introducing errors due to ad-hoc and unstructured cleaning methods. Reproducibility: Difficulty in reproducing results or tracking changes, hindering collaboration and verification. Efficiency: Slower cleaning processes as each dataset may require unique, time-consuming approaches. Quality: Overall data quality may suffer, impacting the reliability and accuracy of subsequent analyses and insights.

Documentation plays a crucial role in the data cleaning process. Without proper documentation, it becomes challenging to understand the data source, its structure, meaning, quality, and any specific conventions or standards used. Documentation provides important context about how the data was collected, which helps in interpreting and analyzing it accurately. It allows data analysts or scientists to understand the variables, their definitions, and the specific domain knowledge associated with them. This is particularly important in cases where the data comes from different sources or has been collected over a long period of time.

In My experience data cleaning without standards and conventions presents several challenges. 1.Inconsistencies in data formats can lead to errors and inefficiencies, as manually reconciling these differences is time-consuming. 2.Without standardized protocols, duplicate entries are harder to identify and eliminate, compromising data integrity. 3.Ambiguous data entries without uniform naming conventions can cause misinterpretation and unreliable analysis. 4.Additionally, the lack of documentation and standardized processes hampers collaboration, making it difficult for teams to understand and trust the data. Overall, these reasons undermine decision-making and reduce overall productivity

If you're always cleaning data of inconsistencies and duplication issues, you're fighting an uphill battle until you implement a data governance strategy. Data professionals can only spend so much of their time cleaning issues like this, and many of these issues can be handled by governing data upstream. Data governance involves establishing processes to make sure that data is consistent, accurate, and reliable. If your data people are spending a lot of time fixing inconsistencies between data points like "1500 North Blvd" and "1500 North Boulevard", move that process upstream and free up time for your data people to focus on finding insights and creating value.

Inconsistency and duplication are common challenges that arise when dealing with data collected from different sources, formats, systems, or methods. When data is collected from various sources, it can have inconsistencies in naming conventions, formatting, or data structure. This can make it difficult to merge and analyze the data effectively.

Establish data governance practices to define standards, conventions, and guidelines for data collection, storage, and maintenance. Proper documentation of data sources, formats, and updates is crucial for understanding and resolving inconsistencies or duplication.

Missing and invalid values are some issues present in real-world datasets and sorting those issues is of primary interest. Machine learning algorithms cannot work on missing data points and throw an error. We should understand the type of missing data to think about the strategy to treat it.

The inclusion or exclusion of Outliers and Anomalies needs to be carefully considered. Without standards and conventions of how to deal with outliers, your data analysis could be fraught with inaccuracies. Things to consider when dealing with outliers, what kind of analysis are you doing (predictive, descriptive, prescriptive, diagnostic,), what is your total n, how many outliers are there, how will the outliers impact your data, etc. Doing fraud analytics, you normally focus on these outliers and would include these in your dataset because these alert the analyst to potential fraud.

Outliers are some anomalous values that generally do not fit into the distribution of the dataset. There are multiple ways to detect outliers and anomalies, but ultimately the decision to keep or remove them is with the user. In cases of healthcare, there are situations of very rare diseases and such data can be an outlier. Removing those datapoints will kill the task of diagnosis.

An additional challenge is a lack of context if the data cleansing is not overseen or conducted by someone who is a subject matter expert (SME) in the particular field. A SME will be able to provide an explanation on the reasoning behind the data cleansing tasks, identify discrepancies based on context, and solve any issues that may arise. Data cleansing should be a collaborative process with proper checks to ensure accuracy.