What are the potential consequences of leaving duplicates in your data analysis or modeling?

Two different data sources may use the same identifier type, but may have a different way of dealing with changes to the record the identifier type is representing. What may have started as agreement between the sources as to the value of the identifier type, may in time lead to divergence between the sources. It is important to remain vigilant about monitoring such changes and creating a data validation layer to capture exceptions to clean up any anomalies.

I experienced different reasons for duplicates: 1) Misleading formulation of data requirements, leading into wrong data queries (e.g. queries that are performed in other teams, provided without asking further clarifying questions) 2) plain data entry errors (human error)

Leaving duplicates in your data analysis can lead to several potential consequences. Firstly, it can skew results, causing inaccurate insights and misleading conclusions. Duplicates can inflate metrics such as totals and averages, leading to erroneous decision-making. They can also distort trends and patterns, compromising the validity of predictive models. Additionally, duplicates can increase data processing time and computational costs, reducing efficiency. Inconsistent data due to duplicates can damage credibility and trust in the analysis. Ultimately, failing to address duplicates undermines the reliability and integrity of your data, impacting the overall quality of analysis and subsequent business decisions.

As a data engineer, I enjoy using Python (either Pandas or PySpark) to generate a hash key from all fields to identify and count duplicates. There are also cases of "hidden duplicates," where 99% of the fields are identical, differing only in some metadata field. Sometimes, it's a duplicate that can be identified through a hash generated from fields that are supposed to be distinct.

If you use Microsoft Excel, you can either apply a filter and just check the drop-down and see, if you spot duplicates. A better approach is to sort your data, so you also can spot duplicates easily. This works for small data sets and is easy and fast. Another approach is to apply conditional formatting in combination with a formula, which evaluates a certain cell range for duplicates. There are lots of ways to build formulas here. Before checking for duplicates, it always makes sense to clean your data. Use the TRIM function first, it strips extra spaces from text, then check for duplicates.

As a spatial data scientist, one of the things that I think about when I see "detecting duplicates" is ultimately the concept of "defining duplicates". That seems like slight of hand, but it's an important distinction that's easy to think about in a spatial form. Duplication is in effect over-representation of a phenomenon: that's one reason it's harmful for analysis. It is possible for two entries to not be equal, but for them to represent duplication. Imagine you are doing a remote analysis in a forest. You drop a bunch of geosensors out from a plane. What happens if some of those geosensors are clustered in one spot? That's effectively a duplicate, every bit as much conceptually as the same record twice.

Pandas, a data manipulation library in python provides functionalities to detect duplicate rows and columns. The is_duplicated() function can help to find out duplicated data in the data frame.

Pandas has a great function to instantly remove duplicate data. The drop_duplicates function in pandas is a single line code that helps us to remove the duplicated data in the structured dataset. To check for any duplicated data we can use the is_duplicated() function in pandas.

Leaving duplicates in your data analysis can impact the accuracy, reliability, and integrity of your results. For example, you can get: ~ Skewed insights due to duplicates distorting statistical methods ~ Inaccurate aggregations due to inflated sums, averages and counts ~ Incorrect correlations as duplicates can introduce false relationships between variables ~ Inefficient data storage resources as duplicates consume unnecessary storage space ~ Increased additional computational resources and time, slowing down data analysis and query performance. ~ Data quality issues which can hinder the overall integrity and reliability of the data and analysis results

Duplicates can screw up with the visualization and EDA phase, especially with the histograms, leading to skewness. One might try to transform the data to solve this skewness which can lead to further complications. Also, duplicate values are waste of space and can also make the machine learning model to be biased or overfit.

Leaving duplicates in your data can ultimately lead to misconstrued insights. This can have a domino effect, resulting in flawed decision-making. Additionally, it poses other risks such as compromised data integrity, compliance risks, and potential damage to customer relationships.

In my experience, it is essential to clean and standardize data to make sure it is reliable for use. This includes specifying data formats, capitalization, and abbreviations, etc. which will minimize inaccurate or inconsistent data entries that may lead to duplicates. In addition, use unique identifiers for each record in the data set especially if you're working with multiple sources.

Consider establishing a data governance committee involving stakeholders from different areas of the organization who will define policies and procedures to ensure data integrity. Also utilize tools to minimize manual input to minimize human errors and conduct regular data quality monitoring assessments. Last but not least, set up regular meetings and ad hoc meetings as necessary to review and update policies and procedures based on new insights and feedback to continuously improve data quality.