What Strategies Do You Use to Handle Missing Or Incomplete Data?


    What Strategies Do You Use to Handle Missing Or Incomplete Data?

    When critical analysis faces the hurdle of missing or incomplete data, strategies from seasoned professionals like a data scientist become invaluable, employing methods such as ML and Mode Imputation. Alongside expert approaches, we've also gathered additional answers that span a spectrum of techniques, ensuring a robust discussion on the topic. From the precision of Maximum Likelihood Estimation to the predictive power of Regression Imputation, discover how experts and additional insights tackle this common analytical challenge.

    • Implement ML and Mode Imputation
    • Adopt Listwise Data Exclusion
    • Explore Multiple Imputation Technique
    • Utilize Maximum Likelihood Estimation
    • Adjust with Dummy Variable Strategy
    • Apply Full Information Maximum Likelihood
    • Predict with Regression Imputation

    Implement ML and Mode Imputation

    While conducting promotional impact analysis on an anti-histamine drug, we encountered missing data pertaining to the Third Party Non-Personal Promotion (NPP) Program. As our customer, the pharmaceutical company recently subscribed to a new vendor to conduct this program, performance metrics pertaining to "3 critical" months were missing. This duration was critical due to it being allergy season in the geography. Consequently, it was imperative to complete the dataset. We followed three different approaches based on the datatype of the missing field.

    1. For continuous metrics, ML imputation: Since the data was of a temporal nature with seasonal components, a time series was fitted to the previous six months and subsequent six months separately, and their mean was used as the imputed value.
    2. For nominal metrics, Mode imputation: The physician universe was divided into segments based on similar writing patterns and some other factors. The most frequent value for each segment was used as the imputed value.
    3. For binary metrics, LOCF & NOCB: The last observed value or the next observation was used to fill the missing value based on the proximity (temporal distance) of the previous or next non-missing data point, respectively.

    Farheen Hasnat
    Farheen HasnatData Scientist, Cognizant Technology Solutions

    Adopt Listwise Data Exclusion

    When dealing with missing or incomplete data, I usually use the 'Listwise' technique. That is, prior to analysis, I exclude any rows that have missing data. By doing this, I can ensure that the data at the basis of my analysis is accurate and comprehensive. Five months back, I worked with a client where I discovered some missing data in their demographics after a recent marketing campaign study.

    I immediately decided to use the Listwise approach, and after conducting my study, I was able to fully examine the facts that were available and give my client wise advice. I demonstrated to them how the analysis was sound and useful, which helped the marketing plans succeed.

    Kartik Ahuja
    Kartik AhujaDigital Marketer, kartikahuja.com

    Explore Multiple Imputation Technique

    Multiple imputation is a strategy where statisticians create several sets of plausible values to fill in the missing data. By using various algorithms, they generate not just one, but multiple complete datasets, which are then analyzed separately. The results from these separate analyses are pooled to give a final result.

    This technique can capture the uncertainty that comes with the missing data. If you're dealing with incomplete datasets, consider exploring multiple imputation to enhance the reliability of your analysis.

    Utilize Maximum Likelihood Estimation

    Maximum likelihood estimation (MLE) works on the principle of making the observed data most probable. Statisticians use this approach because it accounts for the missing values by utilizing the available data to estimate the probabilities of different outcomes. This method helps in constructing a complete dataset where the missing values are fitted in a way that reflects the structure of the data that is present.

    MLE is particularly useful when the pattern of missingness is random. Explore using MLE if you aim to optimize the fit of missing values and afford more accurate inferences from your data.

    Adjust with Dummy Variable Strategy

    Dummy variable adjustment involves creating indicator variables that signal whether data for a particular observation is missing. These indicators are then incorporated into statistical models to adjust for the missingness. This strategy does not try to replace missing values, but instead acknowledges and accounts for the presence of missing data in the analysis, avoiding the introduction of potential biases that could distort the results.

    This technique can be especially effective when the pattern of missing data is informative. When dealing with missing values, consider using dummy variable adjustment to account for the impact of data absence on your research.

    Apply Full Information Maximum Likelihood

    Full Information Maximum Likelihood (FIML) is a technique similar to maximum likelihood estimation, yet it uses all available information in the dataset. Unlike techniques that require complete cases or imputation, FIML works directly with the incomplete data, considering each piece of available data in the likelihood function.

    This approach is especially helpful as it avoids the biases that can occur when modifying the original dataset. If you want to preserve the integrity of your data while still accounting for missingness, think about implementing Full Information Maximum Likelihood in your statistical analysis.

    Predict with Regression Imputation

    Regression imputation is a powerful method for predicting missing values using the relationships found in the observed data. Statisticians model these relationships and then use the model to estimate the most likely values to fill in the gaps. The key advantage of this method is its accuracy and the way it maintains the original structure and correlations within the data.

    It’s important to ensure the model used reflects the true complexity of the data to avoid biased results. If the consistency of your dataset is a priority, you might want to look into using regression imputation for managing missing data.