Your Finance Data Cleaning Checklist: 5 Essential Preprocessing Steps Before Using AI
In 2023, you’re either using AI, want to learn how to use AI, or you’re being left behind; in finance and accounting, the adoption of artificial intelligence (AI) has become commonplace.
However, to harness the power of these cutting-edge technologies, it is crucial to ensure that the data utilized is clean and properly prepared.
I’ve created a data cleaning checklist for you, consisting of 5 essential preprocessing steps you need to take before you feed these data into your algorithm. Plus, when to use Microsoft Excel for this and when to use a more powerful data analytics tool.
1. Outlier Removal
Outliers are data points that deviate significantly from the average values of a dataset. In financial analysis, it is vital to remove outliers to prevent distorted insights.
For instance, if you have a dataset containing 100 invoices, where 95 are in the thousands and 5 are in the millions for enterprise clients, analyzing them together would lead to inaccurate results.
To tackle this, identify outliers using statistical methods like the z-score or interquartile range (IQR), and then either remove them or transform them using techniques such as winsorization or log transformation. The z-score, in particular, is very useful for accounting and finance - it is a simple statistical measure that we can use to identify outliers in financial data. By calculating how many standard deviations a data point is away from the mean, we can determine if it is significantly different from the rest of the dataset and take appropriate actions to ensure accurate analysis and forecasting.
2. Principal Component Analysis (PCA) For Data Cleaning
Step 2 in the data cleaning checklist: PCA.
PCA is a dimensionality reduction technique that can be used to cluster results and analyze them effectively. In finance and accounting, where large datasets are common, PCA helps identify the most significant variables contributing to the overall variance.
By reducing the dimensions while preserving the maximum information, PCA simplifies the subsequent analysis, enabling more efficient AI applications.
Watching this algorithm work with your data is great! Using Python (I’ll explain more below), you can visualize how your results are getting clustered based on the data and understand your clients’ financials better!
3. Inconsistent Formatting Or Irrelevant Data In Your Dataset
Inconsistent formatting refers to discrepancies in the representation of data, such as inconsistent date formats or numerical representations. This data cleaning checklist requires you to standardize these formats, which is essential for data uniformity and accurate analysis.
To address inconsistent formatting, identify the variations in the data and apply appropriate transformations.
For example, you can convert different date formats into a single standardized format or correct inconsistent spelling and abbreviations across the dataset.
In Excel, you can use IF conditionals; if you move to something like Power Query (also inside Excel), you can automate the process by adding this technique to the pre-processing flow of your analysis.
4. Handling Imbalanced Data, Missing Data Or Dirty Data
Imbalanced data occurs when the distribution of target classes is significantly skewed.
In finance and accounting scenarios, this can lead to biased predictions or inaccurate models.
To tackle this, various techniques can be employed, such as undersampling the majority class, oversampling the minority class, or utilizing advanced algorithms designed for imbalanced data, such as Synthetic Minority Over-sampling Technique (SMOTE).
These methods help balance the dataset and improve the performance of AI models and thus, are an important part of this data cleaning checklist.
5. Handling Duplicate Data
This one sounds very logical but you will be amazed how many times the algorithms don’t work because of this!
Duplicate data can lead to misleading insights and redundant analysis. It is crucial to identify and eliminate duplicate records or entries.
This can be achieved by comparing values across relevant fields or columns and removing duplicate instances. Paying attention to unique identifiers or using advanced algorithms to detect duplicates can ensure data integrity and enhance the accuracy of AI-based forecasting models.
Step-by-Step Guide To Implement These 5 Techniques In The Real World
To implement the above data cleaning techniques effectively, it is recommended to begin with Excel and Power Query/Pivot for simpler problems or datasets smaller than 25 GB.
Excel is a widely used tool in the finance and accounting domain and provides intuitive functions for data manipulation and analysis. Power Query and Pivot further enhance Excel's capabilities by enabling advanced data transformation and automation. Excel allows users to perform basic data cleaning tasks, such as removing duplicates, filtering, and sorting. It also provides functions for basic statistical calculations and visualizations. For smaller datasets, Excel can be an efficient and accessible option for performing data cleaning operations.
However, when dealing with more complex problems or larger datasets, transitioning to a more powerful programming language like Python—or getting statistical analysis software—is highly recommended.
Python has gained significant popularity among finance and accounting professionals due to its ease of use, extensive libraries, and robust ecosystem for data analysis and machine learning.
Python offers various libraries, such as pandas, NumPy, and scikit-learn, that provide comprehensive functionalities for data manipulation, cleaning, and advanced analytics.
Pandas, in particular, offers powerful data structures and tools for handling structured data, making it suitable for data cleaning tasks like outliers removal, inconsistent formatting, and duplicate removal.
Additionally, it provides convenient functions for handling missing values and imputing them using various strategies.
Furthermore, Python's machine learning libraries enable finance and accountancy professionals to explore forecasting models. With scikit-learn, TensorFlow, or PyTorch, Python allows users to build and deploy advanced machine learning models, including time series forecasting models, to predict future financial trends and make informed decisions.
What are the Steps of the Data Cleaning Process?
If you're a finance professional seeking to enhance the accuracy and reliability of your data, understanding the steps of data cleaning is crucial. Data cleaning, also known as data cleansing or data scrubbing, is a systematic process that involves identifying and rectifying inconsistencies, errors, and inaccuracies within financial datasets.
The initial step entails comprehensively assessing the quality and integrity of the data. This includes detecting missing values, outliers, and duplicates, which can significantly impact financial analysis.
The subsequent step involves applying rigorous validation techniques to verify the accuracy of the data against predefined criteria. Once identified, the erroneous data is either corrected or eliminated.
The final step in the data cleaning checklist is to standardize and harmonize the data to ensure consistency across various sources and formats. By diligently following these steps, finance professionals can ensure the reliability and integrity of their data, enabling informed decision-making and strategic financial planning.
Data Entry, Data Quality, and Data Management
Data entry, data quality, and data management are vital components of maintaining accurate and reliable data within any organization, especially for finance professionals.
Data entry involves the process of inputting data into a system or database, ensuring its completeness and correctness. Accurate data entry is essential to prevent errors that can compromise financial analysis and decision-making.
However, data entry alone is not enough; data quality is equally important.
Data quality refers to the overall accuracy, consistency, and relevance of the data. It involves thorough validation, verification, and cleansing processes to identify and rectify errors, inconsistencies, and redundancies within the dataset.
Maintaining high data quality is crucial to extract meaningful insights and make well-informed financial decisions. To effectively manage data, organizations must implement robust data management practices. This includes establishing data governance frameworks, defining data standards, and implementing data security measures to protect sensitive financial information.
Additionally, proper data management involves organizing and structuring data in a way that facilitates easy retrieval, analysis, and reporting.
By prioritizing data entry accuracy, data quality assurance, and efficient data management, finance professionals can enhance their decision-making processes and drive financial success.
Final Word On Systems
Starting with Excel and Power Query/Pivot for simpler problems and datasets can provide a solid foundation; following this data cleaning checklist builds that foundation into an institution.
However, as the complexity of problems and data volume increase, transitioning to Python offers a more flexible and powerful solution. Python's simplicity, extensive libraries, and automation capabilities make it an ideal programming language for finance and accounting professionals seeking to leverage AI, data cleaning, and machine learning in their work.
If you’re interested in learning more about AI in FP&A - or finance in general - subscribe to The CFO Club’s weekly newsletter for updates.
