Most of the finance leaders spend majority of their time in cleaning the data. As organisations grow, their data starts sitting in different places and it takes a lot of time and effort to get the right processes in place and maintain data hygiene.
A lot of finance leaders like Nicolas have been talking about the role of AI in finance. One important area where it will play a role is data cleaning.
There are usually the following challenges with data:
1 Data duplicates
2 Incomplete data
3 Missing values
4 Outliers
5 Inconsistent data across sources
The hypothesis is that machine learning and AI can help in the above and reduce 80%+ time spentin data cleaning, significantly increasing the productivity of finance leaders.
Data cleaning process can be done using a combination of the following techniques:
1 Regex and ML based canonicalization techniques:
◦ Regex (RegularExpressions): Regular expressions are patterns that define a search string. They are a powerful tool for string manipulation and matching. In the context of canonicalization, regular expressions can be used to transform and standardize data. For example, if you have different representations of a date (e.g., "01/02/2023" and"2023-02-01"), you can use regular expressions to canonicalize them into a consistent format.
◦ ML (Machine Learning)based techniques: Machine learning algorithms can be employed for canonicalization tasks where patterns may be complex or not easily expressed using regular expressions. ML models can learn from data to understand and transform variations in the input. For instance, a machine learning model could be trained to recognize different date formats and convert them into a standardized form.
2 Clustering or AnomalyDetection:
◦ Clustering: Clustering is a technique used to group similar datapoints together based on certain features or characteristics. In the context of canonicalization, clustering can be applied to identify similar entities and group them into clusters. For instance, clustering can be used to group similar product names or user profiles, allowing for the establishment of canonical representations within each cluster.
◦ Anomaly Detection: Anomaly detection is focused on identifying datapoints that deviate from the norm. In canonicalization, anomaly detection can help identify outliers or irregularities that may need special handling. For example, detecting anomalies in a set of timestamps could help identify and address outliers.
3 Fuzzy Matching: Fuzzy matching is a technique used to find approximate matches for a given string. It is particularly useful when dealing with data that may contain typos, variations, or slight differences. Instead of requiring an exact match, fuzzy matching allows for a degree of similarity. For instance, fuzzy matching can be used to match similar names, addresses, or other textual data that may have slight differences.
4 Imputation. Imputation refers to the process of estimating or filling in missing values in a dataset, and various AI-based approaches can be applied to perform imputation effectively.
It will be interesting how the above pans out and how much time it can actually free up for finance teams. The future is exciting!
