What are the most common mistakes to avoid when working with time series data sources?

1 Ignoring time zones and daylight saving time

One of the first mistakes to avoid when working with time series data sources is ignoring the effects of time zones and daylight saving time (DST) on the data. Time zones and DST can introduce inconsistencies, gaps, and duplicates in the data, which can affect the accuracy and reliability of the analysis. To avoid this mistake, you should always store and process the data in a consistent and standard time zone, such as UTC, and convert it to the desired local time zone only for presentation purposes. You should also use libraries and tools that can handle time zone and DST conversions automatically, such as pandas in Python or lubridate in R.

3 Using inappropriate aggregation and sampling methods

A third common mistake to avoid when working with time series data sources is using inappropriate aggregation and sampling methods for the data. Aggregation and sampling are techniques that reduce the size and complexity of the data by grouping and summarizing it over certain time intervals, such as minutes, hours, days, or weeks. However, not all aggregation and sampling methods are suitable for time series data sources, as they can introduce bias, noise, or loss of information in the data. To avoid this mistake, you should always use aggregation and sampling methods that preserve the temporal structure and characteristics of the data, such as mean, median, sum, or count. You should also avoid methods that can distort the data, such as last, first, or random.

4 Not validating and testing the data quality and integrity

A fourth common mistake to avoid when working with time series data sources is not validating and testing the data quality and integrity. Data quality and integrity refer to the accuracy, completeness, consistency, and validity of the data. Poor data quality and integrity can lead to erroneous or misleading analysis and decisions. To avoid this mistake, you should always validate and test the data quality and integrity using various methods, such as checking for missing values, outliers, duplicates, anomalies, or errors. You should also use tools and frameworks that can automate and streamline the data validation and testing process, such as Great Expectations or dbt.

5 Not documenting and communicating the data sources and assumptions

A fifth common mistake to avoid when working with time series data sources is not documenting and communicating the data sources and assumptions. Data sources and assumptions are the origin, context, and logic behind the data and the analysis. They can affect the interpretation and understanding of the data and the results. To avoid this mistake, you should always document and communicate the data sources and assumptions using clear and consistent formats, such as metadata, schemas, code comments, or README files. You should also use tools and platforms that can facilitate and enhance the data documentation and communication process, such as Data Catalog or Data Studio.

6 Not following best practices and standards for data engineering

A sixth common mistake to avoid when working with time series data sources is not following best practices and standards for data engineering. Best practices and standards are the guidelines and principles that help ensure the quality, efficiency, and reliability of the data engineering process and the data products. They can cover various aspects of the data engineering process, such as data modeling, data pipeline design, data storage, data processing, data testing, data monitoring, or data governance. To avoid this mistake, you should always follow best practices and standards for data engineering using established frameworks, methodologies, and tools, such as DataOps, Agile, or Airflow.