Every day, we create and encounter tons of data–from our browser history to health apps. It can be extremely overwhelming to figure out how to manage the collection, quality, and security of all of that data. Companies are always looking to understand how data can be utilized, but you can only make valuable data-driven insights if you are confident in the quality of your data. Based on Gartner research, there are predictions that “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.” As a part of informing your data and analytics governance strategy, it is important to ensure you are leveraging data lineage to the best of your ability. In this article, we will explore the concept of data lineage, its benefits and importance, and how it can be used in conjunction with data classification and data governance to improve data security and productivity.
What is data lineage?
Data lineage represents and tracks the flow of data from its sources to its consumption. It involves recording and visualizing the transformations and changes that data undergoes along the way, allowing companies to better understand and manage their data assets. Documenting data lineage has become increasingly important in today's data-driven business environment, where accurate and reliable data is critical for making informed decisions and ensuring compliance with regulatory standards.
The benefits of leveraging data lineage
Utilizing data lineage provides several benefits to companies that use it to track and manage their data flows. Overall, data lineage plays a critical role in helping companies track and manage their data flows, ensuring that data is accurate, reliable, and compliant with regulatory standards. Some of the key benefits of data lineage include:
- Tracking errors in data processes: By recording and visualizing the transformations and changes that data undergoes, data lineage allows companies to identify and correct errors in their data processes. This can help improve the accuracy and reliability of the data, reducing the risk of making decisions based on incorrect or outdated information.
- Implementing process changes efficiently: Data lineage provides a comprehensive view of where the data has been, making it easier to understand the impact of changes to data processes. This can help companies implement process changes with greater confidence, reducing the risk of disruptions or errors.
- Performing system migrations with confidence: When IT needs to migrate data to new storage or software systems, data lineage provides the information, the metadata, needed to understand the location and lifecycle of data sources. This can make migration projects easier and less risky, reducing the time and effort required to move data to new systems.
- Combining data discovery with metadata: Data lineage allows companies to create a data mapping framework that combines data discovery with a comprehensive view of metadata. This can help improve data quality and ensure that data is coming from trusted sources, has been transformed correctly, and has been loaded to the specified location.
Why is data lineage important?
On a high-level, data lineage is important because it helps companies track and manage where their data came from, what’s been done to (i.e. data transformations), and where it’s going.. It also plays a critical role in supporting strategic decision making and facilitating data migrations. After all, data-driven decisions are only as good as the data that they were built off of. If you have a solid data lineage system in place, you can feel more confident in the rest of your data pipeline.
- Ensuring data accuracy and consistency: Data lineage allows companies to track and verify the accuracy and consistency of their data, by providing a comprehensive view of the data flow and the transformations and changes that data undergoes. This can help ensure that data is coming from trusted sources and has been transformed correctly, reducing the risk of poor decisions down the line.
- Protecting data integrity and confidentiality: Data lineage helps companies protect the integrity and confidentiality of their data by providing tracking and auditing capabilities. This can help ensure that data is stored and processed in accordance with organizational policies and regulatory standards, reducing the risk of data breaches or violations.
- Supporting strategic reliance on data: All departments within a company, including marketing, manufacturing, management, and sales, rely on data to make decisions and optimize their operations. Data lineage provides detailed information about the meaning and validity of data, helping companies better understand and use their data to create business value.
- Facilitating data migrations: Data lineage makes it easier to understand the location and lifecycle of data sources, providing the information needed to perform data migrations with confidence. This can reduce the time and effort required to move data to new storage equipment or software systems, making data migrations easier and less risky.
The data lineage process
Although data lineage requires several steps, at its core, it’s about the metadata–you are collecting data about your data. Below, we’ve outlined three steps involved in optimizing your strategy by leveraging data lineage. These steps include data discovery, data mapping, and tracking data transformations along the way. By following these steps, companies can create a comprehensive view and understanding of their data and ensure that data is trustworthy at every step. In this section, we will provide an overview of the steps involved in tracking data lineage.
After all, you can never deliver raw data to a stakeholder–imagine a chef serving up a raw ribeye steak! Understanding the steps it takes to get your data from a raw form to the form it is when it’s loaded into your database is critical, so that you can repeat the process in the future, just like a chef needs the right tools to ensure their process when cooking, the same goes for working with your data.
Einblick is a visual data science canvas for the coding data scientist. You can:
- Transform your data using Python and SQL code, easily switching between the two languages
- Use Einblick’s chart cell to visualize your data, so you understand how it’s been transformed quickly and easily
- Ensure data quality by sharing your work with teammates and collaborators, using live collaboration mode
A tool like Einblick can be instrumental in ensuring the integrity and quality of your data, as it’s easy to prototype code and spot errors quickly. If you get stuck, you can get input from your teammates instantly. From data cleaning to exploratory data analysis and data transformation, Einblick’s visual paradigm lets your workspace match your mental model, and it’s free to try!
Data lineage process steps
- Data discovery: The first step in the data lineage process is to identify and locate all of the data sources within an organization. This can include data stored in databases, files, and other repositories, as well as data generated by applications and processes. Data discovery involves cataloging and categorizing data sources, as well as understanding their structure, format, and relationships to other data sources.
- Data mapping: Once data sources have been identified and located, the next step is to create a map or diagram that shows how data flows from source to destination. Data mapping involves connecting data sources to each other and to the processes, applications, and systems that use the data. It also involves capturing metadata about the data, such as its schema, transformation rules, and business rules.
- Tracking data transformations: The final step in the data lineage process is to track the transformations and changes that data undergoes as it flows from source to destination. This involves capturing detailed information about how the data is transformed, what changes are made to the data, and why those changes are made. This information can be used to verify the accuracy and consistency of the data, identify errors in data processes, and support data governance efforts.
We've provided some examples of how you can utilize the Python pandas library for data transformations. Check out the full canvas below.
Data lineage and data classification
Data classification is the process of categorizing data based on specific characteristics. This can be an important part of an information security and compliance program, as it helps organizations understand where sensitive and regulated data is stored, both locally and in the cloud. By classifying data, organizations can improve productivity, remove unnecessary data, and reduce storage and maintenance costs. Additionally, data classification can help ensure that data is handled and protected in accordance with relevant policies and regulations.
What is Data Classification?
Data classification typically involves assigning labels or tags to data to identify its characteristics and sensitivity level. These labels can then be used to determine who has access to the data, and how it should be protected. Data classification can be performed manually, by assigning labels to data based on its content, or automatically, using machine learning algorithms to identify and classify data based on predefined criteria. In either case, the goal of data classification is to provide a clear and consistent way of organizing and managing data, to support information security and compliance efforts.
Data classification schemes
A data classification scheme is a set of rules or guidelines used to classify data. For example, a data classification scheme might define four levels of data sensitivity: public, internal, confidential, and highly confidential. Alternatively, a data classification scheme could be based on the end-use of the data, such as marketing, operations, or product analytics. The scheme would then specify which types of data belong in each category, and what level of protection is required for each category. Data classification schemes can be used to support data governance efforts, by providing a consistent and standardized way of organizing and managing data.
Using data classification with data lineage to improve data security and productivity
Data classification and data lineage can be powerful tools when used together to improve data security and productivity. By combining data classification with data lineage, organizations can better understand where sensitive and regulated data is located, and how it is being transformed and used. Combining these two approaches can help organizations better manage their data, and ensure that it is being used in a secure and compliant manner.
For example, a hospital has a lot of PII or personally identifiable information, and might use data classification to categorize patient data based on how sensitive the information is. This classification scheme could be used to determine who has access to the data, and what level of protection is required. Data lineage could then be used to track the flow of patient data, from its source (such as a medical record or test result) to its destination (such as a patient chart or medical report). By combining data classification with data lineage, institutions, such as hospitals can then ensure that clients and patients are healthy, happy, and safe.
Data lineage and data governance
Data governance is a critical component of data management, and is closely related to the concept of data lineage. Data governance involves establishing and maintaining policies, processes, and controls to ensure that an organization's data is accurate, consistent, and compliant with regulations and standards. This can include things like defining roles and responsibilities for data management, establishing data quality standards, and implementing controls to ensure that data is handled and protected in accordance with relevant policies and regulations. Additionally, data governance can help organizations identify and mitigate risks related to data management, such as the risk of data breaches or the risk of making decisions based on inaccurate or incomplete data. By implementing effective data governance practices, organizations can reduce the likelihood of these risks, and ensure that their data is being used in a secure and compliant manner.
By combining data lineage, which tracks the flow and transformations of data, with data governance, organizations can gain a more holistic view of their data, and better manage and protect it. This can further ensure compliance with relevant policies and regulations, and reduce risk related to data management.
Data lineage can help with tracking and auditing capabilities by providing a detailed view of the flow and transformations of data. By understanding how data is collected, transformed, and used, organizations can identify where errors or inconsistencies may be occurring, and take steps to correct them. This can be particularly useful when tracking changes to data over time, as it can provide a clear and comprehensive view of the data's history and evolution. Additionally, data lineage can help organizations identify who made changes to data, and when and why those changes were made. This can be useful for auditing purposes, as it can provide a clear record of data transformations and changes, and help ensure that data is being handled and protected in accordance with relevant policies and regulations. Overall, data lineage can provide valuable insights into the flow and use of data, and can support tracking and auditing efforts to ensure the accuracy and integrity of data.
Emerging trends and advancements in data lineage
Data lineage is an increasingly important area of data management, and there are a number of emerging trends and advancements in this field. Some examples of these trends and advancements include:
- The increasing use of data lakes and other big data platforms, which can provide a centralized repository for data, and support the tracking and visualization of data lineage.
- The development of new tools and technologies for data lineage, including machine learning and artificial intelligence algorithms, which can automate the process of tracking and analyzing the lifecycle of data.
- The integration of data lineage with other data management disciplines, such as data classification and data governance, to provide important context around your data to ensure you have the information you need to drive business decisions.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.