Data pipelines are an integral part of the modern agency’s approach to building usable data sets and insightful reports. To prevent data accuracy issues, it's important to ensure proper engineering and maintenance of your data pipeline. Let’s review the structure behind data pipelines, learn about the phases data flows through them, and discuss common problems and ways to mitigate damage within your data pipeline.
Data Pipeline Structure
At a fundamental level, a data pipeline is a progression of steps raw data moves through in its journey to analysis and reporting. The output from one segment of a data pipeline becomes the input for the next. The phases of a data pipeline may include storage, pre-processing or cleaning, transforming, merging, modeling, analysis, and delivery. Let’s cover a few of these phases in more detail.
- Storage: The main task of storage within a data pipeline is providing large-scale and cost-effective storage that scales with an organization’s data. A key consideration in regard to data pipeline storage is accessibility. There can be many middle-men within a data management supply chain, so agencies should make data democratization across the organization a priority.
- Pre-Processing: Pre-processing prepares data within the pipeline for analysis and provides a controlled environment for processes downstream. Strong protocols for data cleanliness, which define how data will be used for analysis and transformation, are critical at this stage.
- Transformation: Once data sets within a pipeline are processed, you can apply several transformation techniques to that data in order to configure and prepare it for analysis. These techniques include smoothing, attribution construction, generalization, aggregation, discretization, and normalization.
- Analysis: The ability to confidently apply analysis to large amounts of data is perhaps the most important executive function of a data pipeline. Analysis provides useful insights into accumulated information and enables comparison between new and existing data sets. It’s important to stress that analysis capabilities are defined by the robustness of the upstream phases inside a data pipeline.
When you understand the components of a data pipeline, you’ll be able to process big data and organize your team to capitalize on the insights generated. Well-maintained data pipelines provide a holistic framework and approach to data management, which can impact not only your reporting capabilities but also your culture and organizational structure.
Read our blog, “What Are Marketing Data Pipelines and How Do They Work,” for a full rundown on data pipeline structure and benefits.
Unclogging Your Data Pipeline
Managing data without a strategy or centralized data management platform can quickly spiral into segmented and siloed chaos, making problem-solving and unclogging a data pipeline difficult, if not impossible.
The first step into unclogging a data pipeline is recognizing the potential problems mismanaged datasets can create. A fair amount of problems with data maintenance can be traced back to the cleaning phase of the data pipeline.
While this is not a comprehensive list of all the potential data cleaning problems, the most common ones are:
- Missing data
- Non-unique column headers
- Multiple fields in a single column
- Extra white space around text
- Non-standardized data: dates, names, column headers
While a manual white-glove approach will result in stronger identification of problems, most organizations are working with datasets so massive that automation is the only answer. A robust approach to marketing data integration is the best way to ensure your data pipeline keeps flowing.
Through semantic integration, data cleansing, and data normalization, a properly integrated marketing data pipeline ensures data sources are connected, performance metrics are tabulated, and data sets are standardized, resulting in a unified view of your campaigns and activities that is ready for analysis.
Ways to avoid clogs in your data pipeline are:
- Avoiding single script files
- Decoupling dependencies
- Organizing things in useful abstractions
- Anticipating potential extensions of your pipeline
- The Boy Scout Rule: “leave things cleaner than you found them”
Avoiding Single Script Files
Single script files may seem easier to work with and feel like they might accelerate the delivery of the data, but in the long run, single script files can’t handle a ton of responsibility and will result in a code that’s not scalable, extensible, or usable.
If you can decouple dependencies in your code, you will have independent contexts and cut down on the number of unused dependencies in your data pipeline. This also ensures the code is transferable to new instances, databases, and demands.
Code that is organized in useful abstractions with singular responsibilities will lead to cleaner pipelines with data sets that are more testable, readable, and maintainable. Your classes/methods don’t need to aggregate data, optimize partitions, and write the data to storage in a single go. You can segregate these steps making sure abstractions are responsible for as much as possible in a single action.
You should be anticipating future needs and understand that refactoring code for novel data sets can be a huge time waste. The code should be open to extension but closed for modification. Considering the cyclomatic complexity of the organic development of code should be a chief concern.
The Boy Scout Rule
“Always leave a campground cleaner than when you found it” can and should be applied in software development. Being intentional and consistent about conventions, classes, and variables at every step in the data pipeline will ensure the resulting code is useful and scalable.
Maintaining A Data Pipeline Culture
For your data pipeline to work properly and efficiently, there needs to be a consistent focus on quality and architecture. The best way to achieve this consistency is to focus on culture. This means that managers and engineers prioritize and recognize the principles and values that guarantee quality and cleanliness.
Besides endless meetings and relentless project management protocols, an easy way to engender a stronger culture around data pipeline maintenance is having dedicated venues to discuss the latest research and technical literature about coding and information architecture.
If you establish a strong foundation on how your team interfaces with your data pipeline, you can introduce the latest optimizations and considerations to improve the codebase, establish clear goals, and define the best ways to achieve them. An agency culture that agrees on data management and labeling protocols is in a better position to reap the full benefits and functionality from its data pipelines. For more on this topic, read our blog “Agency Taxonomy And The Importance Of A Data Dictionary.”
It takes a ton of work to build and maintain data pipelines properly, so it’s important to align your team with the tools, resources, and inspiration to keep them engaged and strategically focused. NinjaCat understands data pipeline problems and the pitfalls of a slap-dash approach to their creation, but more importantly, we know the potential benefits of getting this problem solved correctly. If you’re staring down the barrel of a problematic data pipeline and are looking for a platform to help you sort things out, get in touch with us to learn how NinjaCat can help.