A data pipeline is the process of transferring data from the tools where it’s collected to the tools where it’ll be used for insight and transforming it from raw, uncleaned, unfiltered data into actionable, usable data.
This process is an essential part of a business intelligence strategy. It ensures that the data that powers your dashboards and visualizations is correct and will actually lead you to valuable insight.
The data pipeline process can often be long and complex, and different tools have different strategies for performing it. Sometimes, the process takes place across several different tools, and other times, only the original tool and your BI tool are involved.
To effectively understand the data pipeline, business leaders and data experts need to understand the techniques and processes that are a part of it. That way, if there’s an issue with their data, they can know where in the data pipeline to intervene.
Why is data preparation such a process?
When a data pipeline is long and complicated, business leaders begin to question why the process is necessary in the first place. It seems strange that they could collect data from one tool and then need to do all sorts of work on it to actually make it useful.
Data is data, they think. Why can’t I just plug my CRM data right into my BI tool and get straight to work, drawing insight and making decisions? There are a few reasons why raw data from a business tool can’t be immediately used to draw insight.
First, data might not be collected in a format that your BI tool can use. Business tools use all sorts of different formats, and very few of these formats are natively interchangeable with each other. Your BI tool will need to perform special operations during data extraction to make use of the data.
Second, your data may include errors, duplications, or dropped rows. General business tools don’t usually have data cleansing or data validation features, which means that unless something is done, these errors will be carried over into your analytics.
BI tools take steps to prevent those sorts of errors from making their way into actionable data. The data transformation step is essential for making sure those errors are removed correctly.
Third, there needs to be protocols for how and when the data from your BI tool is updated with fresh information from your business tool. Should there be manual or automatic updates? Should new data join, append, or replace the current data?
Proper data loading procedures can answer all of these questions. Without those sorts of questions answered, businesses can’t access updated data correctly.
How does ETL relate to data pipelines?
ETL stands for ‘extract, transform, and load.’ It’s the most common setup for data pipelines among market-leading business intelligence tools. In an ETL pipeline, data is extracted from connected business tools, transformed so that it’s error-free and formatted correctly, and loaded into the BI tool for use in dashboards and visualizations.
The ETL data pipeline has several advantages over other strategies. First, data isn’t loaded into your BI tool until it’s actionable and ready for use. That means you can be sure all the data in your BI tool can be used for insight.
In addition, you can combine data sources from multiple tools in the data transformation step, so that you build custom data sets that solve novel business problems. You can power your dashboards with one holistic data set instead of multiple smaller ones.
There are other setups for data pipelines, but most major players in the BI space agree that ETL is the most effective way for businesses to transfer their data.
The steps of the data pipeline
If your BI tool uses ETL, then your data pipeline will look pretty similar to other BI tools that use the same kind of pipeline. If your tool uses another strategy, it’ll most likely still have these same steps; they’ll just be in a different order.
In the broadest terms, a data pipeline is made up of three components:
To effectively manage their data strategy, businesses need to know how each step affects their data and what to do if there’s an issue.
During the data extraction step, data is pulled out of its original business tool and reformatted to match the format of the data already in the BI tool.
This step doesn’t actually involve a complete transfer of information. Data is pulled out of its original tool, but it doesn’t actually end up stored within the BI tool until later in the process. This step is all about accessing, isolating, and reformatting the data.
This is where data integration begins to become very important. Data integrations are the code bridges between two pieces of software. They’re what govern how data can be accessed and extracted from your business software.
Each business tool has a separate integration with your BI tool. In most cases, it’s this integration, not your BI tool itself, that reformats the original data in a way that a BI tool can understand.
BI vendors know how important good integrations are, and so they offer pre-built connectors between their tools and other pieces of business software. These pre-built connectors are the best way to integrate your tools.
Once data has been accessed, isolated, and reformatted, it’s time for it to be transformed from raw data into something that can be used to draw insight.
This step has two main goals– to remove errors, duplications, and unclean data and to configure the data in the correct way, so that it can be as useful as possible for reports, dashboards, and visualizations.
Data transformation is often the most complicated part of the data pipeline. It’s also the part that allows for the most user input. In fact, it’s rare that this step can be done without user input.
First, users must remove unclean, incorrect, or incomplete data. Some of this work can be done automatically with a BI tool. For example, most tools offer some sort of deduplication feature, which automatically removes duplicate data entries.
Other transformations require more input. To properly join data sets, users need to make sure that all the data in their data sets is formatted correctly. For example, one data set may format dates in MM/DD/YYYY format, while the other formats them DD/MM/YYYY. To prevent confusion when the data is joined, one data set will need to be changed to fit the format of the other.
After the data’s been cleaned, and all its errors have been removed, it’s time to configure the data set for success. In SQL-based tools, users can use SQL queries to build custom columns, pivot their data, and create calculated fields for further insight.
At this stage, users can join their data with other data sets so that they can build custom data sets to solve broader business problems. One major advantage of a BI tool is that it allows for data combination, so that users can find trends across disparate business tools.
Some transformations will be very simple; maybe the data doesn’t need to be joined, pivoted, or edited. Other transformations may be more complex, especially if the data is badly formatted, badly organized, and needs to be incorporated into other data flows.
Finally, after the data has been transformed to get it how you want it, it’s finally loaded into the BI tool so that it can power dashboards and visualizations. This is often the most painless part of the process, since users rarely need to give any active input during this step.
However, users do need to figure out how often their data should be updated and how those updates should take place. In modern BI, data can be updated frequently, either on a schedule or whenever the underlying data in the original tool is updated.
In addition, users need to decide how new data should be added to the old data set when updates occur. In most cases, a simple append is most effective—- the new data is just added to the old data. In other cases, a full replacement may be necessary.
The data pipeline — getting data where it’s needed
The data pipeline is one of the more technical aspects of a BI implementation, but it is important to understand how data moves from one tool to another. This way, if there’s an issue, you can figure out where in the pipeline it is.
Many tools offer self-service data pipeline tools. These tools are designed for people without technical experience or SQL knowledge. With these tools, even data novices can build complex data transformations using simple, drag-and-drop interfaces.
To get the most out of your BI tool, you need to ensure that your data pipeline works correctly. That way, you can trust the data you use to communicate information and drive insight.
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