/ Why the data pipeline is essential for data accuracy

Why the data pipeline is essential for data accuracy

Business intelligence tools allow businesses to connect their data sources to the tool, then take that data and leverage it towards insight.

However, it’s not quite as easy as that. Businesses can’t use freshly generated, unedited data to power their metrics and visualizations. They need to reconfigure and edit it so that it’s actually usable.

BI tools have a process for turning raw data from a data source into something that the business can actually use. This process is called the ‘data pipeline.’ It’s how data gets from the tools you collect it with to your end users, who then draw insight from it.

The data pipeline is essential for drawing value out of business data. However, many businesses don’t prioritize making their data pipelines as effective as they could be.

For many businesses, data just leaves one tool and enters another without any extra work being done. They don’t bother to actually build the sorts of data pipelines that would provide them with valuable data.

The most important element in this issue might be data transformation. Data transformation is the most important step of the data pipeline, but many businesses neglect it.

To create data sets that can be used to make effective decisions, businesses generally need to transform their data in some way. However, many are happy just using the data as it comes out of their data source, whether or not that data is easy to use.

Businesses that are finding that their data strategy isn’t as effective as it should be need to examine their data pipeline. Often, the root cause of a poorly implemented data strategy is poorly implemented data pipelines.
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What is data transformation?

Before businesses can understand what makes the data pipeline so essential, they need to understand data transformation. Data transformation is the most important part of the data pipeline, and the part that businesses need to focus their attention on.

Data transformation is the process of editing, rearranging, and reformatting data so that it can be more effective for drawing insight. There are a lot of different elements to data transformation, like removing duplicate data, pivoting rows and columns, and building out calculated fields.

There’s a perception that data transformation is difficult, so many businesses ignore it entirely. That’s not a winning strategy; by ignoring data transformation, they’re making drawing insight out of their data much harder.

Why is the data pipeline so important?

The data pipeline is the path data takes to get from its original data source to the end user. In the pipeline, data is reformatted, transformed, and edited so that it can be used for insight more effectively.

The end user generally doesn’t put much thought into how the data pipeline actually works. They assume that everything upstream of them works correctly, and that they’re receiving their data in the most effective format possible.

That’s not always the case. When a data pipeline is poorly implemented, data isn’t made more effective. It’s not delivered to the end user in the most actionable way possible.

How can the data pipeline affect the data that travels through it? There are a few key ways.

Formatting and pivoting data

Data can come out of a data source that is badly formatted or arranged. At a high level, data generally needs to be reformatted to effectively fit in with other business data. This process happens as part of the data pipeline, but many businesses ignore it.

Sometimes, it’s as minor as the abbreviations used for different pieces of content. One data source might abbreviate state names, while another might write out the full name of the state. On its own, that’s not a huge problem, but it can affect how those two data sources can interact.

Say you’re trying to connect your organization’s nationwide sales data to the sales efforts of different branches. Your sales data tool uses the full name of a state, while the software your branches use to track their data uses abbreviations.

Unless you reformat one of the tools to match the format of the other, you won’t be able to join this data effectively. In this way, ignoring the data pipeline can affect what sort of data you can and can’t access.

Other times, whole chunks of data need to be rearranged. A row might need to become a column, or a column might need to become a row. You might have to pivot entire sections of data to make it easier on end users.

Again, the data pipeline is essential for making those changes. This is where understanding data transformation is especially important.
Domo Data Pipeline Essentials

Duplicate, incomplete, and corrupted data

There’s no guarantee that data will come out of a data source correctly. Sometimes, data integrations insert errors into the data; other times, the data was just badly collected in the first place.

When there are errors in data, businesses need to remove them. Otherwise, those errors will be carried into data analysis and visualization.

For example, imagine that you’re trying to analyze your marketing campaign’s conversion rates. Somehow, the data integration for one of your marketing tools dropped the decimal for the conversion rate for one campaign, changing its 80.91% conversion rate to 8091%.

When this false data is combined with the data from other campaigns, it’ll massively distort the average values for your conversion rates overall. This error, if not caught and fixed, could easily lead users to believe their conversion rates are much higher than they really are.

That’s just how one data point can distort an entire marketing strategy. Data sets are filled with errors and duplicate data, and each one can lead your users to make the wrong decisions.

These sorts of mistakes can be removed in the data pipeline, but only if data experts actually make an effort to remove them. By prioritizing data transformation, businesses make their data much more accurate.

Combining data sources

One of the major advantages of a business intelligence tool is that it can take data from multiple data sources and combine them into one effective data set.

This allows users to compare and join data that’d otherwise be siloed. Without this ability, there’d be no way to see how data from different tools relates to one another or spot trends between disconnected data sources.

But these sorts of data sets don’t just happen. To build data sets that can combine data from multiple sources to deliver deeper insight, users need to make that happen at the data transformation stage.

Businesses have a few options for combining their data sources, from joining the data if the underlying sets share columns, or doing full appends if everything’s the same.

Again, this sort of combination is powered by data transformation. If a business doesn’t want to interact with data transformation, then they won’t be able to access that sort of synergistic value that comes from combining data sources.

Inserting calculated fields

Sometimes, the data just doesn’t have the sort of information that you want it to have. For instance, you may want to see your sales margin. Your data has full sales revenue and your costs, but it doesn’t combine that information into a concrete ‘margin’ column.

In those sorts of situations, businesses can use data transformation to deliver the information that they want. Many BI tools allow users to build custom formulas that can create completely new fields in their data.

In the example above, you could build a simple calculated field that expressed your margin by subtracting the data in the ‘Costs’ column from the data in the ‘Revenue’ column.

Using calculated fields, businesses can provide their end users with even more avenues of analysis and make their data more usable. By doing it at the data pipeline level, you can make those fields available to all.
Domo Data Pipeline Essentials

Data transformation — the key to the data pipeline

Data pipeline success is essential for overall business success. If businesses can’t connect their data sources to their BI tool and they can’t transform that raw data into something actionable, there’s no way that their data analytics will be valuable.

To access the most accurate data possible and to make their data easy for end users to work with, businesses need to prioritize the data pipeline. But it’s not enough to just put more effort into the data pipeline; they need to make data transformation a key part of their strategy.

Data transformation is often forgotten and sidelined, but it’s a crucial tool for delivering the best BI product possible. It’s what makes data-driven decisions possible.

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