/ How and why you should automate your data pipelines

How and why you should automate your data pipelines

Businesses collect massive amounts of data during their regular operations. They can use this data to drive insight and make decisions, and business intelligence tools can get them there.

It’s not always that simple, though. There aren’t any standards for how business data is stored, formatted, or organized, which means raw business data can only rarely be used to find any sort of valuable insight.

To get any benefit from their data, businesses have to edit, rearrange, and reformat it so that it’s all organized in the same way. Otherwise, not only can they not analyze single streams of data correctly, there’s no way they could combine or compare data streams from different sources.

The most effective way to transform data into a consistent format is to apply a consistent process to it. That process starts when data is first extracted from its original context and only finishes once the data has been correctly changed and can be used for insight.

This process, which combines data transformation with the process of transferring it between tools, is called the data pipeline.

Businesses can operate their data pipelines by hand, but ideally, the process should be automatic. Once you’ve set up your initial parameters, you shouldn’t need to interact with the process. This way, you can spend less time fixing your data and more time using it.

 
domo
 

The data pipeline—what you need to know

In most modern BI tools, the data pipeline is made up of the same three steps, in the same order. The details might change, but broadly, the three steps are data extraction, data transformation, and data loading.

In data extraction, the data is accessed and isolated from its original tool. This is the part of the process where data managers need to decide what sort of data they want and what tools they need to connect to to get it.

After the data is extracted from its original tool, it doesn’t land in the BI tool right away. First, it has to be transformed. Data transformation is the process of changing data from raw and unusable to correctly formatted and actionable.

Once the data has been transformed and expressed in the correct format, it can then be loaded into a BI tool. Usually, this is pretty painless, but users still need to decide if the data should be updated, how it should know to update, and how often it should be updated.

Together, these three steps are known as the ETL process, or just ETL. A handful of BI tools rearrange these steps into ELT, where data is loaded into the BI tool, then transformed.

Usually, ETL is preferable because, that way, there’s no chance that a dashboard builder or visualization designer will stumble on untransformed data when they’re looking to power their content. If given the choice, ETL is better.

 

Why should I automate my data pipeline?

Once you’ve set up a data pipeline for a given data set, you shouldn’t need to mess with it after the fact unless something breaks. The pipeline should run by itself without any input from anyone.

This is a concept known as ‘data pipeline automation.’ Most popular BI tools allow for the data pipeline process to be mostly or completely automated.

Automation is especially important when a user needs to update their data. By setting up rules on how and when data should be updated, users can make sure that they’re always accessing the most up-to-date data possible. If they couldn’t automate this process, there wouldn’t be that guarantee.

In some legacy tools, users have to figure out how their data pipeline should work every time they want to update their data. This is also true when a user has to import data from an on-premise tool like Microsoft Office.

Through automation in market-leading BI tools, users can minimize the amount of time they take spend on their data pipelines and maximize the amount of time they spend doing their actual jobs.

 
domo
 

How should I automate my data pipeline?

Luckily, in many market leading tools, even data pipeline automation is mostly automated. Transformations are saved between updates, software stays connected through data integrations, and setting up automatic updates is easy.

However, that doesn’t mean that data pipeline automation just happens. Businesses still need to be proactive and ensure that they’re on track for data pipeline success. Here are some things to keep in mind:

Natural language processing

In BI tools that use relational databases, data is stored in the tool in the SQL programming language. To transform data, users need to write and perform SQL queries on their data so that they can get it how they want it.

SQL can be very complicated for people without any coding knowledge, and for businesses without a technical focus, this can be a dealbreaker. If users can’t use SQL, they can’t transform their own data, which makes everything more complicated.

However, many tools sidestep this problem. They have features known as ‘natural language processing.’ These features automatically translate things like mouse clicks, typed-out questions, and UI interactions into SQL queries.

The best BI tools have interfaces that are built from the ground up to allow users with no technical experience to perform data transformations, without writing any SQL at all. These tools are often drag-and-drop or flowchart-based.

With natural language processing, users can transform their data, without even knowing they’re performing SQL queries on it.

Broadly useful data sets

Often, when users want to access data to answer a business question, their impulse is to take a very narrow slice of their data that only answers that specific question. From there, they transform their data in a very specific way to answer the question being asked.

While this approach can be useful in some situations, it leads to a lot of extra work when you have to build out data pipelines for each of these data sets.

A big part of data pipeline automation is realizing when you don’t need to build a new data pipeline in the first place. Instead of building a separate data pipeline for each data set you need, try to figure out where and how you can answer business questions with the same few data sets.

Sometimes, that means transferring more data than you need at that moment, so you can use that data set later to power other content. In general, broader, more general data sets are more useful than extremely narrow data sets.

In addition, it’s best practice to power an entire dashboard with the same data set, if you can. This way, everything updates at the same time. With broader data sets, it’s easier to build comprehensive dashboards out of one data set.

 
domo
 

Automatic data updates

Automatic updates might be the most important element of pipeline automation. With automatic updates, users choose when, where, and how new data comes through the pipeline.

In the past, data could often only be updated manually. That means if you wanted to add more data to a data set, you’d have to manually execute the update in some way. That’s not the case with most modern BI tools, but there is still some work involved.

When a user sets up a data pipeline, the last step is usually choosing how often the data is supposed to update. Some data sets might not be updated at all, while other sets might need to be updated in real time.

For sets that need to be updated, there are two main approaches to when the updates should happen.

First, a data set might need to be updated whenever the underlying data set updates. That ensures you’re always getting the most up-to-date information, but it could also lead to more frequent updates than you need or want.

This approach is best for data sets where it’s absolutely essential that the data update in real time. Often, dashboards and visualizations that track specific operations need to be updated this frequently.

The second approach is scheduled updates. Users can schedule their data sets to update at a given rate, like every hour or every day. In some tools, they can even select a specific time they want the data to update.

This approach is best for data sets that change over time but aren’t mission critical. This way, you can control the number of updates you get. You get fresh data, but you aren’t buried in new information.

 

Data pipeline automation—keeping your data correct

Businesses need access to up-to-date, correct information about their operations. That can only happen if their data pipelines are effective and automated.

Data pipeline automation isn’t very hard to accomplish, but it is important. Businesses need to ensure that they’re prioritizing pipeline automation strategies so that they can be sure that their data is useful.

Check out some related resources:

Data Never Sleeps 10.0

How a leading fashion retailer is using data to drive growth

Domo for Financial Services Playbook

Try Domo for yourself. Completely free.

Domo transforms the way these companies manage business.