To get the most out of your raw data, you must automate how your data is collected, stored, managed, moved, and accessed. Data automation is a journey that spans everything from building a data automation strategy to choosing the right data automation tools. What’s the end destination? To automate every manual task, connect all relevant data sources, and have a central data source that your whole business can rely on.
Here’s an overview of what data automation is, why it benefits your business, how to develop a step-by-step automation strategy, and a list of tools to get you started on your data automation journey.
What is data automation?
Data automation is the entirety of automating how your data is collected, stored, managed, moved, and accessed. It starts with understanding the data needs of your business, and it evolves into developing a data automation strategy, prioritizing a step-by-step approach, and putting all the right data tools in place.
To help you automate your data, you’ll need the right kind of data analytics team. With effective data engineers, data scientists, data analysts, and machine learning engineers in place, you can start to identify and automate manual data tasks. With a prioritized list of data needs from the business, your data team can build the data workflows and pipelines necessary to connect your company data sources.
Data workflows and pipelines move data from source to source — whether it’s a dataset from a cloud app or a massive amount of data from an internally hosted enterprise software solution. When your data team automates the movement of this data at regularly scheduled intervals (or in real-time), they can power everything from advanced machine learning processes to SKU-focused ETL (extract, transfer, load) processes that publish product content to your websites for sale.
In the end, data automation looks like easily accessible real-time data for anyone who needs it, data visualizations for key business metrics, and more efficient workflows across your whole company. No one except your data engineers and data scientists will see the actual automations. At the same time, your customer-facing teams get to build better digital experiences, get their work done faster, and collaborate more effectively.
Even though the actual data automation will be invisible to most of your company, they’ll understand the benefits.
Benefits of data automation
Data automation reduces human error, saves time and money, increases accuracy in reporting, lessens the need for repetitive tasks, and leads to increased profits. Many large companies don’t invest in data automation because they believe it will cost them too much money.
Imagine you need to individually update every single customer email address in a CRM (customer relationship management) when someone changes their information on a digital website form. What if your merchandising team had to manually move all new product info into a cloud data warehouse from your siloed-off product information management (PIM) system? Many people still spend their days uploading and downloading excel spreadsheets to complete these types of tasks, and it’s not necessary.
The risk of human error and the sheer amount of wasted time they’re all experiencing can be eliminated with data automation. And unless you’re invested in data automation at your organization, you’re doing things that display a need for more organized data orchestration. You have a huge opportunity to speed up work, save wasted time, build better digital experiences, and make more valuable decisions.
Here are just a few benefits companies can get from data automation:
Reduced data processing time: It takes less time to get useful data that is vital for machine learning, data analysts, data scientists, and real-time customer experiences.
Improved decision making: With automated data, decision makers don’t need to get someone on the data analytics team to run a SQL query every time they need updated information. They can access the data they need or see it in helpful visualizations at any time.
Faster agile product development: When your data is automated, clean, up-to-date, and in the right formats, your product teams can build and prototype new digital experiences faster.
More efficient business processes: No more waiting for one person to get the data to another person through email, uploads, or other manual processes. Now everyone can do their jobs without delay using accurate and trustworthy data that.
Improved reporting accuracy: Reporting is key to every part of the business, and an increase in accuracy can lead to new opportunities, workflow efficiencies, and increased revenue.
Less manual work for data teams: Data automation frees your data scientists to use their full skillset instead of wasting time pulling reports and manually updating data sources for the business.
Reduced human error: When data is automated from one system to another, people have little to no room to make mistakes because there is no human intervention required.
Increased data security: With fewer excel spreadsheets, CSVs, and emails full of sensitive data flying around, you reduce the risk of a data leak or breach.
Scalable digital transformation: Automated data is one of the keys to navigating the always-changing digital landscape. When your data keeps up with new behaviors and allows personalizing customer journeys in real-time, you can scale your business with emergent trends.
It might seem overwhelming to invest in data automation — especially if you’re a large organization using lots of legacy data solutions. But it’s worth the cost and time no matter what scale you’re working on.
Many companies avoid data automation because they don’t know where to start. Like any other complex initiative, you need to develop a strategy.
How to create a data automation strategy
Start close in. Identify the data needs across departments and business units. Find all the manual data tasks and list them out. Learn what kind of information your executives and decision makers need to be effective. Identify the products, workflows, and processes that would be most valuable if fed by automated data. Understand how these can impact saving both money and time spent on work.
Once you have those things identified, you can start to prioritize and build out a roadmap for data automation. If your CMO has a metric they need to see every day to be effective, that could be at the top of the list. Or, if you have a data-heavy process with a well-documented impact on the bottom line, you could prioritize that and show the CMO why their need should wait a few months.
Here’s a simple list of the essentials you need to accomplish to create a useful data automation strategy:
- Understand current data infrastructure capabilities
- Identify data needs and manual tasks to replace
- Learn what executives and decision makers need
- Identify software, workflows, and processes that can benefit the most from automated data
- Map technical needs for data automation (data formats, data access, data security, etc.)
- Outline the potential cost and time savings along with revenue opportunities
- Earmark the quick wins (lowest effort/highest reward)
Your strategy can include everything from data infrastructure upgrades to tasks themselves. Make sure to create a future vision — what’s your ideal state for data automation? Once you have a simple but comprehensive strategy and a roadmap for the next year (or five), you can start on the step-by-step data automation process.
Step-by-step data automation process
Data automation starts with a solid data infrastructure. If necessary, move that into first place in your data automation strategy. Depending on your current technology, building the right data stack could take up all of year one’s budget and time on a five-year plan. It’s still worth it.
Do your best to visualize your data ecosystem, so you don’t miss any opportunities. Even the simplest map will be an overwhelming web of your data warehouses, cloud apps, data quality details, algorithms, databases, CSVs, ETL processes, data models, APIs, every enterprise software solution, and data from every business unit and each department.
It’s worth tackling and building this map over time, but don’t hyperfocus on getting the map right and avoiding the actual territory. Start by automating something you already know is a priority, and learn as you go.
For example, imagine automatically moving all your customer data into a customer experience platform to send personalized content in real-time. It’ll look something like this:
- Gather data from all required data sources — including APIs, data analytics platforms, cloud apps, databases, and data warehouses
- Cleanse and normalize your data, so it’s consistent
- Move data into Customer Data Platform (CDP) or Digital Experience Platform (DXP)
- Trigger any other dependent automated workflows
- Ensure all relevant parties have access to current data
The steps for building a data visualization or reporting automation will look different, but the basics are very similar. Gather data, cleanse and integrate data, move data to a central source, trigger relevant automated data workflows, and make your data accessible.
To get started on your first data automation, you just need the right tools.
Choose your data automation tools
Your data automation strategy will determine which combination of tools works best for your data analytics team.
Maybe your cloud data warehouse is set up, and your data engineers just need a way to build workflows quickly. You might simply need a place to centralize all of your data so you can build visualizations and reporting.
This list of trusted tools gives you everything you need to automate and manage big data — from data validation and integration to data analysis and machine learning modeling.
Snowflake is your solution for data warehouses, data lakes, data engineering, data science, data application development, and securely sharing and consuming data. It’s a fully managed cloud data service that’s simple to use but powers a near-unlimited number of concurrent workloads. Snowflake gives your data analytics team the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and share data.
Fivetran helps you securely access and send all data to one location. It instantly connects hundreds of your most demanding databases and data sources. Fivetran helps data engineers effortlessly centralize data so your team can deliver better insights faster.
Stitch delivers simple, extensible ETL built specifically for data teams. It puts analysis-ready data at the fingertips of your data team and business. With Stitch, you can extract from the sources that matter, load into the leading data platforms, and analyze with effective data analysis tools. Stitch extends to open source data sources, helps you secure and govern data, and orchestrates your data pipelines.
dbt enables data teams to work directly within data warehouses to produce accurate and trusted datasets for reporting, Machine Learning (ML) modeling, and operational workflows. It’s a developmental framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun.
Shipyard integrates with Snowflake, Fivetran, Stitch, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. Data engineers can use these tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds (without the headaches). Hundreds of high-growth data teams use Shipyard to modernize their data workflows and build a solid data infrastructure that connects their data stack end-to-end (from day one).
Get started on data automation right away
Data automation speeds up work, increases reporting accuracy, enables businesses to scale through digital transformation, and can unlock opportunities for increased revenue. With the right data automation strategy, you can make data available in real-time, secure sensitive data, and equip your company for digital transformation.
Shipyard gives you data automation tools and integrations that either work with your existing data stack or modernize your existing legacy systems.
Sign up to demo the Shipyard app with the Developer plan that’s free forever — no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business.