Business leaders understand that they need to harness big data to thrive in today’s competitive marketplaces. Data operations, or DataOps as it’s commonly called, is a growing focus in the data science industry because of its vital role in making massive amounts of raw data useful and actionable.
DataOps refers to the combination of people, process, and technology required to use your data sources, data pipelines, and cloud data storage solutions as strategic assets. With the right approach to DataOps, businesses of all sizes can gain access to insights that help them make faster decisions, identify revenue opportunities, and save significant costs by streamlining their workflows.
Here’s a closer look at what DataOps is, how it benefits your business, the tools you can use to implement it, and where to get started.
What is DataOps?
DataOps is the combination of methodology, technology, and people that manage the data flows that run your business, from analytics to customer experiences. DataOps commonly focuses on the automation of data tasks that can reduce human error and increase efficiency. This includes ETL's (extract, transform, and load) processes, SQL queries, and data science pipelines.
A good way to think about DataOps is like a development operations (DevOps) team in a software company. DevOps teams specialize in software development and maintenance. They understand and work on problems that general IT staff don’t understand. Meanwhile, DataOps teams specialize in managing only the data (aka raw material) that moves between software systems.
DataOps and DevOps are often confused with one another because they’re so closely related and dependent. You’ll need a DevOps team to build and run software while a dedicated DataOps team secures your company's data sources, manages data workflows, and makes data useful to all parts of your business.
Your DataOps team can include multiple specialties. For example, data engineers work with data management toolsets and build data pipelines. Data quality teams work across the entire data pipeline from beginning to end to monitor its health and performance. Data scientists and your data analytics teams drive decisions across the business. And machine learning specialists model data in your cloud data storage solutions to find new revenue opportunities and track advanced metrics like lifetime customer value.
With so many different parties involved in the management of data—and a growing need for actionable insights—DataOps is more important than ever before. Here are some of the benefits that come with getting DataOps right.
What are the benefits of DataOps?
DataOps benefits your business in a variety of ways, from accurate data analytics to simple infrastructure optimization.
DataOps provides your company with the opportunity to use your raw data sets for an improved customer experience and more streamlined business processes. DataOps also provides valuable insights that improve market share growth, increase conversions, bolster brand awareness, and lower customer acquisition costs.
A focused DataOps strategy empowers your data teams to deliver data-driven solutions at speed while providing access to internal data sources as well as external data sets. For example, building a predictive model requires coordination between multiple stakeholders across different departments. DataOps manages the complex processes that require collaboration between team members in different locations, ensuring all components are coordinated so they operate efficiently on the same page.
Here’s a short list of the main benefits of DataOps for business:
- Automated data pipelines
- Streamlined access to meaningful insights for the whole organization
- Faster executive decision-making with real-time business intelligence
- Increased agility to adjust to market changes and new customer behaviors
- Elimination of data silos
- Increase conversion
- Lower customer acquisition costs
- Faster data analysis and reporting with increased accuracy
DataOps also improves scalability of processes and enables self-service access for end users. That makes it easier for everyone to navigate data workflows within their own time frames. When your DataOps team, technology, and strategy are aligned, data operations gives you insights to solve some of your company’s biggest problems.
What business challenges does DataOps solve?
DataOps solves foundational data challenges that businesses face every day, such as data compliance, data governance, and disaster recovery.
DataOps allows you to analyze the entire business infrastructure, including its databases and storage systems. This shows how your data pipelines are performing, builds a data lineage for review, and helps in case of system failure or other data loss events.
DataOps also helps you identify what kinds of backups you need in order to recover your data, should an event occur that causes damage to your business's IT infrastructure.
You can integrate data monitoring tools with DataOps to continuously assess the health of all critical applications and databases within your organization. You'll know when data workflows (e.g. an ETL process) run successfully or have errors and need attention. If a data process has unexpected consequences, the person managing those changes needs to know right away in order to fix the issue and prevent widespread impact.
That’s why you need a wide range of skill sets on your DataOps team to support your business.
What are the different parts of DataOps?
An effective data operations team needs many different areas of expertise. The process of data operations varies depending on the company, but there are some common areas every business needs to cover.
Here are some of the most important areas of focus for DataOps:
- Data analytics team
- Data infrastructure
- Data science pipelines
- ETL and Reverse ETL
- Data ingestion pipelines
- Data transformation
Let’s say you want to introduce predictive analytics into your decision-making process. You need a DataOps team to make that happen.
Predictive analytics require machine learning models to learn from historical data and make predictions about future events. Predictive modeling requires the use of algorithms such as Linear Regression or Decision Trees, which then need to be trained with historical data sets so they know how to predict future events. This means you need a team who understands how to make that possible.
Machine learning models can’t do anything without clean and structured data. That’s why all data operations depend on the data ingestion process, which collects the raw data from all sources and moves them to your cloud data warehouse or data lake.
Once ingested, this raw data needs to be transformed in order to have a normalized structure. Once transformed, the next steps in the data process depend on what type of analytics you're trying to perform.
In order to cover all of these areas you need a DataOps platform—or the tools to build your own.
How does a DataOps platform help?
In order to have a data-driven organization, you need to have the right tools in place. There are many different types of data operations platforms that can power a data team. It's best to use an agile DataOps platform that offers an end-to-end solution for capturing, organizing, structuring, storing, analyzing, and delivering data across the enterprise. This will ensure that your teams work more efficiently with the right information at their fingertips.
It’s no longer enough to have one or two people working on one aspect of the data supply chain. Today's businesses require data analytics teams across departments—from marketing to IT—to work together seamlessly across the data lifecycle. That means your DataOps solutions need to be flexible enough to meet many needs across your organization.
Here are some of the best DataOps tools you can use to collaborate on data operations from any location.
Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. It gives data engineers the tools to quickly launch, monitor, and share resilient data workflows. Plus, they can drive value from your data at record speeds (without the headache).
A DataOps platform manages this complex ecosystem by providing data governance, data infrastructure, and API support. This enables developers to focus on creating high-value analytical apps. These systems ensure data availability, provide access control, and improve disaster recovery capabilities.
Ready to get started or upgrade your DataOps capabilities?
How to get started with DataOps
You need a DataOps solution that can work with your existing data stack or modernize your legacy systems. Shipyard’s data automation tools and integrations fill in the missing parts of your DataOps technology puzzle. It’s easy to find out how and where Shipyard works best for you.
Sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start building data workflows in 10 minutes or less, automate them, and see if Shipyard fits your DataOps needs.