Poor data quality. Inconsistent reports. A disconnect between data scientists, engineers, and business teams. Sound familiar? These are signs of a deeper problem: your data processes aren’t built to meet today’s business demands.
McKinsey reports that 90% of businesses admit the value of data analytics trends. That’s why they work to scale their big data initiatives. But not all of them are doing it successfully.
So, if your data team should go the extra mile fixing a broken data analytics pipeline rather than innovating, it’s high time to change your tune. Instead of dealing with data chaos, consider DataOps, a smarter, more agile way to manage data your data lifecycle from ingestion to insights.
DataOps brings people, tools, and processes together. As a result, you can work with data faster and more easily. It helps fix problems quicker and keep up with changes, without all the stress.
But why does DataOps matter to your business value, and what benefits can it bring to your table? We’ll cover that further down. Keep reading.
What Is DataOps? Definition and Key Concepts in Data Operations
Gartner defines DataOps as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and consumers across an organization.”
DataOps is short for data operations. It’s a great way to manage data more efficiently. It helps teams design, build, and run data pipelines. So that data flows smoothly from the source to data scientists, decision-makers, and business teams.
As we mentioned above, DataOps isn’t just about high data quality. It’s about people, processes, and tools that work together for data transformation.
A good DataOps approach turns raw data into valuable insights that support your business. It focuses on best practices to make sure your data is always accurate, available, and ready to use.
Need help with data management? Contact us, and we’ll make your data work and drive business growth.
DataOps vs. DevOps: key differences in data and software operations
DataOps and DevOps may sound similar, but they serve different purposes. Both aim to improve teamwork, speed up processes, and use automation to reduce manual work. But while DevOps focuses on streamlining software development and delivery, DataOps is all about improving the way data is handled.
DevOps helps teams build and deliver software faster and with fewer bugs. They analyze apps’ work using tools like logs, metrics, and traces to avoid downtime.
DataOps assists data teams with data management to ensure datasets are clean, organized, and easy to access. So, the task of data scientists is to keep an eye on data quality by checking its freshness, size, format, and any changes to catch issues early.
In short, DevOps enhances software, and DataOps increases the value of data for the business and contributes to efficient data management.
Why DataOps Is Important for Modern Data Operations
Let’s be honest: managing data is hard, especially when your business is growing. You’re collecting more and more data every day, from websites, apps, and customer tools. It comes in all shapes and sizes: clean and messy, fast and slow, structured and not-so-structured.
With so much going on, data is often scattered across systems, falls out of sync, or lacks key context. So, teams are dealing with the dilemma of how to find the right data and use it to make smart decisions.
DataOps platform helps handle all of those messy, slow processes and ensure quality data delivery. It connects teams, automates processes, and brings order to chaos. So, your data becomes a reliable, valuable asset.
Top benefits of DataOps for scalable data operations
Still not sure if DataOps is right for you, but still opting for scalable operations? We’ve put together a list of key benefits to show how it can help your data scientists and your entire business work smarter and faster.
Breaks down data bottlenecks
We’ve all been there. You wait days for a report. Or you keep fixing the same data quality problems again and again. DataOps operations can help. It saves time by automating tasks and fixing messy processes.
For example, instead of cleaning customer data by hand every week, your team can set up a system that updates and checks it automatically. This creates space for your experts to focus on strategy, not firefighting.
Improves data quality
DataOps checks the quality of your data at every step. This helps reduce mistakes and cuts down the back-and-forth between teams.
No more spreadsheets with missing info or messy formats. Instead, you get clean, trusted data analytics you can use to make smart decisions. With fewer errors, decisions become faster, more confident, and better aligned across the business.
Keeps your data system smart and alert
If something goes wrong, you’ll know right away. DataOps has built-in monitoring and alerts to catch problems early.
For example, if your e-commerce site stops sending data, your team gets an alert immediately, not days later when someone notices old numbers in a report. That means faster fixes, fewer surprises, and more reliable data every day.
Builds trust in data across the business
When teams know the numbers are accurate, they’re more confident using them. That leads to faster decisions, better strategies, and less time-consuming reports.
Your sales team can finally trust that their lead data is accurate. Your product team can confidently use user behavior data to plan the next release. Trust in data leads to trust in decisions, and that drives results.
Brings teams together
DataOps isn’t just for data engineers, it’s a team activity. It encourages collaboration between data, IT, QA, and business folks.
Picture your analytics team, developers, and customer support working together to build a dashboard that everyone understands and uses. Cross-functional teams stay aligned and move faster with shared access to the same reliable data analytics.
Makes data easy to use
No more digging through reports or chasing data analysts. With clear dashboards and self-service tools, your team can find what they need when they need it.
A marketing manager can quickly pull campaign results without waiting for the data team. Customer service can track issues in real time. That means less friction, quicker insights, and better decisions at every level.
Helps you stay compliant and secure
With growing privacy rules (think GDPR, CCPA), you need to better control your data. You should know where your data is, who and how uses it. DataOps platform gives you that power.
You can track every step in a data pipeline: who accessed it, what changed, and where it’s going. DataOps platform keeps everything in one place. Clear audit trails and real-time control make compliance easier and security stronger.
Fuels innovation
When your data systems are flexible and fast, your business can move faster too. You can try new ideas, test them, and scale what works, without getting stuck in technical debt.
Launching a new product? You can build a custom dashboard in days instead of weeks, track performance, and tweak your strategy on the fly. When data flows freely, innovation becomes part of everyday business.
If your business is ready to make faster, smarter decisions with data, Forbytes is here to help. Contact us, and we’ll bring you a tailored solution that fits your needs.
How DataOps Works
DataOps is a smart set of processes that help your data teams get better results from your data. But how does it work, and what are its core areas? Let’s figure it out.
- Data integration: DataOps helps pull it all together into one clear picture. It uses automated, scalable pipelines to connect the dots. No matter how messy or spread out your data is.
- Data management: Once your data is flowing, it needs to be managed. DataOps platform takes care of this by automating workflows from start to finish, from the moment data analytics is created to when it’s shared.
- Data analytics development: DataOps operations help your team build smarter analytics models and better dashboards that people use. It encourages innovation and regular updates to keep insights fresh.
- Data delivery: DataOps makes it easy for teams to access what they need with self-service tools and clear visuals.
These core DataOps processes work together as a system. They help deliver a better data experience for everyone in your company. Data becomes easier to access, more reliable, and ready to use across all departments.
Do You Need a DataOps Practice? Signs Your Business Is Ready
DataOps is still a new idea for many teams. But for businesses that rely heavily on data, it’s quickly becoming a must-have.
If any of the situations below sound familiar, it might be time to consider building a DataOps practice:
1. You’re drowning in complex data
- Is your data coming in from multiple sources, including structured, unstructured, or a combination of both?
- Is it scattered across systems, tools, and cloud platforms?
- Does managing all that data feel messy and slow?
2. You work in a highly regulated industry
- Are you struggling to stay compliant?
- Are you spending too much time double-checking reports or worrying about audits?
3. Your business runs on data
- Do different teams rely on data to do their jobs (marketing, sales, finance, product, and more)?
- Are data projects piling up across departments?
Ready to take control of your data? Let’s talk about how a custom data solution can make your data faster, cleaner, and easier to use. Contact us today to get started.
DataOps Best Practices to Build a Successful DataOps Platform
Thinking about setting up a DataOps platform? This is the right decision, especially if you want to change how your business approaches and handles your data. To get the best results (and make life easier for your data engineers), here are a few best practices worth following.
Creating a connected data culture
Data shouldn’t live in silos. DataOps helps you bring people, tools, and workflows together into one connected system.
The benefits?
- Your teams, processes, and tech all align around shared data goals.
- You use data to make smarter, faster decisions.
- And instead of focusing on just one project or department, you’re improving the performance of your entire data ecosystem.
Build for data quality from the start
Data quality shouldn’t be a minor thing for your business. That’s why DataOps helps you build quality into every step of your data pipeline.
The benefits?
- You catch issues early using simple, automated checks.
- You create glossaries and data catalogs so teams can understand and trust the data they work with.
- And as your business and data grow, your system stays strong, consistent, and scalable.
Work in agile cycles
Trying to make everything perfect from the start rarely works. DataOps encourages you to move in small, smart steps.
The benefits?
- You test ideas quickly and learn what works.
- You gather feedback early and often.
- And you improve fast, building data analytics systems that adapt to business needs and grow over time.
Real-World DataOps Use Cases: How Companies Apply Their Data
DataOps is a great solution for data teams that work with data every day. It helps data engineers build clean, reliable pipelines and deliver ready-to-use data for analytics, AI, and real business decisions. Let’s look at some real-world ways companies use DataOps:
- Streaming analytics: Target uses DataOps to process real-time sales data from in-store and online purchases. As a result, the company can adjust prices swiftly and optimize inventory across 1,900+ locations.
- Data engineering at scale: Netflix scales its recommendation engine using DataOps principles. As user preferences shift and data volumes grow, data pipelines automatically adjust without downtime.
- Data observability: Airbnb integrates DataOps tools for data observability. So, engineers can catch data freshness and schema issues before they affect reporting or UX.
- FinOps for AI projects: Spotify applies FinOps principles, embedded in DataOps practices, to track GPU usage for training machine learning models.
- Fueling Data Science & AI: Zillow uses DataOps to power real estate AI models that predict home valuations based on dynamic market data. So, this ensures consistent data quality and model performance.
From scattered data to smart decisions: how we built a data platform for a logistics company
At Forbytes, we also have an on-point story to share. A growing logistics company had data scattered across many systems. It was hard to track performance, manage invoices, or make fast decisions. They asked Forbytes to help.
We built a modern data platform using Azure Data Lake and Databricks to:
- Bring all their data into one place
- Clean and organize it
- Make it easy to use in real time
We added Power BI dashboards, so reports that once took days now take seconds. Manual work went way down, and decision-making got faster.
When choosing DataOps tools, think long-term. Look for tools that are easy to scale, support automation, and free up your data team to focus on innovation, not manual fixes.
Essential DataOps Tools for Streamlined Operations
When you’re building a DataOps platform, it’s not about adding just another data tool. It’s about choosing software that helps you manage, scale, and automate your data pipeline without overwhelming your team. Let’s break down some key areas of DataOps and the tools that can help:
1. Data orchestration
What it is: Automating and organizing your data pipelines so everything runs at the right time, in the right order, without manual coding.
Why it matters: As your data grows, hand-coding pipelines won’t cut it. Orchestration lets you scale without needing a huge engineering team.
Top tools:
- Apache Airflow – Open-source and widely used
- Dagster – Modern orchestration with a developer-friendly focus
- Prefect – Easy to use with strong scheduling and error handling.
2. Data observability
What it is: Making sure your data is accurate, reliable, and monitored at every step.
Why it matters: You can’t trust insights from broken data. Observability tools help you catch issues fast and fix them before they cause problems.
Top tools:
- Monte Carlo – Offers automated data lineage, alerts, dashboards, and GitHub integrations to keep your data healthy and trusted.
3. Data ingestion
What it is: The process of pulling data in from all your different sources into one place.
Why it matters: If this step is slow or messy, your whole pipeline suffers. You need a tool that scales with your data sources.
Batch ingestion tools:
- Fivetran – A top ETL solution for moving data from source to destination
- Airbyte – Open-source and easy to sync with many apps
Streaming ingestion tools:
- Confluent – Built on Apache Kafka for real-time streaming
- Matillion – Works with Kafka to help you build real-time data pipelines faster.
4. Data transformation
What it is: Turning raw data into clean, usable formats that your teams can work with.
Why it matters: Clean data powers good decisions. And the easier it is to transform data, the more data and business teams can use it without waiting on engineers.
Top tool:
- dbt (data build tool) – Makes data transformation simple with modular SQL. Great for both engineers and non-engineers. Comes in open-source and cloud versions.
Want help picking the right tools for your DataOps stack? Contact our team, and we’ll change how you approach your data use.
Future of DataOps: What’s Waiting for Your Organization?
DataOps is quickly becoming essential for any data-driven organization with a modern data stack. Several key trends are fueling its growth.
For one, there’s a growing alignment between DataOps and other practices like MLOps and ModelOps, creating more integrated and efficient workflows. We’re also seeing the rise of PlatformOps, a broader orchestration layer that brings together DataOps, MLOps, ModelOps, and DevOps into a single, AI-powered platform.
Speaking of AI, it’s now enhancing nearly every part of the data pipeline, from data ingestion to processing and delivery, leading to smarter, more automated solutions. That’s why high-performing DataOps teams are becoming a strategic edge for companies, playing a vital role in enterprise-wide data transformation.
At Forbytes, we offer data engineering services & solutions to help our clients structure, manage, and optimize data to extract valuable insights for further growth.
Get in touch for a free consultation and see how our data engineering can help you turn your data into better business decisions.
