What is DataOps? First, let's take a step back and look at the bigger picture.
Today’s data-driven businesses are fighting an uphill battle. Most companies are capturing data at an ever-accelerating rate, but they are also struggling to process all of that raw data and put it to use.
It’s a little bit like the fairy tale about spinning straw into gold. On its own, raw data can’t drive better business decisions or improve your machine learning outcomes. Your data scientists can’t mine your data for insights until it’s available to them in a useable format.
In today’s landscape, that needs to happen rapidly in order to keep up with the intake of data and the demands of the marketplace.
DataOps is the solution to this dilemma. If you’ve ever wondered how to speed up your analytics process and reduce your data costs, then you need to learn more about what DataOps can do for you.
Here, we’ll dig into what exactly DataOps means and how you can use it to drive results.
What Is DataOps?
DataOps is a cooperative and process-oriented approach to data management that focuses on improving access to data and shortening the lifecycle between data acquisition and analysis. DataOps breaks down the artificial divisions between the different data-oriented disciplines, so that data engineers can collaborate with data scientists.
The approach owes a lot to the Agile methodology, which allows for the speedy and continuous delivery of software applications. The agile approach enables developers to offer constant, real-time updates to their software. In the same way, DataOps allows data teams to access formatted data on a continuous basis, cutting out the long wait between data capture and data availability.
Agile Data Management
Traditionally, project management has used an approach known as the “waterfall” methodology. The waterfall method follows a pre-determined sequence to move a project through a series of stages, from the conception to the completion of the project.
This approach works well in manufacturing or construction, where the materials are stable and the outcome is well defined. However, the waterfall approach is not appropriate for data science (or, in many cases, for software development). That’s why DataOps uses a new methodology.
Instead of publishing data analytics as the end result of a long project, this approach publishes analytics in short bursts called “sprints.” Sprints can be released at regular, brief intervals. This has a few obvious benefits. First, it gives data teams access to updated analytics on a near-constant basis, instead of forcing them to wait for prolonged periods of time. Second, it allows teams to continually reassess the process and reconfigure the pipeline as needed, so that they can better meet the needs of their customers and their partners.
Automation from End to End
Data orchestration — the process of organizing, cleansing, and moving data — is foundational to DataOps. DataOps automates orchestration and monitoring so that each component of your data ecosystem is optimized for speed and accuracy.
DataOps relies on several interconnected data pipelines to achieve its goal of high-speed, high-quality and continuous data analysis. Let's look more at two of the key data pipelines here.
The Value Pipeline
The so-called “value pipeline” takes in raw data from a broad range of sources, then it (1) cleanses that data, (2) formats that data, and (3) uses that data to produce analytic insights.
This exact process may look different from industry to industry. But let’s imagine that your pipeline is collecting data from sales, vendors, and customers on a constant basis, in a variety of formats and at a wide range of locations. That raw data is processed into business insights that can help your team make better decisions about products, marketing, and customer relations.
DataOps connects up all of your data sources, whether cloud-based or on premises. Instead of relying on a cobbled-together set of interfaces, you’ll have one centralized platform that lets you access your data at every point. Anyone on your data team will be able to plug into that pipeline and collect information as needed.
The Innovation Pipeline
The innovation pipeline allows for constant experimentation with the available data to try and drive business value. This is where new approaches to gathering insight can be tried out and perfected before being applied to the formatted data from the value pipeline. Data teams can run new analyses, build tests, and complete the development cycle in a sandbox before adding the final solution to the production pipeline.
Having solid DataOps allows you to automate the testing process in the innovation pipeline and build new branching solutions off existing pipelines without risk of affecting existing data sets. This way, the code on your new experiments can be thoroughly vetted before being integrated with the massive production-grade pipelines.
Applications for DataOps
DataOps has clear value to any data-driven business. This, of course, means virtually any business operating today.
There are obvious benefits to the DataOps approach in practices like fraud detection and supply chain management. The same goes for any other area that relies on monitoring and assessing a huge inflow of information. It would be hard to find a field that doesn’t benefit in an immediate, quantifiable manner from the DataOps methodology.
DataOps has also been a game-changer in the medical field. Healthcare workers are required to input, organize, and protect a colossal quantity of data on a daily basis. DataOps speeds and streamlines that process and makes it possible to analyze medical data on a rolling basis, which is a huge benefit to the profession.
Artificial Intelligence (AI) and Machine Learning (ML)
From chatbots to human resource management, artificial intelligence (AI) has a growing role to play in today’s business. But artificial intelligence (and its cousin, machine learning) is only as good as the data you can feed it.
We’ve already seen how DataOps speeds up the data delivery process. It also makes it easier for data teams to reconfigure and re-label datasets to serve the needs of your artificial intelligence and machine learning projects.
And, of course, the ease of automation means that the data selection and delivery process takes place at high speed and without human error, which means fewer restarts and bottlenecks.
Working with Shipyard
If your business is scaling, then it would almost certainly benefit from DataOps. Shipyard can help you to seamlessly orchestrate your data and reap the rewards of rapid analytics and insight.
Intrigued? Want to learn more? Sign up for our free Developer Plan. With the free plan you can build and automate workflows in a matter of minutes to start implementing DataOps best practices.
