Businesses across the board are spinning their tires when it comes to data and analytics, with many of them failing to unlock maximum value from their investments. According to one study, 89% of companies face challenges around how they manage data.
While businesses face a variety of data challenges, the majority struggle because they don’t have a strategy in place for analyzing data and moving it across the organization. As a result, data tends to stagnate instead of moving into production, causing teams to miss critical insights and opportunities. And, in many cases, this is due to a disconnect between data and IT operations teams.
In order to overcome this challenge and manage data more effectively, a growing number of companies are implementing data operations—or DataOps—frameworks. Keep reading to learn what data operations entails, why it matters, and how companies can use it to deliver greater value.
Why Is Data Important in Operations?
Companies today are at a crossroads in terms of how they operate. A gap is widening between data leaders who are actively applying and using data and the laggards who are still relying on traditional manual workflows and instinct to make decisions.
At this point in time, companies that fail to integrate data into operations are merely at a disadvantage. But data usage is quickly accelerating among businesses. As a result, over the next few years, data laggards will find it much harder to keep up with data-driven competitors who have years of experience managing and leveraging data.
For this reason, companies need to focus on using data to guide both short-term decisions as well as larger initiatives, such as artificial intelligence and machine learning projects. Taking action today and becoming more data-driven could pay dividends down the line and help businesses remain competitive.
What Is Data Operations?
DataOps is a management strategy that companies can use to drive greater value from analytics and make information more actionable and accessible. At a high level, DataOps involves enhancing collaboration between IT operations and data-processing teams.
Very simply, DataOps involves deploying pipelines—or tools and processes—that enable teams to develop with data. By building pipelines, companies can eliminate wasteful management practices and also unlock maximum value from their data.
As Gartner explains, DataOps is a collaborative data management practice. It focuses on improving the automation, communication, and integration of data between data managers and consumers within a company.
With this in mind, the main point of DataOps is to ensure stakeholders receive timely access to relevant data. This applies to all areas of an organization, from IT teams to sales and marketing departments and everything in between. By controlling and optimizing the flow of data, companies can make better decisions in less time.
What Do Data Operations Teams Do?
DataOps is an emerging field. It brings together multiple technologies, disciplines, and teams. No two companies or environments are exactly alike, and so DataOps’ roles and responsibilities tend to vary between different organizations. But generally speaking, DataOps teams typically work to improve agility and extract insights from data.
DataOps teams deliver business value by making data delivery more predictable, consistent, and reliable. In addition, they help drive cultural change within an organization by making teams more data-driven in how they conduct operations.
For example, DataOps teams help data teams experiment and innovate at a faster pace and respond faster to changing customer and market demands. In addition, DataOps teams facilitate collaboration across different environments and groups.
What Is a DataOps Specialist?
A DataOps specialist is an information technology professional who’s responsible for establishing a company’s underlying data architecture and delivery models.
Since most companies are transitioning to data-driven environments, they need to create systems for data to move efficiently between ingestion points, users, and destinations.
With this in mind, DataOps specialists create the technologies that data teams use to iterate and design products. They work closely with DevOps engineers, data governance teams, data analysts, data scientists, and data engineers.
Benefits of Data Operations
Companies don’t typically run into data management issues until they start growing and scaling their operations. As companies collect more and more data, processing and managing it can become increasingly costly and complex.
But with a robust DataOps framework in place, companies can more easily leverage data and use it to collaborate and guide projects.
Here are some reasons to consider using a DataOps strategy.
1. Enhance Productivity
DataOps centers heavily around automation. As a result, data teams can spend less time on manual tasks like planning, coding, and monitoring.
This enables data teams to work faster and more efficiently during projects. Teams can have an easier time extracting insights from raw analytics and using them to push projects forward and meet deadlines.
2. Create More Interesting Work
If you want to attract and retain top data scientists and analysts, then you need to offer workflows that minimize manual, boring workflows and empower them to do their best work.
Having a comprehensive DataOps system in place indicates a clear commitment to empowering data teams and positioning them for success. Data teams can spend more time focusing on interesting, advanced data work instead of combing through databases and preparing data for production.
3. Improve Access to Data
Another reason why companies fail to maximize data is because of silos between workers and teams. In other words, nontechnical workers may struggle to access and understand data and apply it to their workflows. In addition, data often sits in disparate locations instead of centralized repositories, making it harder to unlock the full value of all of it.
By setting up a DataOps framework, companies can improve data democratization. At the same time, they can more easily tap into shared datasets and use them to glean insights and become more data-driven.
To illustrate, a large company could use automation to provide quick access to data across multiple sources. Data analytics teams could use this information to more effectively plan, predict, and execute projects and capitalize on opportunities.
4. Tighten Data Security
As companies increase their data consumption, securing it becomes increasingly important. Without clear security policies and procedures in place, companies are at a higher risk of experiencing costly data breaches.
A DataOps framework reduces risk by establishing security and identity access management protocols. It enables companies to more easily track the flow of information from end to end and prevent bad actors from penetrating defenses and lifting sensitive information.
5. Become Data-Driven
Companies across all industries want to become more data-driven, using data to guide each of their decisions. Unfortunately, most lack the management system to make this happen.
By implementing a DataOps strategy, companies tend to have a much easier time extracting and using data. And by accelerating data usage, it’s that much easier to become more data-driven and efficient over time.
Accelerate DataOps with Enov8
To establish a successful DataOps approach, it’s crucial to have the proper tools. This is where Enov8 comes in. If you’re looking to embark on your DataOps journey, Enov8 can assist you with its Environment Manager and Test Data Manager tools.
The Environment Manager provides DataOps teams with comprehensive insight into complex IT environments and systems. Meanwhile, the Test Data Manager detects any potential data security risks in your production data and allows for quick remediation through masking and/or encryption.
Take the first step towards implementing DataOps in your organization by signing up for a free SAAS trial. You’ll receive 100 environment instances, unlimited users, unlimited projects, and full access to our support portal.
This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.
