How you collect, analyze, report, and draw inferences from your organization’s data will significantly impact your business.
In traditional data processing techniques, this process is usually manual — making it tedious, time-consuming, prone to errors, and including high labor costs.
Though data processing has evolved over the years, it still remains bulky and needs a lot of manual handling. As a result, it suffers from inconsistent quality, long cycle times, waste, inflexibilities, and bottlenecks. Here is where DataOps comes in.
According to the DataOps manifesto, DataOps values:
– individuals and interactions over processes and tools
– working analytics over comprehensive documentation
– customer collaboration over contract negotiation
– experimentation, iteration, and feedback over extensive upfront design
– cross-functional ownership of operations over siloed responsibilities.
As such, it operates on a set of best practices, workflows, mindset, and architectural patterns that improve quality, velocity, collaboration, and access to data. As an organization, you can leverage the data you gain in your DataOps process to improve organizational performance — and to make more informed business decisions. If you seek to improve your organizational performance by improving your DataOps, here are four things you should know.
1. Improving data quality
DataOps borrows from DevOps, which is a framework of software engineering that iterates and expedites the software development lifecycle to develop quality software.
DataOps borrows from the DevOps framework and uses iterative processes to speed up the building of data pipelines. The pipelines are then used to funnel high-quality data for analysis and interpretation. It streamlines and optimizes the data lifecycle from acquisition to application.
As such, DataOps improve the quality of data in 3 key ways:
– Data Validation — in a traditional data workflow, data validation is often a step that is glossed over and seen as slowing down the data process. However, with DataOps, there is data integration that incorporates and automates the data validation process. This mitigates the risk of using inaccurate data.
– Data Integration — DataOps integrate data from different sources and standardize it, yielding uniform data.
– Data Cleansing — with DataOps, automated data cleansing removes duplicates, incomplete or inconsistent data, thus reducing the risk of errors, and improving overall data quality.
2. Increasing efficiency
One of DataOps’ significant strengths is increasing efficiency, which every organization needs. Whether it be streamlining workflows or reducing downtime, increasing efficiency is critical to improving your organizational performance, which DataOps helps you achieve.
For starters, DataOps helps in streamlining your data processes. It automates repetitive tasks, reducing manual errors, and allowing your data team to focus on the main data work. This results in faster time-to-insights and time-to-market for new products or features.
As DataOps can streamline the data processes, it reduces downtime in the data management process, and acts as a data security platform. Since processes are automated and optimized, there is little to no downtime, leading to increased productivity. It ensures critical business functions are not impacted, enables an accelerated collaboration among teams, and leads to faster decision-making and problem-solving.
Lastly, DataOps enables you to scale your data operations as your business grows. It can help your organization respond swiftly to the growth and keep up with your business needs.
3. Enhancing collaboration
The success of your organization depends heavily on how well employees work together. 86% of corporate executives and employees cite the lack of collaboration as the top reason for workplace failure. Your data team is no different. Its success is predicated on how well it collaborates.
Using DataOps will enhance collaboration in your organization in several ways. To begin with, it will improve data accessibility by creating a standardized procedure for collecting data, managing, and sharing it. It will make data readily available to all data staff and make collaboration and decision-making easier.
By implementing a DataOps system, it is easier to establish a common understanding of data and its importance in your organization. This shared understanding of data can lead to better collaboration between teams working with the data, as there is a shared common understanding of how to handle data in your organization.
It is also easier to foster collaboration across teams as DataOps breaks down data silos and encourages collaboration across teams. It can help bring a different perspective and expertise to solving your data challenges. While facilitating collaboration across teams, it also increases transparency, builds trust, and facilitates open and honest communication.
4. Driving innovation
According to The data-driven enterprise of 2025 guide, enterprises that leverage data to innovate are going to see an increase in their earnings. Adopting a DataOps method can help you capture this forecasted outlook for your organization. Here’s how DataOps can drive innovation in your organization include:
Faster time-to-market
The time to market (TTM) is the time from the conception of a product to its release into the market. Research shows that the first-mover in business enjoys the competitive advantage of market share, revenue, and sales growth. This being the case, it is prudent that your product development strategies are expedient and you’re the first mover.
Since DataOps streamlines your data pipelines and reduces the time it takes to draw insights from data, it inadvertently optimizes your product development process. As an organization, you improve TTM for your new products and services, gain first-mover advantage, and stay ahead of the competition.
Improved data quality
Data is central to innovation. Traditional data frameworks are difficult to maintain and most of the time are outdated quickly, making them unfit for productive purposes like innovation.
Managing your data through DataOps ensures that you are working with up-to-date data that reflects the real state of the markets. This means you get to work with accurate, consistent, and complete data, facilitating innovative solutions.
Better collaboration
DataOps acts like a data access service that allows different teams to have access to the organization’s data. It breaks down data silos and facilitates easier collaboration between teams when driving innovation. This results in a richer perspective on developing new products.
Agile decision-making
Traditional business decision-making processes are usually slow and are more than likely to make the innovation process slow and frustrating. Here is where agile decision-making takes place.
Agile decision-making is a decision-making process in which decisions are made collaboratively, iteratively, and with transparency. This means all stakeholders in the innovation process regularly get feedback on changes and feedback as the innovation process goes on. The team discusses issues as they arise, and solves them together.
Automation and AI
DataOps help identify and isolate bottlenecks within an organization’s data pipeline, and eliminate them by streamlining the data process. All of this is possible because of AI. The automation of manual operations in data handling and integration of agile workflow processes makes it possible to develop innovative solutions that wouldn’t be possible without these technologies.
Conclusion
Improving your organization’s performance is a multifaceted endeavor. It requires thorough scrutiny with all the issues in your organization, and identifying means to improve them. One thing you can start with is improving your DataOps.
DataOps makes your data easier to manage and use in your organization. It may require an initial investment of resources and time, but after that, it is a walk in the park. Be sure to improve your DataOps and get going with improving your organizational performance.
About the Author
Ben Herzberg is Satori’s Chief Scientist and VP of Marketing. Ben is an experienced tech leader and book author with a background in endpoint security, analytics, and application & data security. Ben filled roles such as the CTO of Cynet, and Director of Threat Research at Imperva.
Featured image: ©Alex
