DataOps provides an all-encompassing, end-to-end approach to managing the lifecycle of the information which passes through your organization, and so at its core, it is an asset that should generate value over time.
Here is a look at how exactly the benefits of DataOps are felt in the context of stewarding information from start to finish, and why this should matter to modern organizations.
Orchestrating complex environments consistently
One of the main advantages that data operations bring to the table is their ability to ensure that even the most multifaceted and intricate data environments can be controlled and understood efficiently.
You can not only use DataOps practices to see how data is generated, stored, analyzed, and harnessed but also to catalyze the lifecycle of this information so that it moves fluidly through the systems you have in place and delivers a consistently satisfying experience to end-users.
This is one of the places in which businesses can derive value since if even the most fragmented data pipeline is properly managed, it can create fewer issues that need to be dealt with, while also lessening the likelihood that internal and external users will have complaints that need to be dealt with. Organizations should therefore be able to free up resources to focus on other areas, safe in the knowledge that DataOps is doing the heavy lifting throughout the life cycle.
Drilling down into integration
While DataOps can clearly be impactful in the context of simplifying an existing infrastructure made up of multifaceted elements, it is also worth noting that value can be derived by empowering organizations to pursue more ambitious projects when starting from scratch.
Start-ups, for example, need not be daunted by the prospect of having to cajole data from disparate sources and integrate it into a lifecycle that is not tried and tested. Instead, they can get a head-start and compete with established rivals even if they are drawing in strands of information from databases, APIs, IoT devices, and everything in between.
Integration is the cornerstone of what makes DataOps worth implementing, and it is the removal of artificial limitations on businesses that makes all the difference.
The appeal of accuracy
Even if data is stored, transported, and transformed efficiently throughout its life cycle, all of this effort will be for nothing if it is not also accurate at every point on this journey.
DataOps sets out to enshrine accuracy through frequent and consistent validation; a process that has been difficult to achieve in the past without a cohesive strategy and the right tools on hand.
The accuracy of data within a given infrastructure is of course not just down to the automated processes at play in its management, but also the experience for end-users who may manually interact with it. This is why DataOps looks at both areas and seeks to find flaws in processes and policies, while also addressing UX inconsistencies and finding solutions that suit everyone.
In time, any organization can find value inaccurate data, and without DataOps this would be almost impossible to guarantee, especially in larger-scale operations with many moving parts.
Achieving optimization through monitoring
DataOps is not just a set of policies and solutions you put in place and then leave to their own devices; it can also deliver value by giving you the means to monitor and make optimizations to data lifecycles as time passes and the needs of your business change.
Of course, the benefit of modern services and strategies is that they allow you to automate, to a greater or lesser extent, the observation of your data assets and thus make it simpler to extract actionable insights from what is being monitored.
Keeping tabs on the way that data is processed from minute to minute is clearly useful, but it also ensures overarching, long term perks. For example, DataOps can help you to determine a suitable plan for any changes to capacity which may be required in the future. Given that the data deluge that most organizations are facing at the moment is only going to intensify, such future-proofing and early preparation are ideal.
Self-perpetuating value generation
Ultimately it is arguable that every aspect of data operations feeds into enhancing the value that firms can extract from the resources at their disposal.
As the data lifecycle speeds up, allowing information from different sources to be integrated, validated, and leveraged, the value of data lifecycle management and the broader impact of DataOps will only grow. What is more, this is relevant not only to major corporations but to companies of all sizes and across every industry that wants to grow revenues, so failure to embrace it sooner rather than later will become a bigger issue.
