How data management is changing due to DataOps

The rise of big data in the past decade has created almost endless opportunities for businesses to harvest, analyze and exploit information to their advantage. It has also led to significant challenges, as the volumes of data involved have often been significant enough to overwhelm incumbent data management strategies.
big dataThis is where DataOps steps into the fray, with the intention of addressing the inadequacies of current approaches and ultimately empowering organizations to leverage data efficiently across all departments and at all levels.

Here is a look at how practices are changing in the face of the DataOps revolution and the benefits that these shifts bring with them.

Collaboration is setting the agenda

Modern data operations aim to shake off the shackles of past processes by making collaboration a key consideration of the way information is managed.

This is in part achieved because data is being unified and made more accessible, which in turn means that users can highlight problems with quality and make other pertinent points that can then shape augmentations and ameliorations going forwards.

This inclusive take on data management means that all users are engaged and invested in the overarching data journey that a business is taking. They will also feel comfortable and confident enough with the processes to let technical team members know when problems arise, rather than feeling like they are working in isolation.

Automation is alleviating manual tasks

Getting data in its raw form and converting it into a usable state used to require a herculean effort, often taking up time and resources that could be put to better use in more complex duties.

One of the main tenets of modern DataOps is that these tedious jobs should be automated wherever possible, meaning that data reaches users as cleanly and cohesively as possible.

The upshot is that team members do not need to dedicate any of their precious time to handling necessary but fundamentally mind-numbing tasks manually, and can instead expect automation to take the reins until information is more ordered and applicable.

Cultural issues trump technological concerns

Before the era of DataOps got underway, if there was an issue with data management then the general assumption would be that the underlying hardware or software was to blame in some way for the resulting errors and inefficiencies.

Today, this received wisdom has been turned on its head and decision-makers are instead encouraged to consider how the organizational culture may be responsible for such bottlenecks.

With so much being invested in big data projects, being attuned to the way that the mindsets of team members can impact progress is a necessity.

Ideally this approach should result in improvements being made not just on a standalone basis, but perpetually over time as needs and requirements change and projects evolve.

The changes to data management brought about by DataOps are still taking place across many firms, and in the long term the effects will likely influence every industry imaginable for the better.