Why you should seriously consider implementing data ops

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Data is more important now than ever, but as information flows and grows in greater volumes, organizations of all sizes face the perpetual challenge of how to store and analyze it effectively.

The complexities involved can seem overwhelming in isolation, yet with the rise of DataOps these can not only be overcome but actively exploited for the benefit of all involved.

If you are a newcomer to the concept of DataOps, or just need convincing about the advantages of implementing this type of strategy, here is a look at the reasons which might motivate you to investigate further.

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DataOps is a cross-discipline pursuit

One of the main aims of DataOps is to empower both data scientists and engineers with the means to communicate and collaborate with one another to help businesses glean actionable insights from the information that they generate.

Once you get an understanding of what DataOps really is, you will be able to appreciate why this interconnectedness between different departments and disciplines is so central to its success. Rather than groups of professionals working in isolation, it allows the intermingling of expertise and thus allows for solutions to be found and disasters to be averted collectively.

Complexity is counteracted

The reason that dealing with data has become such a momentous undertaking is that today there are so many systems involved, in addition to a whole host of workers being responsible for them, compounded by the cavalcade of data products that exist at the other end of the pipeline.

DataOps intends to use the combined might of individual teams so that bottlenecks can be eliminated, errors dealt with efficiently and automation implemented wherever possible so that information can flow smoothly and flexibly.

Predictability is also an important tenet of this practice, since the delivery of data is something which needs to happen consistently at all points.

While behind the scenes there will certainly be an unavoidable degree of complexity, the role played by DataOps is one of making this manageable.

Development is catalyzed

When working on data pipeline projects, traditionally it was necessary to cope with high volume processes in an entirely manual way, which ate into resources and also increased the likelihood of human error coming into play in determining the outcome.

DataOps intends to both speed up the development process and reduce the chances of complications occurring, making everything quicker as well as more resilient even when tighter schedules are being used.

Ideally this will mean that engineers are able to conclude work on one project and begin investigating the next big thing far faster than in the past, and with greater confidence that their efforts will have been fruitful.

Likewise the amount of time it takes to extrapolate actionable insights will be reduced in this context as well, while the scalability of solutions can be maximized to make sure that the organization is able to achieve optimal value from its investments.

Guarantees can be made

With a well regimented DataOps practice in place, it is possible for an organization to effectively set in stone a certain selection of truths that will be replicable for all employees who come in contact with information.

In short this involves guaranteeing that the data which is made available is not only accurate and available on tap, but that it is also comprehensively synchronized across any other relevant systems to ensure consistency and precision.

Automation is once again a cornerstone of this aspect of the practice, since it is difficult to achieve such guarantees on the scale that large organizations require using any older, manual techniques.

This ties into one of the other advantages of DataOps, which is to say that it has an incredibly focused aim; one which is not as broad or mutable as DevOps, to which it is regularly compared. Data is the unifying asset of DataOps and so it is much easier to construct an effective practice when this is kept in the forefront of the process.

Performance is a factor

In the world of data, it is not just the need to extrapolate impactful insights which is important, but also to be able to do this at a rate that renders them even more relevant.

DataOps is all about improving observability, so that the systems which play host to the data are themselves scrutinized in an effort to pinpoint performance problems and ultimately find ways to optimize them.

While automation can be used to a degree in this context, it is the human specialists who will benefit most from this aspect.

Conclusion

Whichever way you look at it, DataOps is here to stay and there are myriad perks that come with implementing an effective practice of this kind, so long as you are conscientious with your approach.

In particular it is organizations which want to get the edge over the competition, while also staying on top of their own data management responsibilities, that will have the most to gain.