The deluge of data under which most modern organizations find themselves operating can be overwhelming. This is something that the evolution of DataOps has intended to tackle, with great successes achieved in many contexts.
That is not to say that DataOps teams are not faced with their own challenges which need to be dealt with efficiently and consistently. Here is a quick look at some of the most common complications involved in DataOps and the means by which solutions are found.
Opaque usage of data
With so much information on tap, keeping track of where it is being used, what purposes it is being used for and whether this usage is compliant with data protection regulations is not always easy.
This is where DataOps teams need to make inroads towards ensuring transparency of data usage, not just because this can help to overcome performance issues and catalyze troubleshooting of hiccups, but also because it ensures that information is handled in line with the law.
Obtuse data pipeline orchestration
Being able to automate the most tedious aspects of dealing with data is a blessing of the information era, but unless the pipeline by which these processes are organized is completely comprehended by the DataOps team, its advantages will evaporate.
Understanding data pipelines is not just a team effort; it is something that should be considered and addressed in a wider scope so that as much insight and effective decision-making is possible to achieve. Whether this comes from optimizing deadlines to avoid delays creating disruption during the working day, or pinpointing hardware-related performance bottlenecks, collective and collaborative steps can be taken to address complications here.
Even with the power of contemporary infrastructures at your disposal, there are still finite resources available for you to call upon to manage the data-related workloads that need to be completed. This is an obstacle in its own right, made all the more pertinent because if you do not know how quickly a given task will be completed, you could end up with hardware capacity becoming saturated as runaway jobs monopolize CPU cycles and memory allocations.
Once again automation can come to the rescue here, with DataOps experts needing to leverage all of the analytical tools at their disposal to not only see how workloads are being balanced at the moment but also predict how things might shift in the future and set contingencies in place to prevent the aforementioned conflicts from arising.
The apps running on your hardware in order to glean actionable insights from the data you collect can themselves be the culprits in compromising performance and causing inefficiencies.
DataOps should be concerned with diagnosing the untoward app behaviors which will inevitably crop up at certain points. Team members are not disarmed in this endeavor, and can instead turn to tools designed specifically to provide them with the analysis of app performance that they need to root out troublesome processes.
Furthermore, visualization tools are a potent part of this modern ecosystem, since they may make it easier to both identify problems and share them with others who may not have the same technical background in data science or programming. It is this role played by DataOps that is arguably the most vital in modern business.