October is here and with it comes jack-o-lanterns, skeletons, witches, and yes…bad data. There are many spooky ways your data can haunt you, whether there’s too much of it, it’s not reliable, or it provides no business value. On top of these quantitative issues, an unrefined team lacking in good practices can further exacerbate the situation. Let’s dive deeper into some of these pain points and […]
October is here and with it comes jack-o-lanterns, skeletons, witches, and yes…bad data. There are many spooky ways your data can haunt you, whether there’s too much of it, it’s not reliable, or it provides no business value. On top of these quantitative issues, an unrefined team lacking in good practices can further exacerbate the situation.
One of the scariest problems that data teams run into actually stems from the processes they have in place. Lack of collaboration, both within a team and with end users, is just as detrimental as lack of automation to getting fast and useful feedback. Much like software development, where requirements are constantly evolving, data quickly goes stale and we need to get what we can from it before that happens. Without an iterative, reflect-and-adjust mindset, we fail to take advantage of all the data has to offer.
In today’s Big Data world, oversaturation of data is a new and legitimate concern that must be accounted for. There is no longer time to manually check the quality of every data entry; automation and pipelines have taken over and teams need to shift their mindset to embrace the future that these tools have enabled. If teams don’t take advantage of the processing power that these new technologies offer, they will quickly fall behind in the ever-growing contest to tackle big data.
Assumptions about the trustworthiness of your data can come back to bite you later. Data-driven decision making is only as good as the underlying data; approximations, default values, inconsistent or incomplete fields can all mislead you into a false conclusion. Data providers may not be aware of how the data is intended to be used and so it is not always safe to assume any degree of accuracy.
Even if you tackle all the other nightmares on this list, data is useless if does not contain pertinent information to the problem you are trying to solve. The tricky part when it comes to data relevance is that it can’t really be measured in a generic sense. It very much depends on context and the problem you are trying to solve.
DataOps, at its core, is a set of best agile practices combined with data analytics and pipeline tools. Agile practices are utilized to reduce cycle time and improve data quality by introducing an iterative approach with constant feedback, which is necessary when requirements are constantly evolving. Efficient response time reduces the chance that becomes stale before it gets used. Quick iterations also allow more frequent collaboration with the data end-users, who can, in turn, voice their opinions and propose changes in focus that can further increase the value of the data.
Agile practices inherently boost communication between teammates. With daily standups to facilitate conversation and end of sprint retrospectives to allow team members to voice concerns, teams can make changes based on maturing priorities and the needs of the group. Innovation naturally leads to constantly changing priorities and needs and successful teams will roll with the punches, look for ways to improve, and encourage organizational adjustments or the adoption of new technologies.
DataOps teams make use of a data pipeline to help reduce cycle time and improve data quality. A data pipeline continuously and automatically consumes data as it becomes available. The pipeline (sometimes known as a data factory) contains a proven series of activities aimed at improving data quality, given its context, a specific objective and a set of initial conditions. Included in this setup is what is known as a data pipeline orchestrator, a piece of software that controls the automation and analysis at each step of the pipeline. Iterative adjustments to the pipeline are known as orchestration and are vital to maintaining the conditions in which data will provide the most value possible.
The ability to automatically feed data into this pipeline is key to processing large volumes of data, as it gives the team the ability to harness the value of all the data available without the headache of manual input. Built-in validation allows this process to run uninterrupted, hugely benefitting the amount of data that can flow through the pipeline This automated system includes thorough checks for data format, letting you know quickly when something is off. What’s more, you find out right away whether or not your data contains information relevant and useful to your context.
DataOps is just as much about the people and the processes as it is about the data; it’s impossible to implement a successful pipeline without the correct mindset on how it will be maintained and updated. Iteration and communication are the keys here, as quickly finding points of failure and making constant improvements based on end-user feedback are vital to improving the quality of your data. Scare the bad data ghouls away by shifting your team to the DataOps mentality today!
If your data nightmares keep you up, take the next step towards understanding your DataOps.
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