Even with the vast resources, modern businesses have available to them for dealing with large volumes of data today, the quality of the information is often more important than the quantity.
In fact, if data quality suffers unexpectedly or falls short for whatever reason, entire pipelines can be thrown into disarray, which is obviously not good for productivity.
Thankfully a combination of cleverly implemented SQL and modern machine learning tools can overcome this hurdle, so let’s explore how this is achievable.
The importance of observability
Observability is a major factor in lots of scenarios, whether you are talking about how you monitor SQL server performance or how you ensure data quality is at a consistently high level.
There are a few elements that go towards observability in the latter context, including the freshness of the data, the volumes being ingested, the structure of the management system, and the interconnectedness of sources that might impact workflows in the event of an individual outage.
In essence, this gives you the main metrics you need to monitor. And as you might expect, machine learning is the main way to avoid having to do this manually.
The influence of scalability
Aside from automating the process of pinpointing problems with data quality, machine learning is also well suited to scaling alongside whatever data resources you have at your disposal.
For most enterprise-size projects, an SQL database will be a must-have solution for storing and manipulating the information that pours in from different sources.
One of the perks of SQL as a language for wrangling data is that it is straightforward to understand thanks to its use of English words and unfussy syntax.
In turn this means if you are tasked with implementing machine learning to look out for dodgy data, doing so with SQL in hand will make your life much easier.
Of course it is always possible for false positives to arise, especially as the scale of the data being monitored expands. But even so, SQL and machine learning make amazing bedfellows in this setting.
The choice of tools to consider
Of course there is no need to work on your own machine learning algorithms to parse data and interpret its relative quality in an SQL environment, as there are ample tools available to do just that. The trick is to choose the right one for you, which relies on comparing and contrasting the features on offer.
Open-source options like DVC are tuned specifically for machine learning projects, while Evidently offers similar functionality, albeit with more of a focus on data loss detection and prevention.
The need to retain control
The final thing to think about when monitoring and improving data quality in whatever project you have on your plate is that you can’t put all of your trust in machine learning without applying adequate scrutiny of the outcomes on a regular basis.
Don’t be afraid to get stuck in with some manual tinkering if you feel like your model of analysis needs improving. The work will be worth it in the long run
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