Best Data Engineering Practices for Ensuring High Data Quality

In the modern-day, useful and useless data can be the deciding factor between making the right or wrong decision in business. So much so that particularly bad data quality could deprive a company of up to 25% of its total revenue. It goes without saying that decision-making must be an informed process. And because of that, it’s only natural that the information you use has to be correct, relevant, and up to date. The larger the scale of the business, the more crucial data quality becomes. Any miscalculations caused by incomplete, duplicated, outdated, or irrelevant data are costly, wasteful, and time-consuming.

Listed below are some of the best data engineering practices to ensure the data your company uses is as good as it can be:

  1. Eliminating human error

  2. Deduplicating and completing data

  3. Making data consistent

  4. Streamlining data collection

  5. Combating data overload

1. Eliminating human error

It’s no surprise that human error is something that data engineers should strive to minimize as much as possible. If the source of important information is itself prone to error and mistakes, that must be addressed.

For example, if there are forms being used where you have users input data manually, incomplete or empty fields may quickly become a common occurrence. That’s why you need to make sure that all important fields and data inputs are made mandatory to complete before submitting a form. Likewise, consider switching out text fields for drop-down menus or any other kind of option input, where possible.

This is a very simple and quick method that can cut down the amount of incomplete or incorrect data that comes from human input.

2. Deduplicating and matching data

Data duplication is a serious data quality issue that plagues any system and any industry. Be it because of duplicate entries, multiple systems, or countless data silos – information eventually gets duplicated, triplicated, and so on. Take Netflix, for example: in 2015, their system started producing duplicates for the primary key and the data system did not know how to treat it. As a result, Netflix was down for 45 minutes all over the world. That’s how serious it may get.

Monitoring for duplicates allows eliminating nearly 30% of possible data errors. That said, keep in mind that duplication is a complex problem, and there’s no one-size-fits-all solution for it. The issue here is to do that at scale, not on a friction of data. The ideal scenario for preventing the duplication problem would be monitoring unique values and observing data volumes that indicate the problem. To do that, you need to have a scalable system in place that will automatically monitor every aspect of data. With that in mind, we’ve built Masthead to back you up and catch data anomalies as soon as they appear in your dataset.

3. Making data consistent

Data consistency is imperative to a system functioning properly. With so many units, symbols, and values available for the same entries, consistency is key in making sure there are no issues when data is being exchanged across different systems and databases. A wrong data format used in one instance can cause major data errors across the entire system. The most well-known example of this is when poor data consistency caused NASA to fail a $327.6 million mission. The Mars probe was thrown off-orbit and lost forever, all because of one piece of ground software that provided imperial units, instead of metric ones.

For this reason, data pipelines must be audited and monitored in an automated way. As soon as you find any inconsistencies, you should deal with them quickly to prevent potential headaches and corruption of data. To automate this process, data engineers use monitoring inside automated pipeline tools like dbt or Dataflow, but this is something you need to know how to work with. You also need to have a solid understanding of the data you expect to have.

The drawback of this approach is that implementing and maintaining monitoring tools is, by itself, a time-consuming task. On top of that, to implement automated monitoring properly, data professionals need to be familiar with their data, which requires a lot of work hours.

To take that off the data engineers’ plate, Masthead utilizes ML to build a model for every data table and view to determine expected values and data types to ensure data consistency in the data teams’ data warehouse.

4. Streamlining data collection

With lots of different systems where data can come from, and at scale, automation continues to be a trend for a reason. Current technologies enable reliable data gathering and help provide the most up-to-date data possible; however, these methods have their own drawbacks. When there are so many data streams converging at a single point, all those duplicates, poor data quality, and all the other things become a constant issue.

Today’s tools allow embedding various monitoring/testing options to ensure accurate data in pipelines to the data team warehouse. However, as these tools deliver data to the warehouse, they focus on the process, rather than the resulting tables and views data teams work with to deliver value to the business.

5. Combating data overload

Last but not least, let’s talk about simply having too much data. Data overload is what happens when all of the modern technologies and techniques available are utilized to collect as much data as possible. This results in massive amounts of data of all varieties being gathered. Unfortunately, having too much of a good thing can be a bad thing.

Think of the impact big data has on the environment. Data does not live in an actual cloud — it is stored in physical data centers. which are reported to consume more than 2% of the world’s electricity and generate about as much CO2 as the airline industry.

At Masthead, we stand against collecting data just for the sake of it. Minimizing the amount of insufficient data will help your business become more sustainable, both in environmental and economic terms.

Dealing with irrelevant data and putting so many work hours into doing just that is a major loss of efficiency, and a prime opportunity for optimization — as evident from countless reports of what data scientists spend the majority of their time doing – which is cleaning up.

Our ultimate goal is to deliver data quality at scale for every organization by ensuring that every piece of data is reliable and valid in decision-making.

To sum up, your data is not only an asset but also a significant environmental and economical liability. At Masthead, we strive to help you save resources on data audits, minimize costly data errors, stay on top of any data anomaly, and have trustworthy data stored in your data warehouse.

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Data Engineering, Data Engineering Practices, Data Quality

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