Introduction
The saying goes, “Garbage in, garbage out.” It’s a truism that applies to data and analytics projects. The quality of your data is the single most important factor in any analytics project. Without clean data—and more importantly, good people who know how to clean up the mess—there’s no hope for success.
Data quality is the single most important factor in any analytics project.
Data quality is the single most important factor in any analytics project. It’s also the foundation of your analytics project, and a good data quality process can make an analytics project successful. Data quality depends on good people–the right people, who are trained well and have the right tools at their disposal to do their jobs well.
When it comes to data quality, there are three main areas that need attention:
- Data governance (or “metadata”) – Who owns what? What are the rules for managing information? What are the standards for recording information accurately? How do we ensure that all relevant parties know about these policies and procedures so they can follow them?
Data preparation takes longer than you think.
Data preparation is the most time-consuming part of any analytics project.
Data preparation can be a team effort, and it often involves iterating over different models until you find one that offers a good fit for your data. Data scientists are not necessarily trained to prepare data for analysis, so this task usually falls on someone else’s shoulders–someone who isn’t familiar with how machine learning works or what kinds of insights it might yield. In short: if you’re planning on doing any sort of numerical analysis (and especially if your goal is to create models based on historical data), expect that your project will take longer than anticipated because of all the steps involved in preparing and cleaning up the inputs before running them through an algorithm.
Good data quality depends on good people.
Data quality is a team sport. It’s not something you can do alone, and it’s not a one-time effort. You need to hire people who are passionate about data quality and make them part of your culture.
It takes time to build a data quality culture; you need to nurture it over time so that everyone understands how important it is for the business and what they can contribute towards improving their own data collection methods or processes at work–or even at home!
There are many ways to clean up data.
There are many ways to clean up data. The most common is data cleansing, which involves removing duplicates and other errors from a dataset. Data scrubbing refers to the process of removing sensitive or private data from a dataset while preserving its structure and meaning. Data wrangling refers to using scripts or tools to manipulate and transform large amounts of messy data into a form that can be analyzed easily by computers (for example by converting text documents into spreadsheets). Finally, preparation refers to all of the steps required before an organization starts collecting any kind of information because these steps will determine how well their future analysis efforts will go
If you don’t have clean data, there’s no hope for analytics success.
If you don’t have clean data, there’s no hope for analytics success.
There are three main reasons why you need clean data:
- Clean data provides a solid foundation for analysis. If the information in your database is inaccurate or incomplete, any conclusions drawn from it will be skewed at best and completely wrong at worst. If your business relies on accurate data to make decisions and take actions based on those decisions and actions, then dirty data can be incredibly dangerous to its bottom line–and even its survival as an entity!
- Cleaning up messy files takes time away from other important tasks like building new features or fixing bugs in existing ones (which means more revenue). This is especially true if there aren’t enough resources available on hand right now–in which case I’d suggest taking advantage of our free trial offer so we can help out with cleanup tasks when needed! “If only we had someone else who could do this work,” many companies say when faced with massive amounts of messy files needing attention before they can start using them properly again.”
Without clean data, there’s no hope for analytics success
Data quality is the single most important factor in any analytics project. It’s also one of the most overlooked factors, because data preparation takes longer than you think and good data quality depends on good people.
Data preparation can be broken down into three steps:
- Cleaning up your raw data so it’s ready for analysis (removing duplicates, removing bad values)
- Making sure that all of your cleansed files have consistent formats (like making sure all dates are formatted as yyyy-mm-dd)
- Standardizing fields across multiple datasets so they’re easier to combine
Conclusion
We hope that this article has helped you understand the importance of data quality and how to get it. If you have any questions or comments, please leave them below!
