According to analysts, between 2013 and 2020 the amount of data will increase 10 times. With data continuing to pile up, many organizations have a hard time managing their data health. Here we’ll outline the most important data cleaning steps to help reduce the risks of bad data.
Data cleaning and processing takes a long time- your team may spend as much as 60 percent of their overall time on it. Traditional and manual data cleansing takes too long in today’s world, and this issue only becomes worse when limited non-developers try to clean up bad data without knowing how to.
IT managers already have enough on their hands and don’t need extra issues to deal with. But it’s not all bad. If you follow the correct data cleansing process, you’ll have quality data no matter how large or complex. We’ve reduced the process to five key points.
You should complete these steps at the point of entry, as further down the line your data will only become more complex. Doing it now will save you headaches in the future.
Data cleansing is the process of detecting and correcting, or removing, corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty data. The process can be completed with data wrangling tools or as a batch process through scripting.
Here are the five steps for better data health:
- Analyze quality
- Find out if you have a data quality problem
Better Data Health in Five Steps
1: Standardize your Data
Manually standardizing data is a common issue. It’s incredibly time consuming and expensive if you want quality management. The sheer amount of data can make it an almost impossible task. As your business grows, you need to hire more staff to carry out rising amounts of data.
But there’s another way out of this dilemma without hiring more people to do manual labor. With automated solution, scaling rapid data entry is simple. You can transform data points to a new relevant format, and you’ll grow your data strategy and get more value from your data.
It’s important to standardize data rules and define cross-organizational structures, and then to use these same rules all the time. Here’s how Clover helped HSBC do that. The fewer options data-wise, the easier it is to monitor all of your data and keep everything clean and easy.
2: Validate your Data
Validating your data via automating reduces manual coding costs, time, and the cost of data processing. You truly can’t go wrong by automating here. The risk of human error is also lower, which is an added bonus.
For example, consider address validation. Manually, this validation can create bottlenecks, especially in emerging markets where different languages and structures can muddle things. When Clover worked with a logistics company to automate this process, they reduced human interactions by 90 percent. This opened up so much more time for their team to work on other projects. One system can replace a 30-person team.
3: Deduplicate Data
This is the key to accuracy and efficiency in your business processes. Deduplicating data gets rid of copies and variants of the same data, so you have as few unnecessary copies as possible. Manually, this takes up loads of time and has the risk of human error.
When you have many, many records across many systems, it’s hard to keep duplicated data from negatively affecting the quality of your reports. Likewise, duplicated data increases your chance of inconsistencies between datasets, which reduces your data quality. It also increases your data storage needs, storage that could be used for other things.
Simply automate this process. This cuts down on the amount of code you need to write, and the amount of time it takes to write that code. Remove duplicates from the input data based on a key. You can run this process on autopilot to clean all your primary data. It’s that easy!
4: Analyze Data Quality
If you don’t know what data of yours need cleaning, or how to do so, you can’t ensure the best level to quality for your data. Without automated continual measures, you’ll end up overloaded with bad data again and again. It’s a vicious cycle.
Watching over large-scale datasets can be incredibly complex and difficult. Finding a staff to manually monitor these huge datasets can be even harder, as sometimes you’re running on antiquated systems that these people have no experience with.
Watch our data quality webinar to get a sense of how and why you should design and build every step with bad data in mind. Automated data health checks are a great way to keep your data as clean as possible. You can run these checks whenever you like and receive fast notifications and information.
5: Find out if you have a Data Quality Problem
With data becoming more and more integral to essential business processes, it makes sense that you’ll run into a scalability issue at times. If your development team is already busy around the clock, the prospect of having to clean your large amount of data can be difficult to say the least.
If you don’t know where your data stands in terms of cleanliness, you’ll want to check on its quality. Here are five signs you may have too much bad data in your batches:
- Reports that should confirm each other disagree with conflicting numbers.
- Putting together ad-hoc and regulatory reports is difficult.
- Brining in new data sources is too expensive and painful.
- Reconciliation and validation requires a large amount of workers, and lots of repetitive manual work.
- Consumers of data spend most of their days cleaning and prepping their data.
If any of these sound like you, you may want to look into automating your data cleaning process. This will save you time and realign the focus of your data team towards business growth over manual labor. Automating will reduce the chance of errors that can come from human error. Your scale will also instantly meet the requirements of complex data projects.
Maintaining quality data is difficult for everyone in the modern era. With the right steps and tools, however, you can avoid being drowned in bad data.
For more ways to improve your data quality processes, look at our data quality guide.