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Analysts predict that the amount of data will increase 10 times between 2013 and 2020. So it’s no surprise that many organizations are struggling with data health. This article outlines the essential data cleansing steps to reduce the risks of bad data.
Your team could be spending as much as 60 percent of their time on data cleansing steps and processes. As more data floods into the enterprise, developers are finding that traditional – and often very manual – data cleansing techniques are no longer up to the task. The problem becomes harder when non-developers, with limited tools and skills, try to work with bad data or clean it up themselves.
Data cleansing steps in a nutshell
This is a headache for IT managers who are already juggling budget constraints, regulatory issues, and a pressure from above to deliver real and profitable business outcomes.
But, it’s not all doom and gloom. If you follow the right data cleansing processes, you can ensure the integrity and quality of your data regardless of its scale or complexity. To get you started, we’ve boiled down the process into five key stages, so you can see where your current data cleansing processes fall short.
It’s best to complete these steps at the point of entry, as the problem will only get larger and more complex the further down the road you go. It’s a lot like organizing your holiday photos each evening of your trip, instead of waiting to do it all on your return home.
Definition of data cleansing
So here they are – the five key data cleansing steps you must follow for better data health.
The challenge of manually standardizing data at scale may be familiar. When you have millions of data points, it’s both time consuming and expensive to handle the scale and complexity of the data quality management.
In many cases, the volume, velocity and variety of large-scale data makes it an almost impossible task. And as your business grows, the only way to scale the process is to hire more staff to carry out cleansing and validation tasks.
However, with an automated solution, scaling to handle rapid data entry is easy. When you can automatically transform data points to a new, universal, and relevant format, you’ll mature your data strategy and draw more value from your data.
It’s essential to standardize data rules and define cross-organizational structures, and then stick to them rigorously. Here’s how Clover helped HSBC to do just that. It’s a lot like standardization of parts in the automotive or other industries – the fewer options, the easier it is to keep control.
Automating the validation process reduces the cost of manual coding, the amount of time developers spend on routine tasks, and, ultimately, the cost of data processing. Automating this task saves time and also reduces the risk of human error.
Take address validation as an example. Manual address validation tends to create bottlenecks, especially in emerging markets where varying languages and address structures make things difficult.
When Clover worked with one logistics company to automate their validation process, we reduced the number of human interactions by 90 percent and freed up more time for their team to focus on driving business growth. Now, instead of deploying 30 people to manually verify each address, they use one tool across all their systems.
Data deduplication is key to efficient and accurate business processes. It entails getting rid of copies and silo-ed variants of the same data, so you only have one golden copy or as few copies as possible. But manual deduplication of data takes up resources and introduces the risk of human error.
When you’re dealing with a huge number of records across multiple systems, it becomes a constant battle to prevent duplicated data from affecting the quality of business reports.
Duplicated data also increases the chance of inconsistencies between datasets further reducing data quality and muddying the waters. Another negative impact of duplicated data is that it increases your data storage needs, as you’ll waste money storing the same data multiple times.
Automating this process cuts the amount of code you need to write. It’s as simple as removing duplicates from the input data based on a key. You can run the process on autopilot to ensure you cleanse all source data.
When you gain visibility into the health of your data, you can improve your data cleansing process. If you don’t know what needs cleaning, or in what way, you won’t be able to ensure the highest possible level of quality. And, without continuous measures, at some point you’ll lose control and end up in a mess with bad data, yet again.
Monitoring large-scale datasets changes the way you check data health because the complexity and scale of the data makes the process unwieldly. Because of this, finding the staff with the skills to monitor data manually at this scale is often problematic, especially if you’re asking them to broach antiquated legacy systems that they’ve no experience of and no incentive to master.
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Automated data health checks offer a great workaround. You can run data health checks more frequently, and get faster notification if something goes wrong helping developers to identify the cause of the issue faster.
Are you waving or drowning? Automation is a life-raft in an ocean of bad data
With data driving more and more business processes, there’s no doubt you’ll experience an issue with scalability in the coming years. But, if your development team is already over-stretched, the prospect of cleansing and validating an accelerating volume of data can be daunting.
Perhaps the waves of data are crashing over the bow as we speak, and you’ve already noticed the quality of your data is slipping. If you’re unsure of where you stand, below are five signs that you might be drowning in too much bad data:
If these ring true, it might be time to look at automating your data cleansing process. Making this simple change can reduce the data challenge in several ways:
While maintaining data quality is a challenge for every modern business, with the right data cleansing steps and tools, you can avoid becoming lost at sea.
To discover more ways to improve and refactor your data quality processes, check out our dedicated data quality guide.