4 Data Cleansing Steps You MUST Follow for Better Data Health

Written by
Pavel Najvar
Pavel Najvar

With analysts predicting that the amount of global data will increase 10 times between 2013 and 2020, it’s no surprise that many organizations are struggling to maintain the efficiency of their cleansing and validation processes.

While you might not realize it, your team could be spending as much as 60 percent of their time cleaning and organizing data, with little time left over for data transformations. As more and more data floods into the enterprise, developers are finding that traditional – and often very manual – data cleansing techniques are no longer doing the job. The problem becomes still harder to surmount when non-developers, with limited tools and skills, are required to clean or draw value from these ever-growing pools of bad data.

The consequence of 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 four key stages, so you can see where your current data cleansing processes fall short.

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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.

So here they are – the four steps you must follow for better data health.

1.      Standardize your data

The challenge of manually standardizing data at scale is probably a familiar one. 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 retain control.

2.      Validate your data

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. This really should be automated – leaving this to manual processing can come back to bite you. Take address validation for example.

Manual address validation tends to create bottlenecks, especially in emerging markets where varying languages and address structures make things difficult. However, when we 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.

3.      Deduplicate data

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 needlessly waste money storing the same data multiple times.

Automating this process takes the repetition out of data quality management and minimizes the amount of code you need to write. It’s as simple as removing duplicates from the input data based on a key and can be run continuously to ensure you cleanse all source data.

4.      Analyze data quality

When you gain visibility into the health of your data, you can better prioritize 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 manually monitor data 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 provide faster notification if something goes wrong, and enable developers to immediately identify the cause of the issue.

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 incredibly 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:

  1. Reports that should confirm one another end up disagreeing and show conflicting numbers.
  2. You struggle to quickly put together ad-hoc and regulatory reports.
  3. Bringing in new data sources causes you to sweat because it’s too expensive and painful.
  4. Reconciliation and validation requires large teams, and lots of repetitive work.
  5. Consumers of data spend most of their day cleaning and preparing their 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 a number of ways:

  • Save time and realign the focus of your data team with business growth
  • Reduce the introduction of errors that can come from manual processes
  • Scale immediately to meet the requirements of large or complex data projects

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.

 

Posted on May 30, 2019
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