Data Cleansing Steps You MUST Follow for Better Data Health
According to a survey, data will increase tenfold by 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 sixty per cent of their time on doing anything but developing new features or products because they're so focused on dealing with your companies garbage code and bad database schemas! As more junk gets pumped into your database, developers find that traditional – and often very manual – database cleansing techniques are no longer up to the task. The problem becomes harder when non-developers, who don't have the requisite tools or skills, try to work with bad data or clean it up themselves.
So here are five key database cleansing steps you should follow for better data health.
Standardize Your Data
As your business grows, manual data entry tasks become prohibitively expensive. There's no scalability in manually handling the intricacies of multiple data formats and cleaning error-prone data. Consider how much you could save by automating tedious processes? It's essential to leverage technology for intelligent data validation - when you can convert unstructured inputs into a single, unified format, including errors harvested from many places and fields defining relevant rules for every element of the information. An automated solution helps you standardize data rules and define cross-organizational structures. The more rigorous your system is around how you handle data quality controls throughout your organization, the easier it will be to scale with rapid growth because everything will have already been defined, even down to the details at the lowest levels.
Validate Your Data
Automating the validation process reduces the cost of manual coding and the amount of time developers spend on routine tasks. This is a high-risk process for many reasons, including work quality, human error, and others. Automate your validation processes to save time and money!
Analyze Data Quality
Ensuring that all data is highest quality can be a challenging undertaking. By monitoring your data quality and then continuously measuring it, you can ensure that your company's data always meets its data quality guidelines. However, to gain maximum visibility into your internal system and determine where problems might lie, we recommend automating the process as soon as possible to keep the entire procedure at scale more manageable. This gives you a better chance of maintaining consistent levels of quality during an expanding initiative while improving efficiency and lowering costs by reducing staff involvement in the process. In addition, using automated methods to check your data health offers several advantages not available with manual processes.
Find Out If You Have a Data Quality Problem
Sometimes it seems like everything is coming at us all at once. This can be very unsettling, especially because we don't always have the resources available to ensure that we continue our day-to-day operations as smoothly and efficiently as possible. In some cases, the issues only become exacerbated due to how fast the data is coming in. As a result, there are several telltale signs that you may be drowning in bad data:
Reports that should confirm one another end up disagreeing and show conflicting numbers.
You struggle to put together ad-hoc and regulatory reports.
Bringing in new data sources causes you to sweat because it's too expensive and painful.
Reconciliation and validation require large teams and lots of repetitive work.
Consumers of data spend most of their day cleaning and preparing their data.
It might take time to automate your database cleansing process if it is true. Making this simple change can reduce the data challenge in several 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
Managing data quality can be confusing and expensive for many businesses. Still, you can use tools that might help make the experience less overwhelming and potentially more cost-effective. Check out our website to discover more ways to improve outsourced business and refactor your data quality processes.
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