SPONSORED POST
This sponsored post is produced by Cloudingo.
What is data quality?
In a world of big data, you’re only as good as your data.
Data is a strategic corporate asset. Quality data refers to data that is accurate, complete, effective, and supports optimal business performance. High-quality data serves as a solid foundation for success, enabling better business decisions.
And data quality management has become a critical issue for business intelligence.
The business impact of low data quality
Low data quality imposes a significant impact on business operations and success. If data is your key strategic asset, but your data is very low in quality — incomplete, duplicated, old, or outdated — then your business operations will be low quality and ineffectual.
On the flip side, managing and maintaining high levels of data quality has across-the-board positive impacts on business. Good data breeds efficient and effective sales and marketing. Reps don’t have to struggle to find critical information. From a marketing perspective, resources can be better targeted based on accurate metrics and analytics.
Managing data quality
Successfully managing data quality involves implementing and sustaining a series of steps to achieve achieve these kinds of goals.
1. Profile and carefully analyze your data sets. You can’t make better what’s there if you don’t really understand what is in fact there.
2. With analysis in hand, begin to define and strategize data controls. Controls are not merely matters of who in the organization has access to the data, but also how the data can be created and manipulated.
3. Automate. In today’s data-heavy and fast-moving world of information technology, you will want to automate as much as you possibly can. Implement a technological infrastructure that supports data quality functions.
Elevating data quality
There are some simple but critical techniques to use in your data quality program.
Standardization —You’ll want to create and enforce guidelines for key values in your data set. Once these standards are established, you can update existing data for uniformity.
Data Cleansing — Data cleansing can verify deliverability of physical addresses and email addresses, and can confirm phone number accuracy.
Deduplication — Duplicate data ultimately means key information is dispersed and decentralized. A deduplication program must have the flexibility to analyze duplicate matches from a variety of data points and in a variety of ways. Further deduplication must be quick and easy to accomplish, and it must be able to maintain clean data on an ongoing basis as data changes.
Enhancement and Augmentation — Utilize a service that can validate that the data you have is accurate and actionable as well augment that data with missing pieces.
Additional Resources
Understanding the importance of maintaining high levels of data quality is the first step. But to really get started, you’ll want to check the current state of your data.
To get a handle on this and start building your own custom data quality strategy, get a free report on the health of your data.
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Data: It’s all about the quality
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