Defining Data Quality

TL;DR: Discover the essential characteristics of data quality: accuracy, completeness, reliability, relevance, and timeliness.

What are the 5 defining points of data quality?

While the term data quality can seem like a broad one, data quality can be defined by the following 5 metrics:

1. Accuracy (how close the data values are to the true values)
2. Completeness (whether all required data is present)
3. Reliability (consistency and stability of the data over time)
4. Relevance (significance or usefulness of the data for a specific purpose or context)
5. Timeliness (whether the data is up-to-date and reflects the current situation)

What is the benefit of data quality?

As our use of data grows, more companies are understanding why data quality is critical for business. That is because good data quality can be a game changer for the way they operate. It leads to better decision making, improved operational efficiency, increased customer satisfaction and an overall better customer journey. With high-quality data at their fingertips, organizations can make informed decisions that positively impact their bottom line, while saving time and money on manual data processing and error correction. Plus, leveraging accurate and complete data can give businesses a competitive edge in their industry and help them stay compliant with regulations. Its time to embrace the power of good data quality and all the amazing things it can do for your business.

To do get started on the path to high data quality, organizations need to establish data quality standards and procedures, implement data governance practices, and regularly monitor and assess data quality. This requires collaboration between different departments, stakeholders, and data quality tools & technologies. Ultimately, maintaining high data quality is a continuous process that requires ongoing attention and effort.

Is Data Quality Really That Important?

Yes! The importance of data quality cannot be stressed enough. And, as time goes by, data quality will continue to grow as an important aspect in data management. Think about it – the shift towards online commerce is not slowing down. While that has already been the trend for some time, the trend has been further accelerated since the Covid pandemic. It makes sense when you think about it. As more people were forced to buy products online, many people who were previously reluctant to make a purchase online were suddenly faced with no other choice. After a couple of purchases, most people became used to it and have changed their behavior, opting for the convenience of online shopping. Look at this graph from a study done by the United Nations Conference on Trade and Development.

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So, more online customers, more data. On top of that, a more competitive online space. To ensure survival in the online space, businesses should be thinking a step ahead of the competition in all areas. Their data quality and data quality tools are no exception. Whether it is used for making business decisions, tracking customer behavior, or analyzing market trends, data is at the core of many organizational functions.

It’s logical that with the influx of data through people shifting to online commerce, businesses are in turn making use of more data management tools inside their organization to improve their business aims. For instance, businesses are using more tools to track customer behavior's like website engagement. They use tools to improve communications through automation of email, track customer preferences, and so on. Again, these additional tools further compound the reliance on data.

To get an idea of how much more data is playing a role in the world, take a look at this graph from Statista showing the volume of data created, captured, copied, and consumed worldwide from 2010 to 2020, as well as forecasts from 2021 to 2025.  

Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025(in zettabytes)

Another facet of today's online world is that businesses are more pressured to make decisions faster. Trends can change overnight. Customers buying habits are influenced by algorithms that operate through often mysterious ways. “Get to the top of Google rankings” is the goal of every company, and they will employ any number of tools and technologies to give them the edge. Just like SEO tools give a company an edge in their Google ranking strategy, data accuracy tools give a company an edge in their data analytics and ability to act on data insights.

There is some good news in this story, and the good news is that you are likely already sitting on a gold mine of data right now. The challenge is filtering out the noise in the data, such as false or duplicate records. When you have clean data, you can use it effectively. The way to harvest the gold from this pile of ‘data rocks’ is with data quality tools. You might even consider hiring a data quality analyst, or sort of engineer for your gold mine who understands the topography of your environment. Eliminating the poor data will help you turn chaos into meaning, misconceptions into truth, and give you the confidence to adapt to trends on the fly.

The path to data quality is a road that crosses terrain on various topics. To help you navigate the theme of data quality, we created this series of articles. We have divided the articles into a range of ideas relating to achieving data quality. Through each chapter, you will learn about distinct aspects that help you understand the main topics related to data quality. It's also handy to remember, that data quality is not a destination per-se. Although you can achieve better data accuracy overnight with the right data quality tools, it's just as important to have a plan to deal with bad data in the future and take steps to prevent data inaccuracies from occurring. With that said, below you will find a handy overview for the topics we will be cover in this guide.

Guide Overview

The Importance of Data Quality

This chapter delves into why data quality is so critical for businesses. It will explore the benefits of having high-quality data, such as better customer service, reduced costs, improved decision making, better business operations, and more. By explaining how good data quality can positively impact various areas of a business, we will help you understand why it is worth investing time and resources into improving the quality of your data.

Causes of Poor Data Quality

In this chapter, we identify the main causes of poor data quality, such as data entry errors, incomplete data, duplicate data, outdated data, and lack of data standards. By understanding the root causes of data quality issues, readers will be able to identify and address these issues within their own organizations.

5 Ways to Improve Data Quality

In this chapter, readers will learn about various strategies and techniques to improve data quality. Topics covered will include data cleansing, data deduplication, data profiling and auditing, data governance, master data management, and data quality metrics & reporting. By learning these techniques, readers will be equipped with the tools they need to improve the quality of their data.

3 Types of Data Quality Tools

This chapter will delve into the most common tools and technologies available for data quality improvement. This will include an explanation of data cleansing tools, data quality management software, and data profiling tools. Each of these tools will be discussed in detail, including their common features and the benefits they can provide to businesses.

Mastering Salesforce Data Quality

Salesforce is a widely used customer relationship management (CRM) platform that enables businesses to manage their customer data. However, the data accuracy in Salesforce can be a challenge, particularly when it comes to duplicate data. This section will focus specifically on Salesforce data quality accuracy and explore the most familiar challenges associated with maintaining the best data quality metrics Salesforce.

7 Best Data Quality Practices

In the seventh chapter we will provide readers with practical advice on how to maintain data accuracy over time. Topics covered will include establishing data quality standards, conducting regular data quality checks, continuous improvement, education and training, and collaboration. By following these best practices, readers will be able to ensure that their data quality remains high and that their business operations continue to benefit from high-quality data.

Data Quality & Artificial Intelligence

In our final chapter, we look at the transformative power of artificial intelligence in the world of data management, exploring the ways in which AI will revolutionize the way we work with data.