What Is Data Quality and How to Achieve It
Data quality is a crucial part of any modern business. In this guide, we’ll explore data quality in more depth and explain how you can achieve it.
The quality of the data your organization relies on plays a pivotal role in determining how successful you are at reaching your goals. High data quality means more accurate insights, increased efficiency, improved confidence, and better decision-making.
However, managing and maintaining data quality can be challenging if you don’t have processes in place.
What is data quality?
Data quality is the measure of how well a data set serves its intended purpose. We know that data is the foundation of decision-making in all areas of life, and we need good data quality.
But what defines data quality? There are six pillars that need to be looked at in any data management strategy to ensure a solid foundation.
Pillar 1: Accuracy — the cornerstone of data quality. It refers to the degree to which the data is correct, reliable, and free from errors. An example of inaccurate data would be having a record about an individual that states they are 30 years old, when in reality they are 35 years old.
Pillar 2: Completeness — the extent to which all necessary data elements are present and available. An example of incomplete data would be having a record that doesn’t include an individual home address when it’s needed for marketing purposes.
Pillar 3: Uniqueness — duplicate or redundant data is eliminated from the system. Duplicate data can create confusion, inflate costs (i.e. storage, marketing), reduce customer satisfaction, and allow for fraudulent activities. An example of duplicate records causing a problem would be having multiple records that represent the same individual, i.e. one record has details about a customer called John H and another has details about a customer called Jonathan H, when they are the same person.
Pillar 4: Consistency — ensuring data is uniform and coherent across different sources, systems, and departments. Inconsistent data can result in duplicated or conflicting information, leading to confusion and poor decision making. An example of inconsistency is having one record with DOB formatted as DD/MM/YYYY and another as MM/DD/YYYY. Consistency means all records should adhere to one format.
Pillar 5: Timeliness — the availability of data when it’s needed for decision making. Outdated data can lead to suboptimal decisions and missed opportunities. For instance, if we possess data that is one month old and attempt to create a comprehensive 360-degree customer profile for marketing purposes, the data may no longer be valid or useful. Essential details such as the customer’s address or phone number may have changed, rendering the outdated information ineffective for marketing initiatives.
Pillar 6: Validity — the adherence of data to predefined rules, formats, and standards. Valid data is accurate, reliable, and fit for its intended purpose. An example of invalid data is recording a DOB as 20/20/1990. Since there is no 20th month, this entry is incorrect (with the real date likely being February 2nd or February 20th). Without proper validation in place, errors like this can occur and potentially compromise the accuracy of the customer data.
These six aspects are important when thinking about data quality solutions.
What impact does data quality have on your organization?
We’ve already mentioned that data quality can affect your organization. But how much? In Experian’s most recent Global Data Management Research Report:
of organizations said contact data quality had become more important in the last 12 months
said being data driven helps them stay up to date with what their customers need and market trends
said that poor quality contact data for customers negatively impacts their processes and efficiency
Why is data important?
More broadly, data has increasingly become part of how organizations operate, being used to predict future trends, understand potential challenges, make business decisions, and create strategies across areas such as communications, customer relations, product development, efficiency, and marketing.
Why is data quality important?
The significance of data quality has grown as organizations increasingly rely on data to inform their decision-making processes. Ensuring data quality is crucial as it guarantees the reliability, accuracy, and completeness of the information used for critical business decisions. By measuring data quality, you can identify any errors, assess whether a data set is of a high enough standard to be used, and learn how to better use and manage your data.
What are the challenges organizations face with measuring data quality
Measuring data quality levels can help organizations identify data errors that need to be resolved and assess whether the data in their IT systems is fit to serve its intended purpose.
Technological developments over the last several years have increased the number of ways we can collect, store, and analyze data. But this also means data management has become more complicated, heightening the risk of low-quality data. Low-quality data can lead to decisions that have a significant negative impact. You could:
Spend time processing, reprocessing, and analyzing data that’s not fit for purpose
Make misguided business operations decisions
Miss regulatory compliance obligations
Miss out on opportunities for customer acquisition and growth
Damage your business’s reputation
Cause an ethical issue (for example, a public safety issue if your organization is in the health industry)
Experience financial losses (poor data quality costs organizations an average of $12.9 million each year)
In the next section, we’ll explain how you can achieve data quality and avoid these pitfalls.
How to achieve good data quality
The data quality pipeline
Achieving data quality allows you to get the most out of any data you collect. It’s not a one-off project; data quality should be monitored and maintained so you’re in a position to trust the information you have and use it most effectively.
It’s vital to address different aspects of data quality and help create a comprehensive and reliable data set for analysis and decision-making. There are three main phases involved: data curation, data matching, and data improvement.
A big part of the quality pipeline is data curation, which consists of seven parts.
Data assessment. The data quality pipeline begins with assessing the quality and completeness of a data set. This phase identifies potential data quality issues that need to be addressed, ensuring the data can be used effectively.
Data cleansing. This phase involves identifying and correcting errors, inconsistencies, and inaccuracies in the data set.
Data parsing. This phase involves breaking down larger data sets into smaller, more manageable pieces, extracting specific pieces of information, or separating data relevant to a particular analysis, such as identifying a US postcode in a longer line of address and placing it in the right position.
Data integration. Data from multiple sources is combined into a single, unified data set, creating a more complete and accurate view.
Data enrichment. This phase enhances a data set by adding information or context. Data enrichment supplements existing data with additional attributes or incorporates external data sources for added insights.
Data standardization. This phase involves applying consistent formats, structures, and codes to a data set, ensuring the data is consistent and can be easily integrated and shared across different systems.
Data validation. Data is checked to ensure it meets certain quality and consistency standards, identifying and correcting errors and inconsistencies in the data set.
Data is matched to form entities – or in other words, data that is likely to correspond to the same real-world things is connected. It is important to note that data matching is a delicate and critical phase, often requiring its own dedicated project.
Finally, data improvement involves taking steps to enhance the quality, completeness, and accuracy of a data set. This phase may involve any of the previously mentioned activities, as well as additional steps such as data profiling, data modeling, and data governance.
Take stock of the data you already have
You need to understand your current data before you can start to improve data quality. To assess it, you need to know what data you collect, how it’s stored, who in your organization can access it, and how it’s formatted. Having this information in hand allows you to see what’s working and what could be improved.
Decide what data you actually need
What information would be most valuable for your organization? What do you need to know? Asking and answering these questions will help you decide what data is relevant to your current and forecasted needs. It also prevents you from wasting time and resources collecting information that won’t serve you.
Define what acceptable data quality is
Everyone in your organization should be on the same page when it comes to understanding what counts as acceptable data quality — and what doesn’t. The data characteristics of accuracy, completeness, consistency, timeliness, uniqueness, and validity are a good place to start.
Monitor data entry points
Issues with data quality can start as soon as the data is collected, with the potential for human error. For example, if data is input into CRMs by employees, this raises the risk of inaccuracies or missing information.
Address any errors as soon as they come in by using a system that flags incomplete, inaccurate, or duplicate data so it can be corrected or removed before it goes any further.
Carry out data profiling
Data profiling is the first assessment of the data that has been collected, allowing you to check and analyze the data, discover any issues, and create summaries of the information that’s been found. Carrying out data profiling allows you to understand and clarify:
Whether all the data is legitimate
Whether there are any errors, such as missing values or values that shouldn’t be present
The information the data contains
How the data is related
How the data is structured based on these relationships
Values such as the mean, minimum, maximum, and median figures
How the data relates to your organization’s goals
By making these assessments you’ll be able to get the most value out of the data you’ve collected and prevent costly mistakes being made further down the line.
Identify any bias
Bias can be difficult to avoid even if you have the best of intentions. Biased data leads to biased actions, which ultimately harm customers and companies alike.
There are three main categories of bias found in data collection:
Response bias – the respondents provide inaccurate information.
Selection bias – the data doesn’t represent the population as a whole, missing crucial insights and showing an inaccurate picture.
Systematic bias – the data collection model has a consistent error that leads to biased data being gathered.
Unfortunately, it’s not as easy as simply removing bias from any data collected. However, you can take steps to mitigate it by:
Removing opportunities for respondents to make errors when collecting their data (for example, by providing pre-determined answers for them to choose from)
Using a diverse, representative range of data sources
Fixing systematic errors
Make the data accessible to anyone who needs to see it — and protected from those who don’t
All employees who need to see and update data should be able to access it easily. This will save time and prevent crucial insights from being missed. Don’t make your data easy for unauthorized parties to obtain, however. It should be secure from outsiders, especially if you’re storing private customer information. Use an encrypted database and back it up regularly to prevent losses.
Have a data standardization policy in place
For data to be useful, it needs to have a consistent and accurate format. Standardizing data is the process of transforming varied data into a consistent format that can be analyzed more effectively. For example, you might standardize customer phone numbers so they’re all written in the same way. This is crucial for ensuring data quality, especially when dealing with large datasets.
This makes it easier for anyone who views or uses the data to understand it, analyze it, and identify any errors, and also allows for smoother communication during discussion.
There are two types of standardization:
Internal, which are specifically created for and used within your organization. Having a data standardization policy in place ensures that everyone is on the same page. Provide a written explanation of what your policy is and why — this will ensure standards remain consistent over time.
External, which are used outside your organization and are known internationally. External standardization is best for commonly used data types, so they can be widely understood.
Conduct regular reviews
Technology is continually developing and your organization will evolve, too. So it makes sense to review your data quality processes on a regular basis. This will help you see what’s working and what could be improved, ensuring you continue to get as much value as possible from the data you collect.
What are the barriers to achieving good data quality?
It’s not always easy to reach a level of data quality that builds a trusted data foundation and enables confident decision-making. There are several challenges businesses face when attempting to maintain clean and accurate data, including a lack of trust from stakeholders, a lack of accuracy, and a lack of standard formatting. In this section, we'll explore the barriers to achieving good data quality and discuss how to address them.
Lack of standard formatting
We’ve covered the benefits of having a standardization process for data quality, but what about the reasons why it’s a mistake not to have one? A lack of standardization (set formatting) can lead to the inclusion of data that’s duplicated or simply wrong, resulting in poorly informed business decisions being made.
You can use data transformation tools to standardize your data, which can automate the process of converting data into a standard format.
Alternatively, if you’d prefer to do it yourself you can create a ‘data dictionary’, which defines the names, formats, and possible values for all the data elements in a dataset, and sets rules for how they should be presented.
By standardizing your data, you can improve the quality and accuracy of your analysis, making it easier to draw meaningful insights.
Inaccurate data can have serious consequences for any business. Poor data quality can lead to incorrect decisions being made, which can result in lost revenue, missed opportunities, and damaged reputations. It is, therefore, essential to ensure that data is accurate and of high quality.
Implement reliable data collection processes, introduce a system for identifying and fixing inaccuracies in data, conduct regular quality checks, and invest in data cleansing tools and technologies. By doing so, you can ensure you have reliable information to inform your decision-making processes and drive success.
Fix your data quality today
In today's data-driven world, accuracy is key, and ensuring data quality is critical to staying competitive. You can unify your data and transform decision-making with Quantexa’s Decision Intelligence Platform. With the ability to connect all internal and external data at scale with unprecedented accuracy, it has multiple use cases across industries.
We’ve discussed a lot in this guide, but there might still be more you want to discover about data quality and how to achieve it. Browse these handy secondary sources to learn more.