Imagine a modern data platform as your all-in-one system for collecting, storing, and consuming massive amounts of data in real time. It is a game-changer, allowing companies to weave data into almost every aspect of their operations. Think of it as the backbone of a business’ data strategy.
Why should businesses, big or small, consider investing in this modern data platform? These platforms empower businesses to turn raw data into meaningful insight and use it to help make strategic decisions.
With a modern data platform, companies can gather data from all over the place – customer interactions, sales numbers, social media posts, and more. Then, they dive into that sea of data, searching for patterns, trends, and connections. This effort isn’t just about playing with data; it's about finding actionable insights to steer companies toward growth and innovation.
However, the value of these platforms largely depends on the quality of the data they handle. In this article, we will explore the significance of data quality in modern data platforms and how to measure it effectively.
Data quality as part of data management
Data management is the process of collecting, organizing, storing, and using data effectively. Data management ensures that data is accessible, secure, and compliant with regulations and standards. Data quality is a key component of data management because it affects how data can be used and trusted.
Depending on the use of the data, poor data quality can cause lost revenue, increase costs due to remediation requirements and significantly impact the organization’s ability make accurate and trusted decisions. Therefore, continuously improving data quality should be a priority for any organization that wants to leverage its data assets.
Use data profiling to measure the quality
Measuring data quality might sound technical, but it's not rocket science. It's all about looking at different aspects of your data, like making sure it's accurate, complete, consistent, and up to date. You want your data to be a trustworthy foundation for business decisions.
You can set up some data quality metrics and regularly review them. These metrics allow businesses to spot and fix issues that result in poor data quality and reliability.
To get data quality right, you can use a profiling technique, which scans the characteristics of your data to help you with the interpretation. Data profiling is the detective work that helps understand, clean, and get the best out of the information you capture by collecting statistics or informative summaries about your data. It will also help you identify anomalies in your data, for example if you have a 153 year old customer, based on an incorrectly recorded date of birth.
Data cleansing involves identifying and removing duplicates and inconsistencies. For example, let's say that data profiling revealed issues that need cleaning, such as missing email addresses. You might decide to reach out to the customers for the information or consider excluding the records with missing emails if it has no impact on the data analysis.
Data monitoring and validation is the process of tracking and reporting the changes and trends in data quality over time to ensure its consistency and reliability. It can be expensive to purchase a tool or implement a bespoke measurement system. Instead focusing on the measurement of business critical data sets and limited KPIs will lower your initial costs while maximising the impact of your effort.
So, how we can improve the quality and usability of our data?
What data quality metrics should you measure?
There are a variety of definitions, but data quality is generally measured against a set of criteria called 'data quality dimensions' that assess the health of the data, such as completeness, uniqueness, timeliness, validity, accuracy, and consistency—they are the superheroes in our data world.
All these criteria would be equally important. But, depending on how you're using the data, giving some requirements higher priority over others makes sense.
DAMA International, the not-for-profit data wizards, have set out these six criteria they think are the gold standard for measuring any database. Let's break them down:
Completeness: How much of your data set is filled in instead of just sitting there blank? For example, if the customer information in a database must include both first and last names, any record in which the first name or last name field isn't populated should be marked incomplete. To keep the data complete:
- Regularly audit your data sets to identify missing values.
- Implement validation rules to ensure that all required fields are populated.
- Establish data entry guidelines and enforce them to minimize incomplete data.
Uniqueness: This one's all about how special your data is. Is there only one instance of it appearing in a database or do you have duplicates hiding in your database? For example, “Robert A. Simth” and “Rob A. Smith” may well be the same person. If it is a one-of-a-kind entry, you're golden. If not, time to:
- Conduct deduplication processes to identify and remove duplicate entries.
- Implement unique identifiers or keys to prevent the creation of duplicate records.
- Regularly review and clean up data to maintain uniqueness.
Timeliness: How fresh is your data? Current data is as important as checking expiration dates on your groceries. It might be less valuable if your data is a year old and significant changes have occurred since then. Think of it like tracking the mileage on a car – it changes a lot, so you want the latest info. To achieve this:
- Establish protocols for updating time-sensitive data regularly.
- Monitor data sources for updates and incorporate them into your database promptly.
- Implement automated alerts for outdated data to prompt timely updates.
Validity: Does the collected data match what you were trying to get? If you ask for a phone number and someone types “sjdhsjdshsj”, that's a no-go because it's not an actual phone number. Validity is all about making sure your data fits the description of what you wanted. In other words, it refers to information that doesn't conform to a specific format or doesn't follow business rules. You can validate the data by with these steps:
- Enforce data validation rules to ensure entered data aligns with predefined standards.
- Provide training to users responsible for data entry to enhance understanding.
- Regularly review and update validation rules to accommodate changes in data requirements.
Accuracy: This one's different from validity. Accuracy is all about whether the information you have is correct or not. It's like the truth detector for your data. For example, if a customer's age is 45, but the system says she's 42, that information needs to be more accurate. To ensure data accuracy:
- Implement data profiling tools to identify inaccuracies and anomalies.
- Conduct regular data cleansing to correct inaccuracies and errors.
- Establish a feedback loop with data users to identify and rectify inaccuracies promptly.
Consistency: If you're trying to compare data, you need consistency across the board. Whether on paper, in a computer file, or a database – it should all look and feel the same. For example, if your human resources information systems say an employee doesn't work there anymore, yet your payroll says they're still receiving a check, that's inconsistent. Consistency is the glue that holds your data together. Data consistency can be obtained by:
- Standardize data formats and conventions across all data sets.
- Implement data governance policies to ensure consistency in data entry.
- Regularly review and update data dictionaries to maintain consistency.
Remember, these steps should be part of an ongoing data management strategy. Continuous monitoring, regular training, and a proactive approach to data quality are key. Additionally, involving stakeholders and fostering a data-centric culture within your organization can contribute significantly to sustaining high data quality over time.
Using the power of data to stay competitive
Building a data platform has shifted from a nice-to-have luxury to a necessity for most organizations. Many companies now distinguish themselves from competitors based on their capacity to extract practical insights from their data. This process could enhance customer experiences, boost revenue, and establish a unique brand identity.
Measuring data quality can bring many advantages to your modern data platform, such as increasing the credibility of your data and results, reducing the risks and costs of errors, improving the efficiency of your data processing and analysis, and enabling better decision-making from your modern data platform.
It's time to consider investing in a modern data platform as a strategic move for your company's success and implementing measures to check your data quality. Take advantage of the opportunities that data can bring to your business.
Here at Columbus, we can guide your steps towards building a modern data platform and improving the data quality for actionable insights. Talk with our team today.