Data is an integral part of modern organizations. It enables businesses to increase operational and financial efficiency by facilitating cost savings and reducing wasted resources.

These benefits are realized by a large proportion of the business world, but some still see data as an administrative task – something merely to be obtained.

However, for those that have this warped view of data, others are focusing on their data strategies and perhaps more importantly, focusing on their data quality. For the latter, they’re experiencing the increasingly required competitive edge in their respective field, for the former, they are being left behind.

Some businesses have started rethinking their approach to data in light of this, and put strategies in place to address the gaps, but it’s not always enough. Not all data is equal – it’s not simply data or no data, it’s bad or poor data versus quality data.

Bad data can severely impact business operations. If incorrect data falls through the cracks and is used as the basis to inform business decisions or measure the effectiveness of a given strategy, the consequences could be dire. Data that is either incorrect, incomplete, unreliable, irrelevant, or untimely is considered poor quality. If decisions continue to be made based on poor-quality data, not only will all data inevitably become untrustworthy, but businesses risk making unfounded business decisions. In turn, this often results in financial losses, and reputational damage and ultimately puts the business at real risk of failing.

 

So, how can businesses ensure that bad data doesn’t become a problem in the first place? By putting good data quality measures in place…

First, let’s take a look at what data quality is, what it looks like, and what it means to have it.

There’s one simple question you can ask if you want to know if you have quality data – does it fit its intended purpose? If the answer’s no, something has gone wrong somewhere down the line, but if the answer is yes, you’re starting on the right track.

So, you now know you have some quality data, now you need to measure it against 10 key pillars:

1. Accuracy: is the data accurate, free from errors, and does it reflect the true state of the information?
2. Completeness: is the data complete, contains all the relevant information, and is without any missing values?
3. Consistency: is the data consistent, with the same information represented in the same way across all sources?
4. Timeliness: is the data up-to-date and reflective of the current information?
5. Validity: is the data valid, with values that fall within the acceptable range for a given field?
6. Uniqueness: is the data unique, with no duplicate records present between internal systems?

But how can organizations use the data, and make it accessible and relevant to the business’s overarching objectives? Rules need to be put in place to ensure integrity and security to prevent unauthorised changes and use. This is where the following points come into play…

• Accessibility: is the data accessible, with proper systems and processes in place to allow authorized users to retrieve it, and prevent access to those without?
• Relevance: is the data relevant to the task at hand and meet the needs of the intended audience?
• Integrity: does the data maintain its integrity, with rules in place to prevent unauthorized changes, downloads, or corruption?
• Security: is the data secure, with proper measures in place to protect against unauthorized access, theft, or misuse?

All of these fields need to be carefully considered, and organizations need to put standards, practices, and principles in place to ensure each characteristic is applied to each piece of data (we’ll talk more about this later).

But first, let’s look at why it is so important to have quality data.

As outlined in the introduction, there are countless benefits associated with having good-quality data – we’ve highlighted some of the most common advantages below:

• Improved decision-making: high-quality data helps businesses make informed decisions based on accurate and relevant information
• Increased efficiency: data quality helps to streamline business processes by reducing the time and resources required to correct errors and inconsistencies and update data
• Better customer relationships: accurate and relevant data can improve customer engagement and satisfaction, leading to an enhanced customer experience and increased customer loyalty
• Improved data analytics: high-quality data is critical for effective data analytics and insights as it enables businesses to better understand their customers, operations, and market trends
• Improved compliance: having good-quality data is essential for meeting regulatory requirements, such as those related to privacy and data protection
• Reduced costs: bad data can result in wasted resources, increased costs, and decreased productivity, but by improving the quality of data, businesses can reduce these costs, and protect and improve their bottom line
• Greater collaboration: high-quality data enables better collaboration and communication within an organization, as well as with partners and suppliers
• Enhanced reputation: organizations that prioritize data quality and privacy are more likely to be seen as trustworthy and responsible, improving their reputation and building customer confidence

Now we know what quality data looks like and the benefits it offers, it’s time to assess things.

Assessing data quality is an important step toward improving it. The following tasks can guide organizations in assessing their data quality:

1. Define quality criteria: taking into account the specific requirements of your business and the data in question, decide what makes data, quality data for you. Think about the previous section, and consider accuracy, completeness, consistency, timeliness, relevance, uniqueness, accessibility, validity, integrity, and security.
2. Gather data samples: gather a representative sample of your data to be used for evaluation, which should be large enough to provide a typical picture of the data as a whole.
3. Perform a data audit: then audit the sample data to identify any issues or problems with data quality, this can either be done manually or with specialized technology.
4. Evaluate data quality: based on the criteria established in step one, evaluate the data quality. Run a thorough assessment of data accuracy, completeness, consistency, timeliness, relevance, uniqueness, accessibility, validity, integrity, and security.
5. Identify issues: highlight any data quality issues present such as errors, inconsistencies, missing data, and data that does not meet the established criteria.
6. Document findings: record what you find, including the specific issues and problems identified in step 5, as well as any recommendations for improving data quality.
7. Develop a plan: create a plan of action for addressing any issues that have been identified and improving data quality, taking into account both the findings from the data audit and the specific needs of your business.
8. Implement changes: adopt the modifications and improvements identified in the plan, making any necessary updates to data entry processes, data management systems, and data quality management policies.
9. Monitor and maintain data quality: boosting data quality doesn’t stop at boosting data quality. It’s important to regularly monitor data quality and maintain ongoing efforts to continuously improve it. This includes ongoing data auditing, updating data quality processes and policies, and providing training and resources for data management personnel.

By following these steps, you can gain a comprehensive understanding of the current state of your data quality and take the necessary steps to improve it.

Going back to steps 3 and 4, organizations may need technical support to perform data quality audits and evaluate data quality. There are various tools available on the market, such as:

• Data profiling tools: analyze the structure, content, and relationships within data to identify patterns and anomalies, such as data duplicates, missing values, or inconsistent data formats
• Data cleaning tools: automate the process of cleaning data, i.e. removing inaccuracies, inconsistencies, and duplicates, and ensuring data is formatted correctly
• Data validation tools: validate data against predefined rules and standards, ensuring data is accurate and complete and meets specific quality criteria
• Data governance tools: better manage data quality by establishing policies, procedures, and standards for data management, as well as monitoring and enforcing data quality rules – find out more about data governance, here
• Data monitoring tools: continuously monitor data quality and alert users and data owners to potential issues, allowing organizations to quickly identify and address data quality problems
• Data visualization tools: make it easier to identify patterns and outliers, and understand the relationships between different data elements

These types of tools help businesses to streamline the process of evaluating data quality and conducting audits, which in turn, helps improve the accuracy and completeness of their data, and enables them to make informed decisions based on high-quality data.

But technology is only one piece of the data puzzle…

Alongside data quality tools and solutions, organizations can also use certain data techniques to improve data quality. 

• Data Cleaning and standardization: this involves removing duplicate data, formatting and standardizing data, and using data validation techniques such as; data type validation, range and constraint validation, code and cross-reference validation, structured validation, and consistency validation
• Data collection and entry: this means implementing data entry protocols for all users to adhere to and training data entry personnel on those measures
• Data management: which encompasses establishing solid data governance policies, conducting regular data maintenance and updates, and implementing data backup and recovery procedures 

Once you’ve implemented the tool and techniques to improve data quality, it’s time to look at some best practices to follow to really make your data work for you and provide real business value.

Data Quality Metrics

Define your key data quality metrics. Which departments need what data, and what data isn’t being collected that should be? What data is most useful for the business? Think about things like accuracy and consistency, etc. Don’t forget to monitor these metrics and adjust them in line with changing business demands. 

Data Quality Management

Who is going to manage data quality? Assign data quality roles and responsibilities, and ensure everyone knows what data they own and are responsible for ensuring the quality of. Implementing data quality management processes will help to ensure that everyone has a job and involvement in data quality.

Establish a Data Quality Culture

Ultimately, none of these steps can work unless an organization has a data quality culture. Techniques and tools are only the first steps, data quality must be built on the foundation of a data-driven culture. Encouraging data quality ownership and accountability will help, but organizations need buy-in from all the relevant stakeholders to encourage maximum success.

Quality data is key to driving improved business operations.

Not all data is the same, so instead of prioritizing the collection of data that may or may not be valuable, businesses need to prioritize the quality of data. 

If your business is looking to invest in a data quality solution to do a bit of the heavy lifting, then Exmon has a whole suite of solutions that can help. From data governance and continuous monitoring tools to data visualization functionalities, Exmon can provide everything you need to ensure data quality and regain trust in your data to make well-informed business decisions for the better. 

 

Want to learn more? Reach out to an expert for a demonstration of Exmon’s data quality solution.