How to improve your data quality

Your data is the lifeblood of your organisation. Think about it this way — you do whatever you can to keep yourself healthy, and you should do the same with your data quality.

Poor data quality can negatively impact business decisions and operations. Inaccurate or incomplete data can lead to flawed analysis, misguided strategies, and missed opportunities. In fact, 95% of businesses see poor-quality data as a problem.

Imagine making important business decisions based on unreliable information - it's a recipe for disaster. That's why maintaining high data quality is crucial for ensuring the success and efficiency of your organisation.

Taking steps to improve and maintain data quality is essential for the well-being of your business, and in this blog, we make it easy for you by highlighting how to improve data quality.

Move away from legacy systems
Conduct a data quality assessment
Cleanse your data
Bring your data into one unified place

Move away from legacy systems

For those of you using legacy systems, and especially those of you using Excel — we know your data management system has been with you through a lot, but it's time to say goodbye.

More sophisticated technologies now outpace legacy systems, and using one could cause more harm than good.

Limitations of legacy systems

Compatibility issues: Legacy systems often struggle to integrate with modern applications and technologies, leading to inefficiencies and data silos
Maintenance costs: Keeping legacy systems operational can be expensive due to the need for specialised knowledge and outdated hardware
Security risks: Older systems are more vulnerable to cyber threats as they may not receive regular security updates
Lack of support: Vendors may no longer support legacy systems, making it difficult to find help or resources when issues arise
Performance bottlenecks: Legacy systems can be slow and unable to handle the volume of data processed by modern businesses, leading to performance issues
Limited scalability: As your business grows, legacy systems may not scale effectively to accommodate increased demands
Compliance challenges: Legacy systems might not meet current regulatory standards, posing non-compliance risks

The new dawn of data management systems

Modern systems focus on seamless integration, allowing them to connect effortlessly with a wide range of technologies and platforms. By enhancing the flow of data and promoting interoperability, these systems facilitate a smooth exchange of information across various systems and applications.

Regular updates and advanced security measures in modern systems serve as a shield against cyber threats and safeguard data from breaches, keeping in line with compliance regulations and saving you money.

These systems demand less specialised expertise and boast easier maintenance — especially when they come with comprehensive demos and free plans — reducing overall costs for training, maintenance, and correcting mistakes.

Designed for scalability, modern systems effortlessly adapt to business expansion and the surging demands for data. Operational efficiency is also enhanced with accelerated processing speeds and the capacity to manage vast data volumes.

With sleek interfaces and intuitive features, modern systems enhance user interactions, streamlining employee data handling while minimising risks and improving productivity.

These systems can also automate data quality assessments, cleansing processes, and integration tasks so data remains accurate, consistent, and readily accessible. Automation not only saves time and reduces the risk of human error but also enhances operational efficiency and decision-making capabilities.

Now that you know the benefits of modern data management, here's what you need to do next.

Conduct a data quality assessment

A data quality assessment evaluates the quality of your data. The purpose is to identify and rectify issues related to data accuracy, completeness, consistency, and other key dimensions, ensuring the data is reliable and fit for its intended use.

How to conduct a data quality assessment

Define the scope and objectives

Identify stakeholders: Determine the key stakeholders and their roles
Set goals: Clearly define the objectives of the data quality assessment
Determine scope: Specify the datasets, systems, or processes to be included

Gather and profile data

Collect data: Gather relevant data from the specified sources
Profile data: Use profiling tools to understand data structure, content, and quality

Establish data quality criteria

Define metrics: Set specific metrics for accuracy, completeness, consistency, etc
Set thresholds: Establish acceptable levels or thresholds for each metric

Conduct the assessment

Data analysis: Analyse the data against the defined quality criteria
Identify issues: Document data quality issues, noting their nature and severity

Report findings and develop a plan

Summarise results: Compile a report with the findings
Visualise data: Use visual tools to present data quality metrics and issues
Action plan: Create a remediation plan, prioritising issues and assigning responsibilities

Implement and monitor actions

Execute plan: Implement the remediation actions
Monitor progress: Continuously monitor progress and adjust the plan as needed
Establish ongoing management: Set up processes and tools for ongoing data quality maintenance and continuous improvement

Top tip: These profiling tools can help improve data quality!

Cleanse your data

Within your data quality assessment, you may have highlighted inaccuracies, inconsistencies, and errors that need rectifying to improve data quality.

Your data can include removing duplicate records and correcting simple errors, handling missing data, and normalising and standardising formats. Whatever you've identified as a risk to data quality, there's a way to fix it.

To implement data cleansing effectively, you should define clear data quality standards and automate repetitive tasks using data cleansing tools.

Incorporating cleansing into regular data management workflows and scheduling periodic activities helps maintain data quality over time, as does fostering a data quality culture.

Bring your data into one unified place

Having your data scattered across different platforms can lead to confusion, inefficiencies, and missed opportunities. Unifying all your data can create a clear and comprehensive view of your information landscape.

Integrating data from various sources allows you to break down data silos and ensure all stakeholders have access to the same accurate information. This unified approach enhances the reliability of your data and simplifies decision-making processes by providing a single source of truth.

With all your data in one place, you can easily identify patterns, trends, and insights that would have been difficult to uncover when data is fragmented. This streamlined approach not only makes data easily accessible but also improves overall operational efficiency by reducing duplication of efforts and promoting collaboration across departments.

In essence, bringing your data into one unified place is like organising your workspace — it leads to clarity, efficiency, and productivity.

How to bring your data into one unified place

It's time to say goodbye to scattered, poor-quality data and hello to a more streamlined and effective way of managing your organisation's most valuable asset. Sign up to Starhive today to bring any and all of your data into one unified platform. Improve data quality and manage your data your way.