The impact of poor data quality can have a ripple effect across your entire organisation, affecting the credibility of your business information and resulting in flawed decision-making. To ensure you uncover actionable insights from business intelligence that helps your organisation remain profitable and viable, you need reliable and effective data quality management.
What is data quality?
Data is considered to be of high quality when it is fit for its intended purpose, correctly represents the truth and can be applied to achieve meaningful business outcomes. That’s where data quality management comes in with a practical approach to organising and working with your data that aims to prevent data quality issues from arising. Data quality management also includes remedying business data that does not stack up against your organisation’s business rules, data definitions and KPI metrics established to meet business goals.
Now let’s explore how you can ensure effective data quality management in your organisation to power up the ongoing success of your data ecosystem.
Data governance provides a structured approach to data-driven decision-making using standardised processes, critical business requirements and specific controls. An appropriate data governance framework can form the foundation for data quality management across your entire organisation if it includes these four essential characteristics:
1. Risk and compliance mitigation
Having compliant data means that your assets adhere to data protection regulations, as well as legal obligations. Your data governance framework must enforce security measures to prohibit sensitive information from being accessed by unauthorised personnel and to avoid non-compliance penalties in the process. An appropriate framework also helps identify data-related risks and the necessary controls to mitigate them.
2. Security and accessibility
Effective data governance enables organisations to comprehensively establish policies on how data is gathered, stored, accessed, shared, controlled and deleted. This also extends to the security of every data-related process in the business, with a logical framework that gives users fast, functional accessibility to the data they need.
3. Data Integrity
As data is a vital and valuable asset to your organisation, you need to ensure it maintains its integrity by upholding its accuracy and consistency. Data governance can help corroborate your data so that it reflects the absolute truth and provides you with business intelligence of the highest quality.
4. Policies and standards
Your data landscape must be controlled by standards and policies within your data governance framework. This helps establish effective data quality management by keeping it organised and secure. It also defines clear requirements for how data in your organisation should be created, used and managed judiciously and ethically.
As a key component of data discovery and preparation, data profiling involves examining, reviewing and summarising the data that your organisation holds. Often with a robust and dedicated technology backbone, this method is used to understand the data assets requiring active data quality management and is populated by business users in accordance with business rules to achieve key metrics and objectives. This means that the frequency and distribution of data values is measured against structural levels and data entities across different databases.
Data quality reporting
Data quality reporting involves the continuous monitoring of information quality in an organisation, using the findings from data profiling to inform data quality metrics and flag issues along the way. In an ideal data quality management world, reporting is an iterative and ongoing process that validates your data quality remediation efforts. Data quality dashboards are useful tools for this, displaying important insights clearly and succinctly, demonstrating your performance across the data quality metrics and highlighting trending data issues within your organisation.
Master data management
Master data encompasses information such as customer roles, supplier roles, employee roles, product data, location data and so forth. This can be difficult to manage and maintain given the qualitative nature of this data and the likelihood that the information is replicated in multiple systems across your business. In order to achieve effective data quality management without having to regularly conduct data cleansing, your organisation should adopt a master data management (MDM) framework. This framework, which is typically technology-enabled, helps data owners and stewards to ensure data uniformity across the business and avoid instances of corrupted, miscellaneous data sets and attributes.
Managing your critical business data is fundamental for organisational growth. A powerful, well-built framework for data quality management can be your turnkey to making insight-driven business decisions that lead to successful outcomes.
Want to learn more about how to level up your organisation’s decision-making capabilities by tapping into the power of high-quality data? Download our free eBook to find out how your organisation can improve data quality and data governance to gain more value from your business intelligence.
Optivia’s data quality services empower reliable, informed decision-making. We help you strengthen your business with robust data governance principles designed to improve data accuracy and ensure you adhere to data privacy regulations. If you need a data quality framework that aligns with your strategic goals and helps you make rapid, meaningful decisions, contact us today.