Skip to main content

 Before an organisation can properly utilise their data assets to create value they need to ensure that they have Data Integrity and Data Integration sorted.  In this blog, we make sure that we all agree on the definitions of Data Integrity and Data Integration and look at some of the challenges to getting them right.  

What is data integration? 

Data integration is the process of combining data from multiple sources into a single, unified view. This can be done using a variety of tools and techniques, but the goal is always to create a single source of truth that can be used to make better business decisions. 

There are many reasons why organizations choose to integrate their data. For example, a company may want to integrate data from its CRM system, ERP system, and website to get a complete view of its customers. Or a financial services company may want to integrate data from its trading systems, risk management systems, and compliance systems in order to get a better understanding of its overall risk exposure. 

What is data integrity? 

Data integrity is the accuracy, consistency, and reliability of data. It is important to ensure data integrity because it helps organizations to make better decisions. When data is accurate, consistent, and reliable, organizations can trust that they are getting a true picture of their business. 

There are many different factors that can affect data integrity, including human error, technical issues, and malicious attacks. It is important to have policies and procedures in place to protect data integrity and to detect and correct any problems that occur. 

Why are data integration and data integrity important? 

Data integration and data integrity are important for a number of reasons. First, they can help organizations to make better decisions. When data is integrated and accurate, organizations can get a more complete and accurate view of their business. This can lead to better insights, better planning, and better decision-making. 

Second, data integration and data integrity can help organizations to improve their efficiency. When data is integrated, it is easier to access and use. This can save time and improve productivity. 

Third, data integration and data integrity can help organizations to reduce costs. When data is integrated, it can be stored more efficiently. This can save money on storage costs and backup costs. 

Fourth, data integration and data integrity can help organizations to comply with regulations. Many industries have regulations that require organizations to maintain certain data integrity standards. By integrating and protecting their data, organizations can ensure that they are complying with these regulations. 

How to maintain and measure data integrity 

There are a number of things that organizations can do to maintain and measure data integrity. These include: 

  • Implementing data quality standards:

    Organizations should develop and implement data quality standards that define the requirements for their data. These standards should cover things like accuracy, completeness, consistency, timeliness, and uniqueness. 

  • Monitoring data quality:

    Organizations should monitor their data quality on a regular basis to identify any problems. This can be done using a variety of tools and techniques, including data profiling, data validation, and data audits. 

  • Implementing data governance policies and procedures:

    Organizations should develop and implement data governance policies and procedures that define how data is managed and protected. These policies and procedures should cover things like data access, data security, and data retention. 

What are some common data integrity issues? 

Some common data integrity issues include: 

  • Inaccurate data:

    Data can be inaccurate for a number of reasons, including human error, technical issues, and malicious attacks. 

  • Incomplete data:

    Data can be incomplete if it is not properly collected or stored. 

  • Inconsistent data:

    Data can be inconsistent if it is entered differently in different systems or if it is not properly synchronized. 

  • Outdated data:

    Data can become outdated if it is not regularly updated. 

  • Unauthorized access to data:

    Data can be compromised if unauthorized users have access to it. 

 

How do you ensure data quality and integrity? 

There are a number of things that organizations can do to ensure data quality and integrity. These include: 

  • Implement data quality controls: Organizations should implement data quality controls at all stages of the data lifecycle, from data collection to data storage to data use. These controls can help to identify and correct data quality problems before they cause harm. 
  • Use data integration tools: Data integration tools can help organizations to ensure data quality and integrity by providing features such as data transformation, data cleansing, and data matching. 
  • Educate employees about data quality and integrity: Organizations should educate their employees about the importance of data quality and integrity. This will help to reduce human errors that can lead to data quality problems. 

 

How to integrate data from different sources 

There are a number of ways to integrate data from different sources. One common approach is to use one or more data repositories. Data is extracted from the source systems, transformed into a standard format, and loaded into the data repository. There are a variety of repository types ranging from Data Warehouses to Data Lakes. Once the data is in a data repository, it can be integrated and used for analysis and reporting. Though they can be refreshed very frequently, data repositories are always out of date, to some extent.   

Another approach to data integration is to use a data integration platform. A data integration platform is a software tool that helps organizations to integrate data from different sources. The best of these will use a Virtual Layer approach so that data can be integrated in real-time enabling the development of real-time business solutions. 

The best solutions for data integration will be future-proof and backward-compatible. 

To have future-proofing built in they will be designed to quickly encompass new data sources. This must include internal and external data. The potential to create value by integrating with external data sources is huge. This may mean reaching up and down the value chain – like live-linking to suppliers and shippers. Or public data sources like commodity prices.  

To be backward compatible the solutions need to enable access to old but still valuable resources like ERP systems. Systems like SAP and Oracle are not flexible enough to respond quickly – and cheaply – to new data analysis demands. Digital assets strategies still need to include these laggard systems using modern Virtual Layer real-time access solutions.    

Data Integrity and Data Integration are ongoing processes rather than a ‘fix once, fix forever’ challenge. New data sources will be added as businesses and marketplaces evolve, and new data integration requirements will arise – like ESG compliance. But with a solid foundation in Data Integrity and Data Integration, these changes are easier to absorb. New data sources will create new opportunities and new integration demands will be quickly and effectively delivered.