Data integration is the process of combining data from different sources into a unified and consistent view. It is essential for data governance, which is the practice of ensuring the quality, availability, security, and usability of data across an organization. But how do you know if your data integration is good? Here are some key indicators to look for.
One of the main goals of data integration is to improve the quality of data by eliminating errors, inconsistencies, and duplicates. Data quality can be measured by various metrics, such as accuracy, completeness, timeliness, validity, and relevance. You can use data quality tools to assess and monitor these metrics and identify and resolve any issues. A good data integration should result in high-quality data that meets the expectations and requirements of the data consumers.
Another indicator of good data integration is data availability, which means that data is accessible and ready to use whenever and wherever it is needed. Data availability depends on several factors, such as the performance, reliability, scalability, and security of the data integration platform and infrastructure. You can use data availability tools to test and optimize these factors and ensure that your data integration can handle the volume, variety, and velocity of data. A good data integration should provide fast and reliable data delivery to the data consumers.
Data security is a critical aspect of data governance and data integration, as it protects data from unauthorized access, use, modification, or loss. Data security involves applying various measures, such as encryption, authentication, authorization, auditing, and backup, to safeguard data at rest and in transit. You can use data security tools to implement and enforce these measures and comply with the relevant laws and regulations. A good data integration should ensure that data is secure and compliant throughout its lifecycle.
Data usability refers to the ease and effectiveness of using data for various purposes, such as analysis, reporting, decision making, or innovation. Data usability depends on several factors, such as the clarity, consistency, and compatibility of data definitions, formats, and standards. You can use data usability tools to document and manage these factors and facilitate data discovery, exploration, and integration. A good data integration should enable data usability by providing clear and consistent data that can be easily combined and consumed.
The ultimate indicator of good data integration is data value, which means that data delivers tangible and measurable benefits to the organization and its stakeholders. Data value can be assessed by various metrics, such as the return on investment, the impact on performance, the satisfaction of customers, or the innovation of products or services. You can use data value tools to track and evaluate these metrics and align them with the organizational goals and strategies. A good data integration should create data value by supporting and enhancing the business processes and outcomes.
This article needs to include use of ontologies in data integration to tackle the problem of semantic heterogeneity in the data sources. The effectiveness of this type of data integration depends on the ontology in the backend.
Your data integration is good when it produces the right results. You know the results are right when you've tested properly. You need test cases with expected results and you can prove it passes when the actual results match. Too often, people think an integration works if they start with 10,000 rows and have 10,000 rows after the integration, but are those 10,000 right? Too often, people take a snapshot of production data and run that through the integration, forgetting that the snapshot might not contain all the scenarios or there might be transformations the data goes through. If the integration is being done because there were data issues, the fact that you get different results is a good thing if you fixed the problem.
Data integration is irrelevant if you cannot use the end data produced. It depends on the goals to be achieved, for back-up, reporting or analysis. Based on the need effective metrics and KPI's can be set. Many times organisations fail to understand the expectations of Data integration and implement it to achieve a single source of data platform but without proper definition of ETL and ELT rules, it leads to garbage in and garbage out. We will create a redundant source of data but without a proper single source of truth. Time should be spent on defining data modelling, enriching, cleaning and transformation policies to be implemented instead.