The Value of Data Integration
Data Ideology's simplified approach to leveraging data as a strategic asset to create that competitive advantage groups activities into three distinct technology areas of focus: Data Integration, Data Management, and Data Presentation. These areas require best-practice approaches to handle the organizational dynamics and to successfully deliver these capabilities to the enterprise.
Data Integration is an extremely valuable step in the process. Integration streamlines the data lifecycle to enable a data-driven organization. This is accomplished by moving the data closer to the point of action.
Extracting the Data
Extracting the data is the first critical step in the Data Integration process. In its most simplistic form, extraction is about determining the best source of data and making it accessible for additional processing either now or at some point later in the data lifecycle.
Combining the Data
Combining data refers to creating a unified view of the data that was extracted from multiple data sources. Your future state architecture will determine which data-combining techniques are best for your situation. Combining the data is a very complex step that requires detailed knowledge of the source systems, since data is stored in different ways in each data source. Planning is paramount, and combining data is essential for reducing data complexity, streamlining data connections, and increasing the value of your data through a unified view.
Standardizing the Data
Standardizing the data is a worthwhile step to ensure consistency of the data prior to sharing across the enterprise. This step increases the usability and quality of the data, which is crucial to gaining increased adoption and therefore maximizing the value of the data assets. Keep in mind that standardization increases the quality of the data but doesn’t ensure quality.
Cleansing the Data
The data cleansing process is a critical component of the data lifecycle that ensures the accuracy and integrity of the data. The integration of the data cleansing and data quality techniques should be a vital component of any data-related project. The various data quality techniques such as data validity, accuracy, completeness should be integrated into each step of the process. Data cleansing and quality are also ongoing processes that should be leveraged to continually monitor and assess the quality of data over time. Successfully implementing these components will increase the level of trust in the data along with increasing the data’s level of usability, adoption, and value.
In Summary, Data Integration is an extremely valuable activity that improves your data assets and the re-usability of data throughout the data lifecycle. Successful Data Integration will provide lasting business value to business processes. This includes increased analytics, decision-making capabilities, more integrated business processes, and real-time data delivery that brings the data closer to the point of action. Having a clean, consistent, complete and accurate data set is essential for success. The more data that is leveraged throughout your organization, the more business value you will realize from your data assets.
Written by Mike Sargo
Co-Founder & Chief Data and Analytics Officer at Data Ideology
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.