Customer data integration is critical, and big data systems need to be integrated in a very unique capacity. There are three steps as part of the overall systematic process that will allow integration to occur in a clean manner, producing the best customer data as well as establishing an ongoing system for integration processes.
The data cleansing stream stage is done to produce the highest quality data for business, and involves extracting data from source systems. The quality of the data is essential, which is why the data is extracted first. In the case of integrating data from external systems, such as other data providers, web services can be used which allow the first data sets to be checked. The exported data is temporarily stored inside of an initial staging area, and rules for monitoring this extracted data are established.
Extracted data still has to be measured against the needs of the business, so a data quality rule set is established for all internal data sources based on the stakeholders input for the business units affected. Measures are defined for optimizing data quality of the internal data sources and their implementation are fully defined as well--this means a check is conducted to determine data quality and how it reflects on the demands of the business. The data is then prepared so that only unique data sets are linked to each other when integrated later on.
This whole process is done as a way of generating true value for your company. Determining data quality rules for business areas and monitoring the data quality will ensure that demands are satisfied now and later on.
Ultimately, the object of this step is to find the leading system with the most qualified data, to ensure that the correct fields are from all the systems are consolidated for integration, determine which system the data is to be extracted and finally to connect and test that system. All of these different tests that are conducted will ensure that there are no bottlenecks resulting at any point during the integration process.
At the beginning of the integration stream stage, it must be established exactly what information is to be produced by the target system that is receiving the data from the external systems, so the design of the system must also be part of this. Establishing both of these assures that all of those involved know the data that is required, the format, and at what time in the process.
Customer data integration is based upon harmonization of all source systems, always with the end goal being to create a single customer view. With that in mind, the integration step means not only logical, but physical integration models and field mapping will be decided so that exact instructions are created. If structure and value mapping is not done, the data may not flow properly.
This single customer view, or head record, is created according to the needs of the data end-user. The data users' needs are established by the business areas, such as the data warehouse and the business intelligence systems. Data sets are also monitored carefully during this transformation stage.
While the data cleansing stage took the approach of harmonizing the external source systems by eliminating the duplicates, the integration stage focuses on the matching datasets to consolidate for integration.
The technical implementation of data integration is taken care of during this stage. The Uniserv Data Integration Suite is the best tool to use to conduct the integration, and data is read from source systems and loaded into the staging area. Various transformation components are utilized to adapt from the quality demands. After that, the data is transferred to the target system, making it available for target applications within the different business areas.
After various checks and balances, the process goes live. Monitoring is continuous and test users will check the data so that there are no surprises at a later point in time. Once the implementation of the integration goes through the acceptance test, the data integration process can then be utilized by everyone.
The Uniserv Data Integration Suite is the tool that allows all of the stages of customer data integration to be run and managed smoothly so that all the demands of the business are satisfied, and the data transformation is a successful. With over 300 connectors for a wide range of data storage systems, Uniserv allows for optimizing contact master data, avoiding duplicate datasets, and even using previously established transformation rules--all of these mean that high transparency is provided.
The graphic interface also allows nonprogrammers to understand the process. Those within specialist departments have the ability to contribute actively without understanding all of the programming behind the process. Rules are optimized together, including those who are test users. And ultimately, by using Uniserv the rules can be optimized following the transformation assuring that the integrated data satisifies the needs of the target applications and therefore the business.
Big data is complex, though through effective data integration, it can be managed effectively within any business. Understanding the processes and following the key steps will help to eliminate problems throughout integration processes in the future.
Learn more about Uniserv and applying it's uses with our free paper Achieving High CRM Data Quality with Uniserv.