How can you implement and execute a comprehensive data integration process?

The biggest issue faced by enterprises is universal access to data stored in legacy databases, cloud and hybrid ecosystems.

Before the advent of big data, data sources were always stored on-premise. Today,in a typical enterprise, data is spread across various disparate sources- Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, to social media analytics, cloud & hybrid deployments, message queues, spreadsheets, files and reports. The sheer volume of data being generated by the minute is staggering, but since most of it is fragmented and raw, its value is significantly decreased.

Drive progress across business units

Most companies are adopting a data-driven decision management (DDDM) approach where verifiable data provides relevant insights that are translated into informed business decisions. This data-driven strategy calls for analysis of different sets of information, on-premise or in the cloud. But comparing data from heterogeneous sources is not only tedious; it hinders accurate analysis and is prone to error. Thus it is imperative that the data from all disparate sources be combined to provide a unified view of data assets. This process of consolidation, through a set of specialized tools and processes, is called data integration.

Whether financial, customer, partner, product or social media-generated, data is the most important strategic asset of a company. Data integration addresses the challenge of merging various incompatible sources and translates it into single-view, structured metadata with accurate analytics in batch or real-time.

Data integration, big data, integration

Process of data integration

Integrating data from heterogeneous and disparate sources can be a real challenge. The entire process of data integration is divided into three major phases:

Design

Since the whole point of data integration is to combine the data assets of an enterprise, the initiative must be led by someone who understands the data itself and can map its successful path and long-term goals; and in most cases, it is a non-IT person. The software requirements specification (SRS) document for the data integration project must include an analysis of the following factors:

  • The Business Requirement Specification (BRS) for the project including the objectives and end-result of the integration, i.e., why the integration is being done
  • The source systems, the availability and quality of the data, related business rules
  • Security and other policies, non-functional requirements
  • Support and SLA for the newly integrated system
  • The ownership of the new system, the budget for its maintenance and upgrade

 

Implementation

After the project plan is mapped out and a design put in place, a feasibility study has to be conducted to choose the tools and processes for the integration.

Data integration is further classified into several sub-categories – Data warehousing, Data migration, Enterprise application integration and Master data management and when implemented, it creates a data warehouse where all the data is cleaned, profiled and stored – pre-prepped for analysis. Some of the best data integration technologies in the market today are:

  • Talend – an open source, robust, unified data integration platform
  • ODI (Oracle Data Integrator) – a set of comprehensive integration tools
  • IBM Datastage – a multi-platform ETL tool
  • Informatica – a set of optimized integrator tools
  • MS BI – a scalable data warehousing solution
  • IBM Cognos – a tool to create dimensional data warehouses with ETL and MDM

 

Testing

Any implementation is incomplete without a rigorous testing phase to ensure that the final data assets are complete, correct and up-to-date. Some of the most commonly performed tests for a data integration project include:

  • Performance Stress test (PST)
  • Technical Acceptance Testing (TAT)
  • User Acceptance Testing (UAT )

 

Data Integration – The CyByte Way

Now that Big Data is here to stay, data integration is the only solution to fragmented data. Enterprises driven by a big data strategy must adopt end-to-end data integration solutions to optimize their business and ensure a seamless flow of business-critical analytics in order to stay relevant in a competitive market.

Are you looking to transform your business? Reach out to us and have a look at our comprehensive, customized data integration solutions.