Data Integration

Bringing islands of data together to support business objectives

Data Integration is the set of processes and technologies required to automate the cleansing, reconciliation, and integration of corporate data from multiple systems, and then propagate that data to systems and applications to support both operational and analytical needs.

Unlike a data warehouse platform that serves as a repository of historical, detailed, business-structured data, a data integration hub is an operational system. In the past, data integration and cleansing was done through ETL or homegrown programming. What’s new is that now we can automate the matching, merging, standardization, integration and validation of data – not platform by platform, but all at once.

» The Baseline Viewpoint
» Your Value
» Best Practices

Customer Data Integration:
Core Functions of CDI Hub Processing

The Baseline Viewpoint

Beyond data warehousing and more than just programming – the key is convergence of business vision, data management tactics, and small controlled IT projects
Data integration may arguably be the most complex data challenge you have to master. Moreover, the complexity increases the larger your company and the greater your number of data sources. Because Baseline only does data, we have witnessed firsthand the complexity of integrating data, the breadth of approach options offered by various vendors, and the impact integration can have on your infrastructure and operations.

Unlike our competitors, Baseline believes that data integration isn’t just a programming activity. It’s a series of interrelated tactics and approaches to capturing and managing data that is diverse and dynamic. Baseline began its data management practice in the context of integrated data for business intelligence (BI) systems. We evolved our methods and approaches to include design and delivery of data integration capabilities.
Baseline introduces your executives to the concept of data management while helping you establish key tactics for data integration. We help you reach consensus for investment in enabling technologies. Then we help you implement repeatable processes around master data integration for cleansing, enrichment, and propagation. We work with you to design the data and technical architectures and program the data integration hubs.

Ultimately, a combination of convergence and execution is the key to success. It’s a winning combination: a “top down” vision of the enterprise data integration plan married with a “bottom up” approach via small, controlled projects.

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Your Value

Business agility through faster, less costly application development

Baseline has found that a company’s ability to meet its strategic objectives is directly proportional to the degree to which its data is integrated. Time and time again, companies that integrate data one business application at a time, as identified and prioritized by business stakeholders, emerge the winners. Each project builds a data foundation that can then be leveraged for other applications. The reusable data infrastructure saves developers time and reduces the cost of data integration and maintenance for IT applications by 30 to 60 percent. Data integration is a boon to productivity and business agility.

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Best Practices

Baseline identifies best practices through client engagements and participation in leading industry associations, like The Data Warehousing Institute (TDWI). Best practices for data integration include:

• Ensure that business needs drive IT priorities and decisions. Revisit and adjust priorities regularly.
• Define discrete requirements in the following order: business, data, and functional requirements, followed by technical specifications.
• Avoid “big bang” projects to reduce risk. Build capabilities incrementally and apply lessons learned as you go.
• Build credibility by delivering new business value every 9-12 weeks.
• Assure reuse by establishing common business terminology.
• Organize data by subject areas reflecting the business.
• Have business stakeholders define and sign off on data quality.
• Define service level agreements according to business requirements.
• Emulate real usage scenarios for data validation and user acceptance activities.
• Understand the differences between data integration development and traditional operational system SDLC.

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