Why Do Data Integration Projects Fail?
Industry research reveals that most data integration initiatives fail. Common problems include:
- Late delivery and over budget
- Poor performance of queries and loads
- Unacceptable data quality for accurate analytics and reporting
- False starts, potentially resulting in complete rework
Why does this happen? (Many reasons.) Can it be prevented? (Yes!) Questions like these deserve your utmost consideration before you begin a large-scale initiative, or attempt to rework an existing solution. In our experience, one of the key issues underlying integration project failures is ineffective planning. Myers-Holum has developed an Assessment and Strategic Planning offering to address this need.
Our Approach to the Problem
Our typical structured approach includes four phases, as follows:
The approach, scope, deliverables and timelines for the assessment are defined and approved. Team members, both from Myers-Holum and the customer, are identified. Other pre-project activities such as the scheduling of interviews and organization of a team kick-off meeting help ensure a highly successful engagement.
The team establishes a solid understanding of the current environment and the business drivers and objectives this environment must support. This process is based on a review of existing technical documentation and interviews with both technical and business staff. Key business personnel help us identify the relevant business strategies, initiatives and budgets.
The team identifies any gaps in the integration environment – technical, system, data, and business process, analyzing each component to suit the client’s requirements. Preliminary solution options are weighed.
The team formalizes the solution recommendations, including an iterative implementation strategy. Appropriate project plans – schedules and budgets – are developed based on this strategy. The business benefit or ROI for each iteration is identified.
Typical deliverables from this type of engagement can include one or more of the following:
- Findings and Recommendations
- Business Justification and Range of Magnitude (ROM) budget
- Organizational Model
- Defined technology platform
- High level initiatives / projects to deliver the ‘Future State’ solution
- Roadmap of prioritized projects / timeline
- Architecture diagram of ‘Future-State’ vision, including:
- Conceptual, logical and physical data models
- Real-Time Load Dependencies
- On-premise or cloud
- Hardware and software
- Operating systems, storage and network
Data Integration Architecture
- Data movement from source to target
- Data Quality and Data Cleansing
- Balance & controls
- Decision Support