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Pain Points:

Failure to Satisfy Business Needs:For over 30 years, organizations have integrated their data using a "left to right" approach, moving data from diverse sources (the left) to enterprise data warehouses or data marts (the right), often addressing business analytic need too late in the process.

Slow Processes:Analysts face delays in accessing their data due to the lack of self-service capabilities and developers' limited understanding of business requirements.

Limited Capabilities:The absence of an embedded knowledge base in the existing data integration platform leads to increased manual coding, higher costs, and a lack of essential pre-built components such as Change Data Capture.

Poor Customer Satisfaction:Stakeholders' lack of trust in the quality of crucial metrics, combined with extended data integration implementation timeframes, hinders the achievement of business goals.

Too Expensive:Traditional data integration platforms carry steep licensing costs, and consultancies are reluctant to engage in risk-sharing agreements.

Limited Insights:The absence of a holistic view of the organization's "data ecosystem" leaves stakeholders uncertain about the origin and lineage of their data assets, leading analysts to invest extensive time resolving related issues, and delaying the successful implementation of enterprise analytics initiatives.

Complex Technology:While trying to navigate the ever-changing landscape of traditional and emerging technologies, many organizations adopt a myriad of platforms, leading to escalated costs, integration hurdles, and an overly complex technology portfolio.