Our A2B Data™ product was specifically designed to address a wide variety of issues that every organization experiences with its enterprise analytics initiatives.

Failure to Satisfy Business Needs

  • For over 30 years, organizations have used a “left to right” approach to integrate their data.
  • Data is first acquired from many different data sources (the left side).
  • It’s then entirely transformed into an enterprise data warehouse or into smaller, focused data marts.
  • Finally, an attempt is made to determine what the business needs to do with its data by creating business-focused objects (the right side). By this stage, it’s typically far too late.

Slow Processes

  • Lack of true self-service capabilities prevent Analysts from developing their own data integration components.
  • Analysts must often wait weeks or even months to interact with their data.
  • Data Integration Developers typically lack the domain expertise to understand business requirements

Limited Capabilities

  • Lack of an embedded knowledge base constrains the functionality that can be provided.
  • Extensive manual coding is required, resulting in slower development timeframes and higher maintenance costs.
  • Few pre-built components, or design patterns, are available for even the most critical functionality (e.g., Change Data Capture)

Poor Customer Satisfaction

  • Information stakeholders don’t trust the metrics they need to make business decisions.
  • Prolonged implementation timeframes for data integration initiatives impact their ability to achieve business goals.

Too Expensive

  • Traditional data integration platforms have exorbitantly high license fees.
  • Data Integration consultancies are very reluctant to participate in risk sharing engagements.

Limited Insights

  • Without a comprehensive 360-degree view of their data ecosystem, information stakeholders often find themselves in the dark regarding the origin, lineage, business logic, and utilization of their data assets.
  • Analysts must often spend an excessive amount of time researching and resolving issues that arise from this lack of clear insights.
  • Limited data insights also impact the decision-making process, delay projects, and reduce overall operational efficiency in the organization.

Complex Technology

  • Most organizations struggle to determine how to best leverage emerging technologies such as Artificial Intelligence, Data Lakehouses and Data Fabrics.
  • A wide variety of platforms have often been implemented to support enterprise analytics initiatives, including data integration solutions, data cataloging products, metadata management systems, and many others.
  • This results in higher costs, system integration challenges, and an unnecessarily complex technology portfolio.