Data as a Service

Make enterprise data readily available

As organizations continue to mature their data management operations, processes and technology stack, the use cases for data will continue to broaden. Capabilities must be in place to support not only analytics and data science consumption patterns, but as the volume, trust and reliability of the data increases, the need will arise to start replacing the operational data stores permeating across the enterprise.

Companies that shift their data management capabilities to support both use cases will see huge advantages in their ability to derive value out of their data, for literally any use case. This is what we call establishing “Data as a Service” (DaaS).

DaaS brings together the technologies necessary to retrieve data from heterogeneous sources such as transactional databases, data warehouses, enterprise resource planning (ERP) systems, and customer relationship management (CRM) solutions.

By implementing DaaS, companies can realize:

  • Accelerated data aggregation
  • Accelerated consumption
  • Increase in agility to serve current and next-gen data consumption patterns
  • Affordability by removing intermediate data marts and operational data stores

However, for DaaS to deliver on its promise, many challenges need to be overcome, such as:

  • Standardizing ingestion to support multiple sources of data from structured to unstructured, real-time and batch.
  • Integrating data from multiple systems of record in various file formats, which is essential to capture “the single source of truth”.
  • Defining a plan for metadata management and lineage to ensure data is discoverable, reliable and searchable.
  • Defining common data definitions and hierarchies in a common data catalogue so that data can be captured, collected and revealed.
  • Establishing an enterprise domain model to provide access to secure and governed data; protecting not only the data assets but the reputation of the organization maintaining this data.
  • Presenting the data seamlessly to consumers – both analytical and operational.

Much of the complexity within implementing DaaS involves linking front-end channels to the data. This involves a thoughtful architecture that presents the data seamlessly through presentation layers, including APIs and microservices.

Next Pathway has extensive experience in establishing DaaS platforms for large enterprises, regardless of their maturity level. This includes everything from establishing a well-governed Data Lake, to developing a presentation layer for efficient and flexible enterprise consumption.

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