The Data Cloud for Enterprise Analytics

As enterprises look to make their businesses more data-driven, they face major challenges in managing the ballooning scale and complexity of their data analytics workloads. 

Relying on hundreds or even thousands of data sources — anything from sales KPIs and log files, to click streams and videos — is no longer unusual. Many large organizations also already maintain 50 or more analytics applications that collectively generate a billion queries per year, and those numbers could grow exponentially in the near future.

But data silos and the limitations of traditional analytics architectures (e.g., inconsistent management of data sources) complicate any attempt to drive digital transformation with analytics. Fortunately, the emerging data cloud provides a solution.

Data cloud a unified data management ecosystem

What is a data cloud?

The data cloud is a unified data management ecosystem that enables easy reuse, portability, and consumability of enterprise data. It is powered by a single cloud analytics platform, which connects all of an enterprise’s data sources, data warehouses, data lakes, workloads, and supporting infrastructure in a cloud of choice.

Data cloud architectures help eliminate silos and fragmentation, making it easier to access, query, and transform data into consumable insights. In a complex multi-cloud world in which enterprises routinely rely on multiple cloud service providers (CSPs) alongside on-premises resources, the data cloud provides much-needed consistency.

How does a data cloud work?

The data cloud works via three main components:

1. Data sources

A data cloud solution can collect, process, and scale enterprise information from multiple source systems, whether the infrastructure is hosted in a major public cloud or on-premises. This data may be unstructured, semi-structured, or structured. And as it all goes into the data cloud, it gets refined in a consistent way, without the technical debt that comes from traditional approaches that rely on a “pipeline jungle” of redundant processes.

2. Data warehouses, data lakes, and related data architectures

Depending on the business requirements at hand, enterprise data from those sources may live in a:

  • Data warehouse: A tiered architecture that integrates data from multiple sources and makes it accessible to front-end clients, through which users can gain insights.
  • Data lake: A centralized repository for any type of data in its raw format, with management usually done with open-source software like Hadoop.
  • Data lakehouse: A combination of the above, adding capabilities such as streaming analytics and reporting dashboards atop a data lake.
  • Data mesh: A decentralized architecture built around logical domains corresponding to different parts of the business.

Each of these architectures can connect to a cloud data platform, which can leverage its SQL engines, data fabric, and APIs to deliver consumable insights.

3. Future-ready cloud data platform

This is the “brain” of the data cloud. It processes and scales data from every source and architecture to create a single source of truth that can be stored once and reused many times.

Whether a company needs only basic data warehousing or requires more advanced functions for analyzing geospatial and time series data, the cloud data platform within a data cloud can do it all. This platform can also be deployed in multiple ways, including in single- and multi-cloud environments and on-premises, for maximum cloud choice and flexibility.

How enterprises are solving problems with the data cloud

This versatility made a data cloud the best way forward for Medibank, one of Australia's leading health insurance and wellness services providers.

Before implementing a data cloud, Medibank was struggling with customer service issues including tax statement availability and other accessibility matters. This fostered a negative perception among Medibank customers that the organization knew it had to turn around — and that's exactly what it labored to do.

Medibank began a digital transformation campaign to transition from legacy tech and practices to a future-ready approach, and the adoption of a cutting-edge data cloud was instrumental to these efforts. Coupled with the implementation of a modern enterprise analytics platform, the cloud allowed the organization to unify its various data sources into a cohesive ecosystem. This decision ultimately helped Medibank realize the following benefits:

  • A great leap forward in efficiency across multiple areas, including automation, process standardization, quality assurance, and compliance.
  • Timely preparation and distribution of tax statements, a task of considerable difficulty in the years before data cloud adoption.
  • Apps based in the cloud allow Medibank to maximize the value of data in marketing, accounting, member health, and customer service efforts.

Six must-have capabilities of the data cloud

The Medibank data cloud embodies some of the key capabilities that any such implementation must have. Data clouds at a minimum should offer: 

1. Modern data source ingestion and processing

Data clouds should be able to load and query any data across cloud and on-premises environments. From IoT sensor data and digital twins to ERP and customer relationship management (CRM) entries, a data cloud can consistently ingest, aggregate, prepare, and serve all business data in one place. It opens the path for advanced analytics without costly process redundancies.

2. Choice of clouds and deployment models

Many enterprises have a multi-cloud strategy in place. The data cloud is built for this reality, as it can be deployed on any combination of AWS, Microsoft Azure, and Google Cloud. Moreover, it can be set up on-premises, too. That makes it ideal for hybrid cloud architectures designed for use cases like exchanging large amounts of data between applications with minimal latency.

3. First-party cloud provider integrations

Beyond just being deployable on different cloud vendor services, a data cloud has first-party integrations that help drive an accelerated data analytics ecosystem. These integrations can make the most of each provider’s cloud storage options, data lakes, data pipelines, business intelligence services, AI and machine learning, monitoring APIs, encryption, and reporting.

4. Self-service for easy reusability and portability

The self-service capabilities of a data cloud empower users across an enterprise to extract insights from data without having to first replicate or move it. Data becomes highly accessible, reusable, and portable. Because a data cloud so efficiently loads and queries data from multiple sources within a single unified environment, there’s also less data fragmentation and siloing.

5. Advanced optimizations for cost and performance

A data cloud solution offers optimized pricing for each workload, via a mix of pay-as-you-go and predictable cloud service billing. Separated compute and cloud storage economizes costs as well. On the performance side, dynamic resource allocation helps with meeting multiple application service-level agreements and with handling a mix of strategic and tactical workloads.

6. Security and compliance

Data clouds must protect the business-critical and often sensitive information that flows through them. They can integrate with security services from major cloud vendors and also come with their own monitoring capabilities. These protections shield enterprise data even from sophisticated modern threats.

The journey into the data cloud

Data cloud is still a relatively new concept and it will evolve as more enterprises update their business strategies for the multi-cloud reality. Teradata Vantage is the connected multi-cloud data analytics platform that makes a data cloud achievable today.

The most flexible data platform in the industry, Vantage can be deployed in the cloud computing environment of your choice, on-premises, or on commodity hardware. It can ingest any type of enterprise data and connect all of the information involved in one place, for consistent high-performance analytics.