Cloudera Data Platform as a multifaceted value proposition
Cloudera Data Platform (CDP) represents a paradigm shift in today’s data platform architecture, catering to all current and future analytics needs. It is based on Cloudera Data Hub (CDH) and Hortonworks Data Platform (HDP) technologies and provides a holistic, integrated data platform – from edge systems to artificial intelligence, helping customers accelerate complex data pipelines and “democratize” data assets.
In this introductory article, I will present a comprehensive framework that reflects the benefits of CDPs for technology and business stakeholders. I developed this framework to help organizations make the business case for CDP investments, and to provide a mechanism for prioritizing analytics investments based on specific business goals (such as lowering technology costs or accelerating initiatives to drive organic growth).
The assessment framework includes four dimensions: 1) business value increase, 2) reduction and / or elimination of technology costs, 3) optimization of infrastructure costs, and 4) operational efficiency.
Business value framework for CDP:
In the following sections, I will present an approach to quantifying each of these dimensions.
Increasing business value
This category describes CDP’s differentiating capabilities for accelerating deployment of use cases (and realizing associated business value) that:
1. Provide a diverse set of analytical tools for different use cases throughout the data lifecycle (data streaming, data engineering, data warehouse, operational database, and machine learning).
2. Offer their own integration mechanism between analytic platforms through Shared Data Experience (SDX) to simplify the deployment of complex pipelines.
3. Provide the ability to expand the use cases using different formats and types of data (both structured and unstructured) from multiple sources.
4. Provide a robust SDX security and governance mechanism that helps scale the platform for the growing number of users and roles in the organization.
To quantify the acceleration of business value growth, you always need to take into account the specifics of the industry and the customer. For example, in the case of a large healthcare provider implementing a CDP, I was able to demonstrate business value by articulating the ability to accelerate time-to-market for inorganic growth initiatives such as
For future asset sales and provisioning, CDP Public Cloud accelerates the separation of data assets and analytic workloads in an elastic and scalable cloud environment. This benefit is provided by Replication Manager, a key CDP functionality that accelerates the migration of existing on-premises use cases to the public cloud by extending security and management configurations.
For future acquisitions, CDP will function as a single landing page for all of the acquiring organization’s big data workloads, regardless of the platform it was originally on (for example, previous versions of CDH / HDP, other cloud storage, or legacy on-premises platforms). Given the breadth of opportunities that CDP offers, it will help close the technology gap and accelerate IT integration activities, which are key to realizing the business value of M&A strategies.
Reducing / eliminating technology costs
To help clients reduce (or eliminate) the cost of assistive technology tools that are used in conjunction with competing analytics solutions, CDP provides the following capabilities:
Cloudera control plane Replaces infrastructure monitoring tools by offering a single dashboard for monitoring clusters deployed across a variety of on-premises and cloud environments.
Apache Ranger (part of Shared Data Experience – SDX) replaces data security tools by providing a granular mechanism for data access policies.
Cloudera Data Catalog (part of SDX) replaces management tools, facilitating centralized data management (data cataloging, data retrieval / origin, tracking data problems, etc.).
Workload Manager (part of SDX) Replaces big data application performance management tools by offering its own mechanism for analyzing performance and troubleshooting specific jobs or workloads (eg, query failures, latency).
SDX acts as a data abstraction layer that decouples data assets and context from the underlying data processing and storage layers. This eliminates the need for third-party data orchestration / abstraction tools that often try to provide some level of semantic consistency between heterogeneous, isolated data stores that are typical of point solutions.
Areas of Eliminating Technology Costs Using CDPs:
Optimizing infrastructure costs
Infrastructure is the highest cost in the total cost of ownership (“TCO”) equation for analytic use cases deployed in either on-premises or public cloud. CDP helps customers optimize overall infrastructure costs by providing a choice of hosting type (public cloud, on-premises or hybrid cloud) and cloud provider (such as AWS, Google, or Azure). This is enabled by Shared Data Experience (SDX), which allows you to seamlessly transition from one infrastructure type or cloud provider to another with minimal migration effort. As a result, CDP helps clients:
Optimize on-premises installation costs by allowing on-premises workloads to be delegated to the cloud based on consumption patterns and infrastructure economics. Thus, customers can reduce or even avoid data center expansion by leveraging the elasticity of the public cloud to meet peak capacity needs or free up local capacity.
Optimize cloud computing and storage costs with a multi-cloud deployment model that helps you minimize cloud costs based on relative unit costs across cloud providers.
In addition to minimizing infrastructure costs, CDP allows organizations to avoid being tied to specific cloud providers. This advantage drives the value proposition of the Cloudera data platform, not only in terms of short-term cost reduction goals, but also strategic goals for provider diversification.
Operational efficiency
This category reflects the value that Cloudera Data Platform provides to technology and business stakeholders in terms of operational efficiency for operations throughout the data lifecycle. These operations can be divided into the following categories:
End User Operations: CDP accelerates data operations (“DataOps”) and machine learning operations (“MLOps”) by providing an integrated technology platform that enables data scientists, data engineers, and analysts to collaboratively analyze and interact with data and deliver end-to-end results. data pipelines, and so on, without integration delays or having to deal with disparate chunks of data.
Security and data management operations: CDP provides advanced security and management capabilities for information security and data management teams. These capabilities simplify security operations (“SecOps”) such as managing user authentication and authorization. It also provides robust data management through Shared Data Experience (SDX), allowing you to centrally manage your data assets.
Data Platform Management: platform administration teams can take advantage of the built-in integration of all analytic frameworks and security / management tools, since they do not have to deal with heterogeneous technologies and perform integration work (for example, customize their own integration mechanisms), manage dependencies, incur customization overheads and etc.
Thus, the Cloudera data platform allows all direct and indirect users of the analytics environment to minimize the effort spent on non-value-added tasks and focus on what matters most: getting value out of the data.
Each of the four criteria I have presented has a different meaning (or “weight”) depending on the industry and the context of a particular customer. For example, a technology organization that is rapidly evolving its data offerings and / or entering new markets should place greater emphasis on accelerating business value creation, while an organization that aims to rationalize costs should focus on reducing or eliminating costs. In developing a data platform strategy for our customers, I try to articulate their business priorities and goals in detail and adapt this model accordingly by identifying the right parameters of value and assigning appropriate weight to each of them based on relative importance.
