Organizations are currently wasting billions of hours a year manually performing complex data tasks that are too hard for them to automate with existing tools. These manual tasks include orchestrating data consolidation from a large number of sources, rebuilding analytical workflows from scratch every time new data becomes available, building unwieldy Excel spreadsheets to handle multi-step data transformations, and hiring resources to write ad hoc code for repetitive data analytics and data science projects.
Most organizations currently attempt to manage their complex data workflows through a mixed bag of manual, disconnected solutions at each step of the chain with humans exerting significant effort to connect the dots. This represents a huge opportunity for consolidation and cost savings.
An advanced data automation platform is architected with the deep capabilities and flexibility needed to automate any data-driven task. This can be accomplished through no-code intelligent building blocks, which leverage advanced configurability and responsiveness to adapt to even the most complex workflows.
As an example, at Cube, through work we've done with existing customers, we've been able to reduce data operating costs by up to 90% across a diverse set of use cases.
Let's break down what a data automation tool does, and how it can help your organization.
A data automation platform is often no-code and helps companies easily automate their most complex data workflows. This means a single nontechnical user can effortlessly do the work of an entire data team.
In the case of Cube, this is accomplished through an intelligent building block architecture. Using a point-and-click interface, users rapidly create building blocks that:
These building blocks are linked together to create workflows that orchestrate your data across four main functional areas:
Integrations with any data source through APIs, data connectors, and other custom integrations, ingesting them into a centralized location with a standardized structure.
Intelligent processing to transform and interpret data on a deep level, covering a wide range of capabilities -- ETL, multilayered data calculations, custom logic, text analytics, supervised and unsupervised learning, predictive analytics, and more.
Automated analytical outputs. Senior executives and less data-savvy users access results in visual formats (e.g. dynamic web dashboards or powerpoint presentations). Data-centric users access results through interactive applications (e.g. data table exploration views, REST APIs and business workflow apps).
A marketplace that lets users exchange data assets created on the platform. Data assets can be datasets, dashboards, data science models, workflows, or entire applications. Users can leverage these assets as blueprints to accelerate their automation efforts.
Data automation platforms are most effective when they connect end-to-end workflows. In the absence of a unified data automation platform, automation breaks down and users resort to a high amount of manual effort to connect the dots across multiple siloed data tools.
The enterprise data space can be reduced to two buckets for the purpose of analyzing how data automation platforms compare to more generic data analytics tools:
These tools are not able to automate complex data workflows. They are one-size-fits-all tools that deliver data visualization that sits on top of datasets already transformed through other means. Any data workflow capabilities that exist are primarily within the realm of basic ETL. Organizations using these tools have failed to achieve deep automation and are often looking for competitive alternatives.
Data automation platforms tackle the automation of complex analytical workflows across data collection, processing, data science, visualization and custom functional apps. Some of these platforms are geared towards a generic audience and others are more technical. A platform like Cube is built with the user friendliness and intuitive UI for business stakeholder needs, but with deep flexibility and capabilities to satisfy technical audiences.
Data automation platforms can be a powerful way of driving productivity across your organization. The key is choosing an automation platform that has broad coverage across all parts of the data lifecycle, yet is deep enough to tackle the complexity of your workflows.