Data Cloud: Building the Foundation for Modern Applications in a Hybrid Environment

In the 2000s, the proliferation of the Internet and digital devices led to an exponential growth in data being generated and collected, giving rise to the term “big data” to describe the growing volume, variety, and velocity of data created from various sources. Fast forward to the present, demand for Artificial Intelligence (AI) and generative AI applications is intensifying, with enterprises looking to gain an advantage and competitive differentiation by harnessing the benefits of AI and generative AI from both business optimization and employee productivity perspectives. One thing in common between big data and AI is the importance of a robust data management strategy.

Technology providers understand the challenges enterprises face when deploying AI or generative AI applications. Cloud hyperscalers such as Microsoft Azure have introduced Microsoft Fabric, an analytics platform that integrates individual Azure applications into a unified product. Amazon Web Services (AWS) works with software providers such as HighByte, Palantir, and EOT to create a data management architecture as part of AWS’ industrial data fabric platform. Software-as-a-Service (SaaS) vendors such as Salesforce and Snowflake have introduced data clouds that bring enterprise data to the cloud platform, leveraging generative AI for data discovery and analysis.

Data cloud is a platform that aims to unify enterprise data infrastructure, providing a transparent and holistic view of the entire business by breaking down disparate and siloed data. This integration helps enterprises drive better customer experience, tighter data access control, and accurate data analytics, enabling enterprises to make better-informed decisions.

Previously, business and operational data were deployed on public clouds and on-premises at data centers, creating a disparate and siloed data infrastructure. This prevented enterprises from fully benefitting from data that are being created and stored. This causes a disconnect within the business. For example, in a sporting goods manufacturing environment, often the production supply chain application is deployed on-premises, close to the production facility. The marketing department uses a SaaS Customer Relationship Management (CRM) application. This scenario breeds data silos and disparate data management, causing a delay in customer fulfillment.

The emergence of AI and generative AI has significantly impacted the need for a data cloud or a data fabric architecture. A data cloud can include components such as data storage, data integration, data analytics, and data security/governance. Traditionally, each of these data components was deployed separately, causing complexity and confusion. Data cloud enables enterprises to facilitate the transfer, management, and processing of these data on the cloud, allowing for efficient, scalable, and flexible data processing.

The deployment of a data cloud can have a significant impact on application modernization. By adopting a data cloud platform, enterprises will be able to facilitate seamless data sharing on the cloud, driving better integration between different data sources or formats. Data cloud platforms also provide cutting-edge analytic tools including Artificial Intelligence (AI)/Machine Learning (ML) applications that allow enterprises to run analyses such as pattern recognition and predictive analysis. The integration of enterprise data into a unified data cloud platform allows enterprises to better extract and leverage the benefits of the various data scattered across the business.

A data cloud framework represents a significant shift in approaching data management. Enterprises that are looking to embark on a data cloud journey must have a clear technology strategy to reap the benefits of a data cloud. Some of these considerations include:

• Public Cloud Platform: The foundational component of a successful data cloud framework lies in choosing the best public cloud provider for the business. While AWS, Microsoft Azure, IBM Cloud, etc. all support data cloud architectures, these cloud providers differ in the technology stack, diversity of partners in their ecosystem, regional data center availability, and support for private and hybrid cloud environments.
• Data Governance: Integration of data from various sources, formats, and locations represents a challenge in security and compliance. Policies and procedures need to be established to ensure data governance, data privacy, and compliance with existing jurisdictions. Compliance with existing industry regulations such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in healthcare will also help shape a robust data cloud framework.
• Leverage Data Analytics, AI, and ML Models: One of the main benefits of deploying a data cloud architecture is the ability to leverage data analytics and AI models, running in the cloud. By modernizing the data management architecture, enterprises can utilize ML models to enhance predictive data capabilities and ensure accurate decision-making.

Deploying a data cloud framework can be a complex undertaking, involving various moving parts across the entire business. However, in the current environment of AI, ML, and generative AI differentiation, the urgency to have a robust data infrastructure that can support these cutting-edge technologies has intensified. In the race toward building the latest shiny new AI solution, many enterprises have relegated data management to a secondary thought, which will lead to AI solutions that are less optimal, or even worse, fail.

By adopting a data cloud strategy, enterprises can unlock the full benefits of their business data and complement these insights with external data, integrated into a scalable, flexible, and secure environment. A unified data cloud strategy promotes close collaboration between business units and trust in the data, bridging the gap for hybrid environments within a business that ultimately translates to better innovation and customer experience.