As a data leader, you know the influx of data at our fingertips can create an abundance of opportunities and challenges. We have more data to train AI models and address important use cases, but we also have to contend with increased complexity throughout the data estate.
A modern data architecture that’s fit for purpose can provide the scalability you need to handle impending data growth, so you can operationalize AI technology and optimize your data estate. It’s the key to scaling enterprise-grade AI and it could become your biggest competitive differentiator.
What is a data architecture?
It describes how your data will be managed—from collection to consumption—using models, policies, rules, and standards set by your organization. It helps data analysts develop a true understanding of your data, regardless of where it’s located, while keeping new deployment and application requirements in mind as your business grows.
Data architectures are essential for meeting the specialized needs of modern organizations, applying advanced analytics, and using data and AI at scale.
of enterprise-controlled data will go unused1
Why is it a business imperative?
As IBV reports, “Without trusted, reliable data, even the best AI will deliver faulty, biased, or dangerous results. Yet getting your data house in order is no small task, and for many enterprises one that is far from complete.”2
In other words, the quality, security and accessibility of your data is now more important than ever.
An effective data architecture has the flexibility and high-level framework to support the speed, scale, and direction of your changing organizational needs and supports multiple uses cases, from automating proceses with generative AI to optimizing data.
Developing a detailed data strategy that defines the technology, processes, and people required to manage your data is the first step to creating a fit-for-purpose architecture—one that provisions data consistently, and with quality, for every use case.
When building your modern data architecture, consider the following elements.
Data lakehouse
A data lakehouse architecture enables data access across your hybrid cloud from a single point of entry, allowing you to unify, curate, and prepare data for AI models. It combines the flexibility of a data lake with the performance and structure of a data warehouse. Most lakehouse solutions have intelligent metadata layers that make it easier for you to categorize and classify your unstructured data.
Data lakehouses also help organizations construct price-performant workflows based on a genuine understanding of their data and their real business requirements. This enables workflow optimization, which improves costs and performance, and the discovery of hidden connections in the data.
To democratize access to data that’s been optimized and governed by your data lakehouse, you should consider implementing a data fabric.
Data fabric
A data fabric is the next step in the evolution of data architecture and management tools. It’s designed to create more fluidity across different data pipelines and cloud environments, making data securely accessible to your end users and facilitating self-service data consumption.
Data fabric architecture streamlines end-to-end integration using intelligent and automated systems that learn from your data pipelines. By integrating across various data sources, your data scientists can create a holistic view of your customers, accessible on one dashboard. The architecture then makes recommendations to better capture the value of your data and increase productivity, accelerating the time to value for all your data products.
Databases
A database is a digital repository for storing, managing, and safeguarding data sets—and it’s an essential element of a data architecture.
Applications need to be able to store, manage, and govern massive amounts of structured and unstructured data across a hybrid cloud environment to support advanced analytics and AI use cases.
To successfully operationalize AI, your organization must find the right database for the right workloads at the right price. Every database needs to be reliable, secure, responsive, and purpose-built for your specific workloads and requirements.
As you map out how your data will be accessed and managed, you’ll want to give special consideration to your infrastructure, since that’s where your data is accessed and managed.
It may be necessary to modernize your infrastructure to scale AI and to help your data engineers respond to modern workloads and demands. Many organizations are shifting to an intentional hybrid cloud approach that focuses on aligning technology with business goals, enhancing scalability, and improving overall business performance.
A hybrid cloud platform creates consistent experiences across environments, workflows, and teams.
Footnotes
1 How Strong Is Your Data Balance Sheet?, Scott A. Snyder, Knowledge at Warton, November 2022.
2 CEO decision-making in the age of AI, Global C-suite Series, IBM Institute for Business Value, June 2023.