Your company just invested in a modern, cloud-based data platform. It promises to bring you closer to realizing your vision of becoming a more data-driven organization. However, it’s not as simple as it seems on the surface. If you’re still relying on a traditional data platform architecture, you’re likely to encounter pitfalls on the path to success.
As you generate and acquire new data, your platform can’t scale appropriately. Users can’t trust the accuracy of data, and it’s hard for them to find the information they need. The prospect of adopting advanced analytics, let alone machine learning and artificial intelligence, seems as far away as ever. Instead of unlocking new possibilities, the new data platform just carries over old baggage.
The platform itself isn’t the problem. The issue is having an outmoded data platform architecture. To manage the volume and complexity of today’s business data, companies need an enterprise data architecture system that’s built for modern cloud platforms. A modern data platform architecture reduces that complexity and ensures data is accessible and usable for those who need it. Here’s how.
Data platform architecture defines how data platforms are designed and structured to meet organizational goals. As part of the broader data governance strategy, it is the blueprint for how data assets are managed as they flow through various components of the platform. There are several different approaches to data platform architecture, but it typically consists of multiple layers. For example, one approach incorporates four different layers:
When the data layer architecture is carefully crafted, organizations can overcome the data quality and availability challenges that often prevent them from developing a more data-driven culture.
With an enterprise-wide approach to data platform architecture, businesses can realize the full potential of their data platforms.
By 2026, global data volume is expected to reach more than 221,000 exabytes, a 21% increase over 2021. To accommodate the rapidly changing volume and variety of data and maintain performance, modern data platform architecture enables you to scale horizontally. This scalability also allows you to adapt to fluctuations in demand for data. For example, retailers will have higher demand for data during and immediately after the busy holiday season. The right data platform architecture allows them to scale up and down as needed.
In a recent Oracle survey, 74% of respondents said that the number of decisions they make every day has increased tenfold over the last three years. While data can help them navigate the decision process, many are too overwhelmed to take advantage of it. Eighty-five percent say that the volume of data they have to manage actually makes decision-making more difficult.
Effective data platforms serve up information that’s timely and relevant for users. This is especially important as more non-technical people are expected to use data for decision-making. A well-thought-out data management platform architecture facilitates seamless integration of data from different sources, both internal and external. Users, regardless of their technical capabilities, gain a unified view of information for better analysis and decision-making.
When organizations rapidly deploy new innovations, they run the risk of making their data platform architecture more complex. That complexity makes it difficult to maintain the architecture, limits business capabilities, and ultimately hinders the potential for future innovation.
Businesses need a modern cloud architecture that offers them the flexibility to rapidly innovate and adopt new technologies, even if they can’t predict what those technologies will be right now. By developing a data platform architecture that can support real-time processing, modularity, and flexible data schemas, they can deploy innovations today and be ready to adapt to whatever changes they encounter tomorrow.
Manual processes for disseminating data are all too common. Sixty-one percent of data leaders say that manual workflows, such as service tickets and processes that rely on custom code, hinder their ability to increase data access to users. Protecting sensitive data further complicates data accessibility. As a result, IT and data analytics teams spend more of their time reactively responding to requests instead of working on more strategic tasks.
A data platform architecture that is built around end users’ needs can address these challenges. For example, when data is automatically classified and tagged, it allows users to perform data discovery on their own. With enhanced self-service capabilities, non-technical users can create dashboards and reports and manage data sensitivity concerns without relying on support from IT and data analytics teams.
To reap the full benefits of modern data platforms, businesses need solutions that are simple to manage and allow them to evolve with rapidly changing market factors, customer needs, and business challenges. Identifying the ideal enterprise data architecture and implementing it can be difficult to navigate.
Kenway can help. We understand how people, processes, and technologies interact to unlock data insights and achieve business goals. When one asset management firm was struggling to gain a full view of its customers, Kenway’s experts designed a solution that suited the unique needs of the organization and its customer base. Now, with a platform backed by best-in-class data architecture, data management, and data governance capabilities, the organization has deeper insights into its current and prospective customers. It also has eliminated manual data consolidation tasks.
If your organization is ready to enhance its data platform architecture, contact our experts for a free consultation.
How does data platform architecture help enterprises?
Data platform architecture helps enterprises streamline and integrate data between various sources and scale their data capabilities. With more efficient workflows and a scalable infrastructure, enterprises can empower users to make data-driven decisions and foster innovation.
How many types of data architecture are there?
There are numerous different types of data architecture, with the Zachman Framework, The TOGAF Standard, and the Federal Enterprise Architecture Framework being some of the most popular.
