Data is the new oil - a potent resource that can fuel innovation, improve decision-making, and transform businesses.
According to IDC, worldwide data is set to reach 175 zettabytes by 2025. However, inefficiencies and unreliable data sources have significantly slowed down data professionals, with around 86% of analysts dealing with outdated data and over 60% having to wait for data to be cleaned and prepared every month.
But as data volumes grow exponentially, companies often struggle with collection, storage, and organization of this invaluable resource. This is why it's become imperative to leverage the security and scalability benefits of the Cloud.
To help organizations make the move with confidence, our partner, Google, has created a white paper on the key decision points necessary in creating a modern, unified analytics data platform. Download it now to discover key insights and findings or keep on reading to learn more.
The Data Conundrum
The heart of these issues lies in outdated organizational structures and architectural decisions that lead to a gap between data aggregation and usage. Companies are keen to modernize their data analytics systems by migrating to the Cloud, but this move alone isn’t enough to overcome the challenges of siloed data sources and brittle processing pipelines. Strategic decisions around data ownership and storage mechanisms must be made in a holistic manner for a data platform to be truly successful.
Modern businesses now aspire to create a unified analytics data platform built on Google Cloud Platform.
But why is this necessary? While big data has created vast opportunities over the last two decades, presenting relevant, actionable, and timely data to business users has become increasingly complicated.
Most companies are yet to realize tangible value from their data, and the two primary issues are data freshness and difficulties in integrating disparate and legacy systems across silos.
Envisioning a Unified Data Platform
In an ideal scenario, companies might envision a simplified structure where there's one set of consistent data sources, one enterprise data warehouse, and one tool for business intelligence. But in reality, organizations are far from simple, and unanticipated complexities around data ingestion, processing, and usage frequently crop up.
From conversations with hundreds of customers, it's evident that a more holistic approach to data and analytics is needed - a platform that meets the needs of multiple business units and user personas, with minimal redundancy in data processing. This requirement extends beyond just purchasing new software components or crafting a new architecture. It calls for companies to assess their overall data maturity and make organizational changes in tandem with technical upgrades.
As companies advance, by the end of 2024, 75% of enterprises are expected to move from piloting to operationalizing AI, leading to a five-fold increase in streaming data and analytics infrastructures. But here lies a challenge - the architectural friction that keeps data ownership segmented, limiting the insights from reaching production systems.
The Importance of Data Lifecycle
The entire lifecycle of data involves several users, each with a unique perspective on data governance, freshness, discoverability, metadata, processing timelines, and more. This diversity in user perspectives often leads to the use of different systems and software to operate on the same data at different processing stages, making the case for a comprehensive platform that can serve all.
The big question then arises: Data warehouse or data lake?
Both have their merits and drawbacks, and the choice depends largely on the intended usage, types of data, and personnel. While data warehouses often prove difficult and expensive to manage, data lakes pose their own challenges of scaling, cost, and governance.
Bridging the Gap
Considering these trade-offs, many companies opt for a hybrid approach, where a data lake is set up to feed data into a data warehouse, or a data warehouse has a side data lake for additional testing and analysis.
This approach aims to bridge the gap between understanding and exploring the business by merging these functions and their data systems. It creates a virtuous cycle where a deeper understanding of the business drives directed exploration, which in turn enhances the understanding of the business.
The objective is to treat data warehouse storage like a data lake. On the Google Cloud Platform, you can build both separately, but you don't have to pick one over the other. In fact, the convergence of data lake and data warehouse into a unified set of functionalities is possible, giving rise to a modern analytics data platform. Google's smart analytics platform powered by BigQuery, for instance, serves as an ideal example of such a converged, serverless data solution.
Emerging Trends: Lakehouse and Data Mesh
Two emerging concepts further strengthen the idea of a unified analytics data platform - Lakehouse and Data Mesh. Lakehouse brings together the best of data warehouses and data lakes, enabling a wide variety of data types and volumes. On the other hand, Data Mesh decentralizes data ownership among domain data owners and facilitates communication between different parts of the organization, leading to a distributed data architecture.
Dealing with Legacy Systems
Transitioning to a new data platform from the ground up isn't always possible for companies dealing with existing legacy systems.
However, Google Cloud offers solutions for every stage of the data platform journey, including lift and re-platform, full modernization, and lift and rehome strategies.
In conclusion, Google Cloud significantly simplifies data analytics, removing the traditional constraints of scale, performance, and cost. Its unified analytics data platform allows businesses to unlock the potential hidden in their data, facilitating the operationalization of insights across the enterprise.
The end goal is to move beyond data silos and step into the realm of insights and action, all powered by a cloud-native, serverless approach.
Want to learn more about the power of Google Cloud? Download the white paper Build a Modern, Unified Analytics Data Platform With Google Cloud.
Still thiking about modernizing your data and analytics strategy? Check out our modern data and analytics accelerator to jumpstart your journey.
