Unlocking data analytics: eight strategies for effective cloud data design and management for Google Cloud

Unlocking data analytics: eight strategies for effective cloud data design and management for Google Cloud

Deepak Kumar Arya

30 Sep 2024

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Learnings and best practices based on successful Google Analytics data platform implementations

Effectively leveraging enterprise data is one of the best ways to grow a business. Data-driven businesses are 23 times more likely to surpass their competitors when it comes to acquiring customers, nearly seven times more likely to retain them, and 19 times more likely to stay profitable. They're also improving operational efficiencies and saving costs.

An increasing number of organizations are successfully monetizing data and leveraging it to boost their top lines. In the last four years, organizations have, on average, improved in activating data, unlocking its value, and scaling infrastructure, platforms, and tools that enable them to leverage data more effectively.

Today, nearly two in three executives agree that their organizations use activated data to introduce new products or services or to develop entirely new business models. But there are challenges when it comes time to actually implement cloud systems.

Numbers from HFS Research show that only about one third of organizations are realizing cloud-implementation ambitions even though 65 percent have made strategic investments. So, how should companies build towards the results they need?

Creating a structured and secure data ecosystem

The ability to share data across an ecosystem of stakeholders is a core component of turning data into insights and action that make it useful, marketable, and widely consumable. But many companies make one big mistake when moving to a cloud platform: they treat data the same way they did when it was on premises, overlooking opportunities to operate in new, more efficient ways. The beauty of cloud data platforms is their ability to manage data in ways that weren't possible before - as long as the data ecosystem has been set up securely and effectively.

A data analytics platform on the cloud manages, processes, and analyzes large amounts of data in a more scalable and flexible way than on-premises infrastructure, and businesses can use them for everything from data ingestion and storage to data processing, analytics, and visualization. Ultimately, cloud data platforms enable enterprise organizations to become data-powered enterprises thanks to better decision-making abilities and more efficient operations.

Eight strategies for data analytics implementation

For the last 10 years, Capgemini has worked with Google Cloud Platform's data analytics services and team to serve some of the world's largest enterprises in retail, manufacturing, finance, and more to modernize data estates and leverage cloud and AI/ML tools to extract and share actionable insights - reducing costs and increasing top-line revenue. Here are eight of our key learnings.

• Create a data platform architecture that drives success. Everything you can achieve with cloud-based data analytics will revolve around your core platform architecture. And to function effectively, three conditions must be met.
  • The ability to host all data formats: This includes real-time and batch data.
  • The segregation of business domains: These should be linked using a common key.
  • The right file format: While most data is in CSV files, better formats include Avro and Parquet, as they both provide better compression and the fastest read and write experience to the database.
• Data ingestion must be in a common format across all domains to enable reuse. Cloud data systems ingest new data much more efficiently and they enable data harvesting from sources such as Google Ads and social media platforms, which provides a more holistic view into customer behavior and feedback. But data ingestion must occur in a common format across all data domains. Again, I recommend going with Avro or Parquet.
• Use cloud-native partitioning and indexing. In most cases, moving data to a cloud platform is not just a lift-and-shift operation. It requires a process of data transformation that converts one style of business logic into another. Many enterprises, when moving to the cloud, look to replicate legacy partitioning and indexing to their new cloud database. However, cloud-native databases such as BigQuery and Bigtable are designed differently than many older on-premises systems. Defining the business key and the partitioning are critical for optimizing the query, which leads to lower costs.
• Consider data security from the beginning. Data security is often omitted in the design phase when moving to a new platform, and this can lead to many issues down the road when the organizations have to meet security compliance objectives like GDPR and data privacy and access. Some tools integrate data security parameters right out of the box, such as Google DLP, an excellent tool to identify and secure PII data. This means data security has to be included as part of design and not bolted on as an afterthought.
• Data lineage is key for resolving production issues, and has to be part of design. Data cataloging is critical to making data accessible across the entire organization. Sharing data products through a data catalog, so there is a consistent definition of the data products across all the dashboards and applications, ensures enterprise users are speaking the same language.
• Enable generative AI. Is your analytics platformGen AI-enabled? Gen AI is invaluable in helping organizations leverage the full potential of data analytics. It's also useful for unlocking new insights and reducing time to market on new use cases. For example, BigQuery can use Google's Gemini AI for more user friendly, prompt-based searches.

AIOps design is now available to quickly identify the core issues leading to high cloud cost and quickly deploy the solution. (For example, poorly design BigQuery SQL can be quickly highlighted for the Ops team.)

• Include FinOps and AIOps to BusinessOps. One of the major issues faced by customers is the high cost of moving to the cloud. This is often due to poorly designed FinOps modes, or lack of a design. FinOps has to be part of the design process so that we can then feed the usage data of the cloud services into the FinOps dashboard for active monitoring of the usage and cost parameters.
• Understand how your data analytics platform will drive value. Many organizations struggle to extract business value from their data. Realizing true value depends on the following technical requirements:
  • Data domains must be linked using business keys, and this has to be part of the design
  • Designing Data-as-a-Service or Data-Products-as-a-Service allows common definitions and reduces duplications
  • Data platform design must include the ability to ingest new data sources
  • Setting lifecycles for data products is important as new variations are created
  • Many successful organizations provide an end of life for a data product.

The critical factor in all of this is that choosing and implementing a data analytics platform is a long-term decision. In addition to developing the right architecture and databases for the current requirements, consider future use cases your organization may face five or 10 years from now. The ideal platform is robust and flexible enough to accommodate changing needs.

For customers who do not want to wait for a new design, we typically recommend a re-platforming strategy. However, many organizations are choosing to combine both a new design and a re-platforming approach to achieve the best of both worlds. And that and our other advice gives them a much better chance of joining the group of companies that realize cloud-based ambitions.

Interested in exploring Google Cloud Analytics data platform implementations? Contact us for more information and an assessment.

Senior Director

Deepak is currently leading the GCP Practice, ETL, MDM, Data Governance, and all cloud delivery at Capgemini I&D India, managing around 2,500 team members. Driven by new challenges, he prioritizes people connections to achieve business objectives and seeks opportunities where technology can enhance efficiencies and drive revenue.