After having been a part of building several data analytics practices over the last two decades or helping technology and business executives grow their business and gain market share, we thought we knew how to maximize value from data. Yet, we continue to be awed by the new ways that cloud enables companies to transform their data foundations to extract even more value from their data. Take the example of one company who, by consolidating over 14 disparate legacy source systems into a scalable, cloud-native data foundation, built an enterprise people platform with cross-domain data that captures all aspects of employee hiring, training, day-to-day operations, wages and retention.

Everywhere we look these days, we see companies across all industries working to modernize their data foundations as this company did. And the top ticket to modernization is the cloud.

Why the cloud makes data more valuable

Today, data is generated everywhere. It comes from humans, machines, IoT devices, edge systems—here on earth and beyond, in space. All that data adds up. Today, we are generating a staggering 2.5 quintillion bytes of data daily.

But all that data is worthless unless it is accessible and broadly applicable—its value comes from use. The challenge is to not only handle the sheer volume and variety of data that arrives from many disparate sources but to realize its full value. That only can be achieved through a truly modern data foundation on the cloud. Cloud is the only place where data gains the necessary scale and agility to be accessible, applicable, and ultimately valuable.

Cloud-based architectures and platforms enable all types of data to come together in one place as a powerful collective resource for the entire enterprise. On the cloud, you can ingest huge amounts of data in real time, get all that data to work together seamlessly, and then employ analytics, AI and automation to adapt the data for all kinds of use cases.

Consider the company we mentioned earlier. Inundated with data, including ingesting half a billion data records from 30,000-plus locations on a daily basis, the company needed a solution that could handle all that data without requiring custom coding to generate reports. By building a metadata-driven framework using native Spark, it not only manages the daily data deluge into robust quality reporting, but it also helps connect the dots on employee hiring, training and satisfaction, stores’ performance metrics, and the impact of employee turnover on business profit and loss.

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The top ticket to modernization is cloud

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What constitutes a modern data foundation:

Fundamentally, a data foundation consists of frameworks, capabilities, tools, and services that efficiently and effectively store, process, manage and serve data. Through modernizing, you can enhance your data foundation to achieve three key characteristics: modern data engineering, AI-assisted data governance, and data democratization. These characteristics are key to blending data from different sources together in real-time, building agile reporting, and leveraging analytics and AI to derive valuable insights and deliver meaningful business outcomes.

Modernizing your data foundation:

  1. Set the stage
    It begins with asking and answering the question: What data do you need and in what form to run and grow your business? This is the essential first step of understanding the baseline capabilities needed by your data foundation to deliver desired business outcomes. You then need to select the right cloud service provider that can deliver those capabilities based on both current and future objectives. Since not all aspects of a modern data foundation on the cloud can be developed at once, you will need to create a roadmap of which capabilities to develop immediately, and which others can be introduced over time. Aim for early wins while laying the groundwork for even greater value in the future.
  2. Make the move (migrate and modernize)
    There are two principal modernization approaches to consider—business function/use case or data product oriented. In either approach, we recommend using building automation and configuration tools for data ingestion and curation to accelerate your data transformation to cloud. AI/ML techniques can be leveraged to deal with the data engineering and data management challenges. It’s important to build data catalog and self-serve capabilities to democratize data and make it available to data ‘citizens’ for broader adoption.
  3. Operate and optimize
    Managing a modern cloud-based data foundation requires extensive standardization and automation. And because change is a constant, especially with the cloud, continuous optimization is also necessary. Moreover, ensuring data quality and trustworthiness, especially as the volume and diversity of data grows over time, is essential if data is to be fully used as valued capital across your enterprise. The cloud can be much more secure than a proprietary data center, as long as your security teams have the skills and tools specific to your cloud platforms.

Transforming your data foundation on the cloud is a multifaceted and multistage process. But it is one of the most worthwhile investments in your business that you can make. On the cloud, your data will work harder, your people will work smarter, and your business will benefit in ways never imagined before. Let’s talk about how we can help you accelerate your data foundation transformation to grow, innovate, and generate sustainable value for your business.

Shail Jain

Lead, Cloud First – Data & AI

Vishal Talwar

Lead – Cloud First, Sales, Solutions and Offerings

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