Insights

Transforming your operations with big data on the cloud while controlling costs

Competing with data native organizations while maintaining ROI

Niranjan Ramsunder, Chief Technology Officer, UST US

Established organizations are facing fierce competition from digitally native and agile rivals. Many are burdened by the technical debt of legacy systems, which slows down their ability to innovate and brings high costs when transitioning to elastic environments like the cloud, often affecting ROI. Additionally, these challenges lead to increased expenses in effectively utilizing the vast and diverse data available. At the same time, business efficiency suffers due to data being siloed across various organizational structures and acquisitions.

This blog delves into how organizations can leverage big data to maximize ROI as they increasingly transition to the cloud. While the move to the cloud has pros and cons, it's become a near-essential step for many.

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The costly reality of data initiatives

Many enterprises collect large volumes of data across multiple systems but fail to utilize this data optimally. According to a recent survey, 86% of organizations report that their technology is inadequate or outdated, significantly hindering their ability to use their data effectively. This issue, compounded by a need for more scale of skilled employees who can engineer and analyze the data, leads to significant inefficiencies and wasted resources. Integrating data to derive actionable insights is complex and costly, and projects often cost hundreds of millions. Challenges such as synchronization across time zones and differing data definitions further complicate this process.

Delays in realizing value are common due to the numerous technology choices and the scarcity of high-quality talent. Many vendors offer solutions that require substantial upfront investments in technologies, infrastructure, and skilled resources. Additionally, data collection is often driven by "greed" rather than need, resulting in expensive, underutilized data repositories.

To address these challenges, it's essential to rethink how data initiatives are structured and executed. By implementing a well-organized data strategy, enterprises can begin to unlock the true value of their data, driving innovation and efficiency. This process involves:

• Organizing data initiatives to maximize value

Challenges such as data integration complexity, high costs, talent scarcity, and inefficient technology choices often hinder success. We focus on a strategic, use-case-driven approach to overcome these hurdles and transform organizations into data natives.

This methodology identifies high-value use cases by analyzing data, studying industry trends, and collaborating with business stakeholders, which is essential to success. It also develops tailored data strategies and technology roadmaps by prioritizing use cases based on potential impact and cost. Organizations can enhance their existing list of use cases by identifying additional opportunities. Delivering a data strategy with a prioritized list of use cases, business value assessments, implementation costs, and a technology roadmap will help leverage data effectively and cost-efficiently for actionable insights and sustained growth.

• Leveraging data to drive innovation

Businesses can stay ahead of the competition by exploring data-driven marketing strategies, optimizing data-driven operations, and discovering new opportunities for data monetization. Examples of successful data-driven innovations can inspire other organizations to explore similar pathways, ultimately leading to greater efficiency and profitability.
Other areas, like stewardship and data governance, managing privacy, and other regulatory needs through masking, tokenization, and encryption, are inherent to this process and deserve a separate, focused discussion.

• Accelerating cloud adoption

Once we agree with all the key, relevant stakeholders on what needs to be done and how success will be measured, we look at minimizing the cost of moving to the cloud. For example, we are finding opportunities to use GenAI to automate migration from older query language constructs to Spark and PySpark-type massively parallel processing technologies and to move to open-source table formats like Iceberg. We also see significant opportunities to automate and optimize testing and QE efforts to minimize friction with business users

• Optimizing the execution of data programs

As these use cases become realized, we have found huge opportunities to maximize the benefits of separating compute and storage and making it even more fine-grained.

1. One of the initial reasons organizations moved to the cloud was to avoid the need to scale compute and storage together, which was the inherent need for older data warehousing technologies. We see further opportunities to optimize costs in the cloud by:
2. Separating compute clusters and thus their costs further into machine learning loads and distributed querying clusters (e.g., the use of technologies like Trino and Spark are some examples)
3. Selectively moving workloads to open-source, self-managed constructs also helps with managing costs better
4. Managing DataOps with automation to reduce the costs of ongoing operational support with application telemetry-based self-healing, driving adoption of self-service, and reducing the skill levels needed to manage support are all potential areas of value.

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Conclusion: A data-driven future

Implementing a strategic approach to data initiatives requires careful planning, prioritization, and alignment between business objectives and data capabilities. Organizations can maximize the value derived from their data by identifying and prioritizing use cases, integrating various data sources, and employing performance tuning and cost optimization techniques. Emphasizing the importance of high-quality talent, the right technology choices and a structured methodology ensures that data initiatives meet regulatory requirements and drive competitive advantage. This comprehensive approach helps enterprises transform into data natives.