11/14/2023 | Press release | Distributed by Public on 11/14/2023 08:01
By:Sujith Gopalakrishnan, Managing Principal Architecture , Snowflake Business Unit
Today, most organizations with legacy data systems are focusing on modernizing their platforms, aiming to cultivate a data-driven culture, capitalize on their data assets, and embark on substantial data-focused initiatives. Building a new data platform from scratch using Snowflake is essential to enable innovative business capabilities, enhance customer satisfaction, gain insights, and unlock business value.
When organizations embark on building a data cloud platform using Snowflake, greenfield initiatives are usually enabled through either the 'lift and shift' approach or a process of reengineering, often referred to as 're-architecting.'
Reengineering initiatives could entail significant risks and challenges if not managed correctly, given the complexity of technology decisions, people, and processes involved.
Reengineering initiatives often entail designing and building various data functionalities within and around Snowflake from the ground up. This involves finalizing the Snowflake architecture, creating a data model, integrating new data sources, building data transformation pipelines, ensuring data quality, governance, security, observability, DataOps, and enabling new consumption patterns, including BI reporting, data science, or building data apps. A lack of expertise in these multiple areas or improper prioritization of activities can lead to adverse effects.
The absence of a structured approach to navigating the nuances often results in delays, failures, and a lack of confidence in the initiative. Furthermore, the lack of methodology, frameworks, and accelerators can lead to a loss of focus and delays in the overall initiative.
Designing and building a new data platform involves multiple aspects that can potentially delay the program. Factors contributing to delays include the readiness of the business, the time required to finalize use cases and requirements, coordination across multiple teams, experimentation and evaluation of various solutions and tools, poor data quality, lack of ownership, and challenges in planning and prioritizing various modules. Without a structured approach, there may be delays in developing a Minimum Viable Product (MVP) and bringing the final data product to market.
While accounting for new cloud infrastructure and licensing costs is implicit in a greenfield exercise, the lack of optimization on these services, long trial periods, failures, and delays in solution finalization can impact project timelines. The factors mentioned earlier that lead to delays in time-to-market can also result in extended personnel costs, further adding to the continued operational costs of legacy data platforms.
Adopt a structured approach for the reengineering exercise, drawing learnings from past reengineering experiences as guiding principles. The methodology framework should address various aspects, including identifying phases/modules, defining activities, deliverables, prioritizing modules, handling dependencies, establishing ownership, and setting completion dates. A strong project governance, design review, risk management, and a fitting execution-delivery model would enable guardrails for the overall initiative.
Prioritize business use cases and build MVPs to help stakeholders understand what to expect from the new data platform and showcase quick wins. Incorporating changes as per their feedback goes a long way in winning business trust and gaining more support.
Engaging domain and technology experts becomes imperative with processes, technology, and business considerations in place. Experts who have previously dealt with such initiatives and have experience handling these functionalities would bring significant value to the team. They can guide avoiding pitfalls, offer insights on approaches, and assist the team in getting things right the first time.
Utilize accelerators, frameworks, and methodologies designed for the Snowflake reengineering exercise to avoid pitfalls, expedite the overall implementation, and enable results. Explore new ways to empower your business and consider Snowflake's capabilities beyond the immediate data processing needs. Use native Snowflake services wherever feasible, from streaming requirements to building applications or AI. Identify the right native Snowflake services to enhance the user experience.
A Snowflake greenfield reengineering program based on the insights and expertise acquired from extensive Snowflake reengineering initiatives is a sure way to avoid hiccups or chances of failure. Such a reengineering program should comprise methodology, framework, jump-start kit, and accelerators, all geared toward expediting the greenfield reengineering program.
Ensuring Snowflake greenfield reengineering initiatives are first-time-right, delivered on time, and cost-optimized requires:
LTIMindtree's Snowflake Reengineering Program offers a proven methodology, framework, jump-start kit, and accelerators, backed by extensive expertise. The program is aimed at expediting the greenfield reengineering to build the Snowflake Data Platform right the first time.
Managing Principal Architecture , Snowflake Business Unit
Sujith is a Chief Architect with close to two decades of experience in architecting and building data and analytical applications. He is a TOGAF Certified Enterprise Architect with deep expertise in the areas of data governance, consulting, and implementing data platforms on AWS and Azure. He is also a Snowflake SnowPro Advanced architect.
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