Summer reflections: 6 lessons from 1 year Snowflake practice

About one year ago, we started our Snowflake practice and helped our customers modernising their data architecture to get more value out of it. In this article, we share our learnings from one year of conversations with data architects and CIOs on this topic.

#1: Data has found its way into the boardroom

It’s fair to say that a majority of the companies in Belgium realize the importance of data as a strategic and even business-critical asset. Boards and management teams are increasingly putting significant pressure on their organizations to generate more value from data. One CIO (with also a CDO role) from an important manufacturing company reported to us that she received a request after a board meeting to investigate how her team could generate data products allowing to sell some of their data to third parties and generate revenue to co-finance planned investments in a data platform and accompagnying tools.

#2: Obstacles on the road

Under internal or industry peer pressure, companies start proactively launching initiatives to get much more value out of the data and become truly data-driven. But at the same time, they often appear not to be ready to conduct successful data innovations. Organizational, technical and cultural obstacles are often blocking the golden bridge towards the promised land where data flourishes as assets. Departmental silos and processes, a lack of in-house skills and trust about what will happen with the shared data often prevents efficient collaboration and innovation with data.

#3: The need for a new data architecture

We often noticed outdated IT and data landscapes, and hence the lack of architectural robustness to handle even basic data tasks. Platform modernization is a crucial first step to allow for handling massive data volumes, addressing unstructured or streamed data, and providing sufficient computational power to run the data models and deliver data products. The shift to a modern data infrastructure is not just about keeping up with technological advancements, it's also about getting the basics right by putting in place a scalable and future-proof data foundation that supports data-driven decisions and innovation.

#4: Choice complexity and decision stress

Companies that did realize they needed a new data architecture, often struggled to decide about the right choice. It can indeed be an overwhelming task, given the the plethora of technologies available in the market and the lack of experience in making such choices. The wide variety of available technology and the perceived risk of making wrong architectural decisions, can dramatically slow down the modernisation process. When advising our customers, we often emphasize that using technology without knowing first what you want to solve, is often a waste of time and money. What is the data strategy and vision for the next 2-3 years? What (future) business problems or (expected) opportunities need to be addressed? Having clear answers will help defining what a company needs from an architectural and technological perspective.

When it comes to technological choices, we see a tendency among the SMEs in Belgium to rely on one single vendor proposing a complete “one-stop” offering, covering almost all of their corporate IT-needs. While it may seem appealing to work with one and the same supplier for ERP, CRM, Cloud services, BI, Analytics and AI, a single-vendor strategy carries its own risks. Relying too heavily on a single provider can lock you in, limit flexibility and innovation, bear security risks and prevent benefitting from competitive, transparent and optimised prices and TCO. Setting up an independent central data platform to execute the data strategy will mitigate business risks and often help reducing operational costs in the long run.

#5: Data and access security are still underestimated

Despite the increasing awareness of data breaches and cyber threats, data and access security remain underestimated in many organizations. Investments and focus on security measures are often limited, leaving data vulnerable to unauthorized access and potential breaches. It's crucial for organizations to prioritize data security, not just as a compliance requirement but as a fundamental aspect of their data strategy. Implementing robust security protocols, regular audits, and employee training are essential steps in safeguarding data integrity and trust. From a technological perspective, being able to rely on a highly secured modern data platform will be a key factor in ensuring trust and allow for succesful data innovations.

As organizations generate and consume more data, the need for secure data sharing both internally and externally becomes increasingly important. This is where data platforms like Snowflake come into play, offering secure data sharing capabilities that facilitate collaboration while maintaining data security across the whole organization. By leveraging such technologies, companies can ensure that their data is accessible to the right stakeholders without compromising security.

#6: IT expenditure is still seen as a cost, not an investment

A common misconception in many organizations is viewing IT expenditure purely as a cost rather than an investment. This perspective can hinder progress and innovation. Investing in modern data infrastructure, cloud technologies, and advanced analytics should be seen as strategic moves that drive long-term value. Shifting the mindset from cost to investment is crucial for fostering a culture of innovation and growth fueled by data. Identifying interesting uses cases that require a limited initial investment while yielding value quickly, can drastically increase visibility and enthusiasm with regards to further investments in data infrastructure.

Our advice when considering modernizing your data architecture

Modernizing your data architecture can be an overwhelming and stressful journey. The following guidelines can help you starting with confidence.

#1: Start with a clear data strategy and vision

Before diving into technology choices, define a clear data strategy and vision. Identify your business objectives, (future) problems to solve and opportunities to seize. Think about inspiring “light house” organisations you can learn from and talk to. Make sure you build a compelling story to generate enthusiasm for and belief in your data vision.

#2: Invest in a robust, scalable cloud-native data platform

Invest in a scalable, future-proof cloud-native data platform that will help executing your data strategy and grow your data business over time. Implement robust security protocols to safeguard data integrity.

#3: Involve business stakeholders early and identify high-impact use cases

Engage business stakeholders from the outset of your data modernization journey. Early involvement ensures that the data strategy aligns with business needs and objectives, facilitating quicker buy-in and demonstrating tangible benefits. Prioritize use cases that can deliver quick wins and showcase the potential of data-driven initiatives.

#4: View IT expenditure as a strategic investment, not as a cost

Shift the perspective from viewing IT expenditure as a cost to seeing it as a strategic investment. Prepare a concrete, compelling story for c-suite, explaining what the business impact will be on their strategic indicators (reduced costs, higher revenue, new revenue streams, ..). Make sure you understand the pricing model of your technology vendors and prepare your negotiation strategy and tactics well beforehand.