At Oliva Advisory, our approach to advanced analytics projects is rooted in people and the diverse team of experts who collaborate on these initiatives. We emphasize well established processes, bestpractices, and protocols to mitigate risks and ensure that we consistently deliver secure and reliable data for consumption.
1. Evaluate the choice of No-Code Tools with Business Needs
No-code tools offer a low barrier to entryand can enhance collaboration when changes can be tracked. It's crucial to choose a tool that aligns with your data team's skills, culture, and, most importantly, your business requirements. This choice not only boosts your team's productivity but also prevents you from becoming a bottleneck in theprocess.
2. Master the Art of Deciding to Buy or Build
Leveraging existing frameworks or readily available tools can significantly expedite project delivery. The key is toidentify the most efficient solutions for your specific business challenges.
In one of our projects, our data teaminitially leaned towards building everything in-house, investing substantial time and resources. However, they still grappled with the challenge of loading data from diverse sources. That's when we realized that adopting a hybrid SaaS data integration platform was a more effective route. As data engineers, it'svital to discern when to leverage existing tools and carefully weigh the pros andcons of in-house development. Consider factors such as cost, control requirements, and integration with existing systems.
3. Embrace the Power of Open Source for Agility
Some of us tend to stick to the technology and methods we're most familiar with. However, with the rapid evolution of the modern data landscape, it's imperative to tap into the power of open-sourcedata tools at your disposal. Staying current with the latest developments inthe ecosystem can accelerate project delivery by allowing you to build on the collective wisdom of the open-source community. Nevertheless, be vigilant about security vulnerabilities, as they are increasingly relevant to enterprise adoption.
4. Avoid Over-Attachment to a Single Tool
To excel in the dynamic field of data engineering, it's essential to understand the strengths and limitations of various tools and programming languages. Relying exclusively on one tool forall tasks can be counter productive. Assess tool suitability based on thespecific requirements of each project, optimizing processing costs where feasible.
5. Invest Time in Continuous Learning
Bolstering your knowledge of diverse software and data analytics tools positions you as a bridge between different disciplines. This proficiency streamlines collaboration and accelerates development, ultimately leading to actionable insights.
6. Share Best Practices and Standardize for Efficiency
Effective data practitioners aren't justexperts in their craft; they are also passionate about sharing their knowledge andbest practices. Standardizing code into frameworks and templates cansignificantly streamline processes.
7. Adopt a Product Mindset
Treating data models as Application Programming Interfaces (APIs) for downstream consumers is a game-changer. Actively seeking feedback from these consumers and documenting your data models ensures data accuracy and utility.
8. Strike the Right Balance Between Short-Term and Long-Term Goals
While designing data pipeliness, it's crucial to focus on their impact on critical business functions and anticipate future changes. Avoid getting too engrossed in immediate tasks and consider factors like idempotency, stateful vs. stateless processing, and batch vs. streaming solutions to prevent production issues.
