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Building a layered approach with AI

We have made significant strides in the evolution of design. It is crucial for us to recognize that disruptive technologies have always been the catalyst for change, shaping the way we design and communicate. In an era before the printing press, people painstakingly wrote books by hand. Similarly, the emergence of mobile phones, computers, and the internet has changed everything.

Embracing these changes and adapting to newer technologies is essential for staying relevant in the field of design. There are designers who use Adobe Dreamweaver and Photoshop to create website UI. It feels like a different era since the introduction of more recent software such as Figma, and Adobe XD. However, the core principle remains the same: staying pertinent requires adaptability as the times change.

While there’s an ongoing discussion about AI potentially replacing design jobs, my personal perspective is that we should not fear it but rather leverage it to our advantage since AI is an execution tool. AI is poised to simplify our lives. When you consider the broader picture, your value as a designer in the upcoming years will be determined by your ability to synthesize innovative ideas to solve problems. Let me elaborate on how, as designers, we must strengthen our problem-solving muscles.

How the layered approach works:

Tools like Midjourney are designed for image-specific tasks, enabling the creation of visually appealing images. Chat GPT on the other hand is a text-based AI model, although now with the incorporation of DALL-E inside Chat GPT, it gives image outputs too.

I want you to imagine for a second, what if you used Chat GPT to create nuanced prompts for Midjourney.

This is layering software on top of another software to receive your desired output. For this to work, you have to first train your Chat GPT agent on Midjourney’s functionality and then prompt using a basic framework.

The framework works like this:

First, you inform Chat GPT about your desired role or objective and define your approach or task. For example, ‘I am a skilled graphic designer who designs illustrations for top brands in India. You need to provide me with ideas for designing a coffee shop logo, along with some links for inspiration.’

Then, you specify your timeframe or any restrictions. For instance, ‘I can’t spend more than 30 minutes looking at inspiration, so please only provide the best ones you find that you believe would be most helpful.’

Finally, after training your agent, request Chat GPT to provide a prompt for Midjourney.

The key thing to remember while implementing this approach is to churn out a nuanced prompt that will serve as a base for your creative design process later. That’s it—two layers of software, and the pivotal layer comes into play once you’ve received the output: your creativity. You make AI as Robin to your Batman. Consider this as no longer needing to begin from a starting point. With AI tools such as these, people who have the potential to think clearly to formulate ideas would flourish. I encourage you to use these AI tools in your daily life to learn and become the best in your respective fields. 

There are many more tools that have come into the market such as for UI, a single line prompt can generate a whole app’s UI screens with tools like Uizard. Builder.io is a tool that uses AI to convert Figma design files into code. Recent developments also suggest that AI can help with user research for large organizations, potentially marking a paradigm shift in the UX industry, as these AI models have been trained on human behaviors, this has been put forth by fantasy.co. There is so much more to come, the least we can do is be prepared for it. 

I hope you had a good read and this article gave you a sense of what is waiting in our future.

About the Author: 

Shivani Shukla is a Senior UI & UX designer at Mantra Labs. Updating her knowledge and staying up to date with the current trends has always been her priority.

Further Readings:

Design and Technology Fusion Shaping the Future of Innovation

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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