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Me vs Client – My Very first Client Meeting

By :
4 minutes read

Remember the feeling that comes when you think you wrote all the right answers in the exam but when you are handed the mark sheet and you barely passed?

I am sure most of us have gone through that. 

Well, I experienced almost the same feeling in my early days at Mantra Labs.

I had gotten assigned to a new project and was super duper grateful for this big responsibility. I dived right into it and wanted to do all of it on my own. I really thought I did everything perfectly. 

We know what is said about perfection, it is difficult to achieve. 

“In design, there is no perfection, there is just iteration.” (Design Gyaan 001 )

This is exactly what I said to myself when I failed. (Big words)

My client was this really big organization. I was working till the very last minute for the first meeting and thought to give it my best.

I joined the call with all my designs and just assumed all my work and efforts would be well appreciated.

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BIG FAIL

The 2 words that best fit the first client meeting. It was everything I had not anticipated. 

There were almost 15 top management people present from their end. 

(Nervous alert – the feeling you get when you see the exam paper and know nothing). 

I suddenly realized I was not clear on how to introduce my concept and present my design. All the keywords in my brain seemed to have gone on vacation at that moment. 

(That’s how I wrote my exam answers. Not just me, most of us did.) 

Also, it wasn’t all my unpreparedness and nervousness that led to the downfall of that meeting. They had a very direct approach and their feedback was also not very clear.

I came out of the call almost in tears and was extremely put off.

UPSET

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That was the emotion I felt after it and thought I wasn’t ready for this big transition. Felt even worse thinking that all my hard work was for nothing.

The next day I got back to work. With the help of my managers, we changed our strategy for approaching the project. We did a brainstorming (whiteboarding) session with not just the design team, but also with project managers, marketing, and the business team. We scrapped all of our old designs and came up with 3 unique design ideas and iterated on these. 

I aligned the design process to match the client’s requirements and my company’s standards and prepared for the next meeting.

BIG PROGRESS

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Changing the strategy played into our hands and we were more confident with our new versions of designs. I understood where I faulted earlier and prepared for my meeting before. I made a small set of key pointers that helped me drive the conversation and explain my designs easily. 

“Always make notes before the exam for revision” (Design Gyaan 002 )

In the next meeting, the clients realized I had understood the assignment and we had a more fruitful discussion. We showed them our designs with the new strategies implemented and they reciprocated positively. We cracked the design pitch meeting in the second call and had a path to move forward.

I walked out of the second meeting with a big smile on my face. (It was like getting straight A’s)

I did realize the places where I was lacking and needed to work on and since then I have started to maintain all these as part of my practice.

NOW FOR THE …..REPORT CARD

1. Agenda: Innovate and not just design

All the research, competitor study, beautiful elements, and trending UI styles didn’t work. While approaching a project it’s the innovation behind it that stands out and makes it work and not the research combined with beautiful design. New ideas don’t exist, until you come up with one.

2. Notebook: Writing in a notebook helps.

The old-fashioned pen-paper approach is key. Writing down keywords for your design that will help you in explaining your audience about it will make it easier for you. It helps you put all your thoughts in place and you won’t miss out on the important things you need to convey. Also, don’t make the mistake of doing this in your Notes app on the laptop because mostly you will be sharing your screen. 

3. Pointers: Prioritise Goals

Make a list of all the tasks in your bucket and choose the top 5 tasks to complete. For that particular day, you can prioritize 3 tasks, one simple, one major requirement, and one that interests you. Do the simple one first and this will help you check one off and give you a sense of accomplishment. Then take up the major one and then the last one. You would have completed a major task and also be happy at the end of the day with doing the one that interests you.

4. Subject knowledge: Know your tool properly to be the best!

I learnt greatly more about the tool Adobe XD and its features while working on the design pitch. I learned how to organize my files to make the workflow efficient. Also, I learned more about how to present my design screens. 

5. Co-curricular:

Don’t forget to charge your laptop before any important call and to keep the charger handy. Also, close all your Google chrome tabs, you don’t want them to peak at your mess. Another interesting thing is to try to document your design progress. I maintained this in the Miro board application. This has helped me to view my progression from the first design and see how much I have improved.

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PASSED…

About the Author:

Diya is an architect turned UI/UX Designer, currently working at Mantra Labs. She values designing experiences for both physical and digital spaces.

Want to know more about designing?

Read our blog: Designing for Web 3.0

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

By :

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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