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From Nerves to Sucess: My First Client Presentation

Greetings to all my inquisitive designers and dedicated blog readers. Within this post, I’m excited to recount my first client presentation. It’s quite an amusing tale if I do say so myself.

In March, I started working on a project as a junior UI/UX designer, collaborating closely with a senior designer. Upon receiving the Business Requirements Document (BRD) from the client, its contents initially left me confused. Nonetheless, I diligently commenced work on the project following the provided instructions.

Initially, my focus was on the agent portal. However, my senior designer later reassigned me to the back office admin portal, which comprises four distinct modules. I initiated my work on the first module, specifically the back office admin segment. The senior designer informed me that, in this compact module, we would only be incorporating 2 to 3 menus on the dashboard. 

When I initially began the development of the back office module, I delved into the Business Requirements Document (BRD) and discovered a multitude of menus outlined within it. I meticulously organized these menus and set to work on them. Approximately 8 days into the project, my senior inquired, “Madhuri, how long will it take you to wrap up this module? It appears to have taken quite some time.” At that juncture, I had successfully crafted a comprehensive dashboard along with three distinct menus, complete with their respective detailed screens. When I presented my progress to him, he expressed his astonishment, remarking, “I never envisioned this module to be of such substantial scale!” What compounded the challenge was the lack of available references, with the module encompassing roughly 150 to 160 screens in total. Despite facing numerous hurdles, I finally managed to successfully complete the “Back Office Admin” module within a span of 25 days. “Back Office Admin” The name itself does have a somewhat horror ring to it for me at the time. But all jesting aside, I was genuinely relieved to have accomplished this feat.

As the moment approached for the client presentation of my module, uncertainty loomed over thinking about who would take the reins and stand before the client to present the design. I had assumed that my senior would take up this role. I vividly recall the day when my senior informed me, “Hey, you’ve worked on this module, and it’s your responsibility to make the presentation.” At that very instant, I couldn’t help but think, “Oh god, help me through this.” My hands trembled, and I felt far from prepared for the upcoming presentation.

In the wake of this revelation, I began my preparations for the design demonstration. Eventually, the moment arrived, and my senior asked me to start the presentation. Drawing in a deep breath, I began.

Initially, I presented a complete overview of the entire module with details regarding the available menus, our overarching design approach, and the step-by-step progression of our design methodology. Within this context, I explained our primary objectives and how we successfully attained them. Then I gave them a walkthrough of the design and each screen in detail with a comprehensive description of the specific module, in line with the client’s explicit requirement for a detailed design explanation.

During my initial demonstration, I showcased a grand total of 160 slides, an enriching experience in itself. Throughout the presentation, the client posed several inquiries, to which I lent keen attention and replied with utmost politeness and clarity.

Following the presentation, I gleaned valuable insights. The presentation itself has two distinct modes: the first is the online presentation, and the second, is the offline presentation. Mine was online, giving me certain advantages. Nevertheless, it is crucial to bear this point in mind to ensure the success of your own presentations.

1. Embrace Self-Assuredness: Confidence is a constant factor, even when you occasionally misspeak. Regardless of what you express, do so with unwavering confidence.

2. Begin with Confidence: As you commence your demo or presentation, initiate with a warm greeting and introduce yourself.

3. Harness the Power of Your Voice: During an online presentation, your voice takes center stage as your unique identity. Therefore, it’s crucial to employ a clear and composed tone, maintain pauses between sentences, and avoid speaking too rapidly. Allow your audience ample time to ask questions if they have any.

4. Clarify Screen Details: During any type of demo or presentation, provide a comprehensive overview of the specific screen, covering everything from the header to the footer.

5. Center Your Core Message: Concentrate on your primary message—why you’re introducing this particular design and the underlying process behind it.

6. Client Q&A Etiquette: During design discussions, clients often pose numerous questions. It’s essential to attentively listen to their queries and respond politely. If you’re unsure about a particular point, kindly express, “I’m not certain about this at the moment, but I’ll certainly follow up with you to provide a thorough response.”

These are the key points you should embrace to excel in your presentation. With these thoughts in mind, I’m signing off and will be back soon with a new, engaging blog post.

About the Author: Madhuri Vinchurkar is a passionate UI/UX designer working at Mantra Labs. With a keen eye for creating seamless and visually captivating digital experiences, she has honed her skills in crafting user-centered designs that not only look great but also offer intuitive interactions.

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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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