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Understanding the Why’s in designing

We have been ingrained with a lot of rules and regulations since our childhood. And out of curiosity, whenever we asked why, the answer was- some traditions and customs must be followed…😤

And this didn’t end there, even in UI/UX too, the same is followed even today.

So many rules and no clear explanation of  Why.❓

In this blog, we’ll try to understand the reasons why certain guidelines must be followed when designing. For example, why we shouldn’t use red background on blue and vice-versa? Why button should have a certain touch area? And so on.
To begin with, the majority of the rules related to the design are actually connected with how the human body is structured or as we call it, Designed. Not clear? We’ll go one by one discussing the reasons behind most widely used 6 rules. 

1. Why is Red font on a blue background is big NO ❌?


The choice of font color and the background color is usually based on factors such as contrast, legibility, and aesthetic appeal. However, it is important to ensure that the combination of colors provides good contrast, making the text easy to read. But why is it hard to read?

This occurs because of Chromostereopsis, which is a visual illusion that happens when certain colors are placed next to each other, making it unnecessarily difficult to stay focused on both colors. The illusion is due to the stimulating of different areas within the eye, causing some light rays to coincide with others in the eye. Because of this, it becomes difficult for the human eye to focus on them.

2. Why Recognition is better than recall?

Don’t let users remember!
As a designer, we should always try to reduce the user’s memory load by keeping objects, actions, and options visible. The user shouldn’t have to recall details from one section of the dialogue to the next.
Why?
Because of short-term memory. 

The majority of the information in short-term memory will be stored only for about 20 to 30 seconds, or even less, and can last for up to just a minute.


Most information decays quickly, unless we rehearse it. We remember 7 things, +/- 2 in short-term memory. Recent research shows a decrease to 4 things +/- 1.
That’s how our brain is designed. So it becomes hard for the users to remember information, it’s always best to recognize the information than recalling.

Oops! I forgot which account number I selected 🤯😶‍🌫️


3. Why Larger Button size (touch area) must be used? 

​​The button size should not be less than 42 pixels(not a hard and fast rule). This is not because of visual appeal, balance, etc., but because of the thumb/ finger touch area. The smaller the size, difficult it becomes for the user to perform actions using the button or icons in that case. And larger items are easy to see.


4. Why too many Fixations isn’t good for the user? 

The brain assembles a continuous visual experience from a sequence of fixations and saccades, making vision continuous. Fixation is the location at which our eyes fixate and a saccade is a fast, simultaneous movement of both eyes between two or more phases of fixation in the same direction.
Things that attract the scan are bright colors, big numbers, people, etc.
Too many fixations make it difficult to scan through the design, it recreates too much cognitive load. So we have to reduce eye fluctuation to keep the focus and to get the work done easily and efficiently.


5. Why is the Floating icon always on the right end?


Ever wonder why floating icons are on the right end of the phone? This is because of the way people naturally read and scan content. The floating icon concept is connected with how our motors (hands) and eyes function. In many cultures, people read and scan content from left to right. This means that their eyes are more likely to start on the left side of the screen and move toward the right. And also most Indians are right-handed and the right end is the easiest area to be accessed while using the phone. Anywhere on the top becomes difficult to access.


6. Why success icon is green and the alert red?


The use of green and red colors to represent success and alert respectively is commonly used in user interface design. This is based on the psychological associations that people tend to have with these colors. Green is often associated with positive emotions such as growth, harmony, and success, while red is associated with danger, warning, and urgency.

And in the real world, the traffic signal-go is green, and the stop is red. Using the same color for success and alert becomes easy to associate with less or no cognitive load.

Wrapping Up:

These are just a few whys and they are many more. Learning the why behind these rules may help in making work more meaningful and becoming a good designer. 

Hope you found this article helpful. 

Want to know more about designing?

Read our blog: Iteration Leads to powerful results in Design

About the Author: 

Charishma is a UI/UX designer at Mantra Labs, who believes in creating experiences that matter. She is an MBA turned designer who fell in love with the process of how design is made.

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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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