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Why Storytelling in Design is Important?

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4 minute read

You have all heard of storytelling, everyone in the design community and social media talks about it and you wonder – ‘What does it mean?’. 

Honestly even I was in the same exact confusion in the beginning. I dived into the internet and went through articles and videos to understand what all these people were saying.

Let me break down what my understanding of storytelling is.

WHAT???

Did you experience that feeling you get when you go into a meeting all excited to present your designs with josh, and get nothing?

This usually happens when the people on the other side have not really understood your design. It’s because you spoke your language and not the language they are used to. 

(Just imagine a developer coming up to you and talking in their programming slang. You’ll be clueless and confused. Things will fly off your head.)

We need to talk in a  language that our audience will understand. Storytelling is nothing but the art of conveying your message in a way everyone can understand. It is something that is a part of our daily lives and culture.

We need to build an emotional connection with our designs/ideas so our audience will respond to it in a more positive manner. People connect with emotions and for that, we need to establish trust by talking in a way they can understand.

Just like the movies, your design should tell a good story. Before we start designing we need to set a plot for our designs, create characters and start making things relatable to the real world.

WHY???

A design is easy to do but convincing people of an idea is tough (No offense). This is where storytelling comes into action. Most of us usually start designing without understanding the brand, the audience, or the idea. We are always running behind in making a good design rather than what will work for the product in the real world. 

Most designers make the mistake of making the design perfect and supporting it with tons of research. But the truth is that people can’t connect with that type of design emotionally. (The key is to bring your design to life)

We need to put the idea out in the world. The idea is always more powerful and it gets a whole new dimension when you start sharing it. People will have more ideas to the original core idea and it will keep growing. 

Let’s discuss this in an easier way. (By storytelling) 

The first thing is to start by thinking about your idea as the main plot line. Everything will revolve around this now. Like in DDLJ – the plot was that Raj & Simran loved each other but her family had already accepted another proposal for her.

Next, you start by building your characters in the design. Define your main hero and the supporting roles. Basically, find your Raj and Simran within the design and highlight them. 

Now like in every movie, we need a bad guy. Your problem statement can be the villain.

Like all good blockbuster movies, your story needs to have some masala and drama. 

(Add some dramatic train scenes) 

​​

Then start thinking of all the content as the dialogues of the movie and the graphics (imagery and illustrations) as the songs of the movie. We need to create a good balance between the songs and the dialogues.

Your subheadings and body text can be the supporting roles that make the hero stand out.

Also like all the action scenes of the movie, the CTA’s need to be there in the right place at the right time. Our users glance over the screens and in a very small time, we need to highlight our action items.

(Timing is everything)

First you yourself need to understand the story plot and characters in depth, then you can connect your audience easily to it. Audiences can forget the hero’s name but the story is what they will remember and talk about.

I have broken down the hero section of a popular insurance website into simple elements of a story. We can use this same process to talk about the entire website and further the complete concept. We need to break down the bigger element into smaller elements that should convey the same idea.

HOW???

Your story needs to be rooted, it needs a strong base. You’re selling your story, and your idea to everyone and not just a website, or an app.

So there are 2 ways to do this – like an art form or with some masala.

The first one you should take on when you have an experimental design, for example when you reimagine any traditional approach. (Also when you have a lot of time and budget).

The other way to go about it is with some masala and creating a dramatic pitch. Try to build a story that places your product/idea in a real-life setting. 

Next thing is to always practice with an audience before you go in for the final pitch. Practice your story a couple of times with your friends, family, and professional peers, and keep iterating the story by seeing their expressions, moods, and responses. Always practice with a small group. (Practice makes a man perfect😊)

You need to really understand your audience and connect with their emotions. On the first attempt you might fail but keep practicing and each time you’ll add things or remove something and you’ll get better at storytelling.  

(Also you aren’t chocolate, you can’t make everyone happy…Just Accept That!)

Just always remember your idea is the most important aspect of your story. Pick what you need to be the hero of the design, make things easily relatable and that will help your design to be memorable.

….. And then the final pitch might just be a BLOCKBUSTER!!

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: How To Get Design Inspiration?

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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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