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How To Get Design Inspiration?

3 minutes read

Did the egg come first or the chicken, it makes you go round and round in circles. That’s the feeling you get when you start designing something new. 

Inspiration can come from everywhere, especially if you are a designer. To create your finest design, you must get into the nitty-gritty of everything. I began my journey as an interior designer which gave me an edge when I transitioned to UI/UX Design. When we start working on projects, the first thing we do is construct a mood board. But for me, the challenging part was deciding what was good or bad and what worked in the real world.

So, I began with extensive thought about the problem at hand, followed by conceptual visualizations of all possible solutions. It seems intimidating, but it worked for me. I’d later project all those things from my imagination onto the screen. This process didn’t always produce viable solutions, which was a major problem to cope with. After all, what’s the purpose of having a good design if it doesn’t work? So, I merged this approach with swiping and gathering inspirations that I loved by favoriting my way through multiple sites to create a perfect Mood Board.

Next, was putting the mood board into action and creating something unique.

And, in my opinion, this is the most basic process chosen by designers.

Then I joined Mantra Labs, which was intimidating since I went from being a loner to being a loner in a group. From analysing my own ideas to working with a group of amazing designers who don’t hold back on their criticisms. (They never stop talking :P.) It was also intriguing to observe every designer as each one had a different approach to getting design inspiration and it was reassuring to know that there is no right or wrong way. It can come at any moment, anywhere, and in any form; all you have to do is enjoy the experience because it’s a Pandora’s Box, where you get lost and then come out with something amazing you weren’t expecting to find.

I try browsing design websites and talking to others about their work to get insights. And I can say that I’m definitely getting better as a designer day by day- the key is to stay curious and explore new things.

Here is a compilation of some wonderful exercises I intend to try on my projects as soon as I have the opportunity.

Create a lot of opinions and then pick the best one.

Creating a lot of different variations for one project and then critiquing it to bring it down to one which you like. 

Take a counter intuitive path

Going crazy with the thought process, and breaking the stigma of keeping basic, and crazy is fun, and it might surprise you.

Inspiration outside

This includes everything we have done or do on a regular basis, such as opening a bottle or flicking through the pages of a book.

Try apps in your category

Like for an education app, look for inspiration on social media, in travel, or something else.

Visit design websites

As you examine different types of designs, it inspires you and gives you a bank of ideas; all you have to do now is learn how to use those ideas on time and on the right project.

Ask a friend

This is one of the best ways to get better insights and diverse perspectives which can be very helpful.

Conclusion:

The exercises listed above may or may not work for you because there is no perfect science to getting creative inspiration.  Lorinda Mamo once stated, “Every great design begins with an even better story.” So, in order to find design inspiration, you must first find the story. Keep experimenting with different ways; you never know what might work for you.

About the author: Neha is a designer at heart who walks and talks too fast and is always willing to try new things, whether in business or in life.

Want to know more about designing?

Read our blog: 5 Things to Consider while Designing an App for Gen Z’s

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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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