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Iteration Leads To Powerful Results in Design.

“You can only make it once but you can make it better as many times as you need”

Clients rarely arrive at a design firm with a detailed project roadmap in hand. Instead, they have a hazy idea of what they require – make it pop, bring a wow factor, make it look good, and so on. In such cases, the designer’s main challenge is to get into the clients’ heads and create things exactly how they want the product to look, even if the clients themselves lack understanding.

The best way to ensure your design is a perfect fit is to work in iterations. This allows us to create a solution that satisfies the client and meets the needs of the customer.

Iteration Leads To Powerful Results in Design

Iteration, the most fundamental concept in design

In its most basic form, iteration is simply a series of steps that you repeat, tweaking and improving your product each time. With every repetition, iteration aims to move a little bit closer to the optimal situation. As designers, we are always looking to improve on the current design approach and this is where an iterative design process comes in handy.

​​You can think of the iterative design process as a continuous cycle of prototyping, testing, and making adjustments and refinements – it is an ongoing, incremental process leading to the best possible outcome.

The 1997 version of Apple.com
The 1997 version of Apple.com
The 2022 version of Apple.com
The 2022 version of Apple.com

It’s fascinating to observe how the product gradually changed the appearance of its own homepage, going from its ugly beginnings to its current minimalism to align with the current design trends and in response to user feedback.

The do’s and don’ts of design Iteration

  1. Do: Fail Faster
    Embrace trial and error to learn what not to do even when you miss the mark by adopting a “fail faster” mentality. Since failure is unavoidable, it is best to deal with it as soon as possible while still taking note of what can be learned.
  1. Do: Be Flexible
    Design methodologies still allow for some flexibility even though they have strict guidelines to help us express our creative freedom without devoting too much time to each iteration. In the end, we must choose which opportunities to prioritize first, when to iterate or test more, and how many concurrent design iteration processes should be running at once.

    These choices are largely based on intuition and experience, utilizing any data and research that may be available.
  2. Do: Work Asynchronously
    Utilizing all resources (tools, teammates, etc.), complete tasks as quickly as possible by allowing other designers to work on unrelated aspects of the product in parallel and developers to start putting validated solutions into practice. By doing both of these, product turnaround times will be drastically reduced.
  1. Do: Collaborate and Listen
    Which issue ought to be resolved? What version is the best? Is the testable prototype ready? What do all of these comments mean? We are confident in our ability to respond to these questions because of the unique expertise and new perspective that our teamwork partners have to offer.
Iteration in Design
  1. Don’t: Try to Solve Everything
    Avoid attempting to solve new problems once the issue we’re solving during the design iteration process has been selected. Even though it’s common to find areas that can be improved (during testing or through observation), make a note of them since they might make excellent starting points for subsequent iterations.

    We cannot measure the effect that design iterations are having on key metrics if we allow scope creep to occur.

Benefits of Iteration in Design

  1. It Saves Resources
    Because iterative design processes frequently give us user feedback (or stakeholder feedback, at the very least), which drives us forward at a steady pace, they almost always save the most time.

    Positive feedback can help us know when we’re heading in the right direction, and negative feedback can help us know when we’re heading in the wrong direction, so we’re always moving forward and never really wasting any precious time.

    Without any feedback, we run the risk of racing to the finish line only to fall short, wasting a lot of time and bandwidth. Design iteration is also the most economical choice because time is money.
  1. It Facilitates Collaboration
    Healthy collaboration is facilitated by an iterative design process because it gives stakeholders the chance to provide feedback and even share their own ideas. This gives us information that we wouldn’t have learned on our own because we can only see things from our own point of view.
  1. It Addresses Real User Needs
    Designers have a tendency to work alone if they don’t follow a methodical iteration process (especially one that includes collaboration). Being siloed makes us overly introspective, which causes us to jump to conclusions and engage in counterproductive perfectionist tendencies.

    But using an iterative design process makes sure we remain focused on user needs and make choices based on their input. Additionally, prioritizing the next best design improvement method rather than concentrating on haphazard ones helps us.
  1. Facilitates Regular Updates
    Instead of just dumping the end result on stakeholders and keeping them in the dark until then, an iterative design process allows us to regularly update them on the status of the project.

    It means that developers can start even while the design is still in progress, which is especially advantageous for developers.

In Conclusion

Designers can quickly create and test ideas thanks to the iterative design. Those that show promise can be quickly iterated until they take enough shape to be developed, while those that don’t show promise can be abandoned right away.

The 90’s version of arngren.com
The 90’s version of arngren.com

Here’s an example of what happens when we don’t iterate – this 90s website is still around.

So do it, then do it again!

About the Author: Unnathi is a UI/UX designer, currently working at Mantra Labs. She is passionate about research and has expertise in building digital systems that provide engaging experiences. 

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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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