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Tips for Designers To Tackle Layoffs 

The Layoff Season…

SAP Lab Laid off 300 employees. Ericsson to fire 8500 people. Headlines like these have become very common these days. Over 340 organizations have laid off more than 1.10 lakh people so far across the world. Not only humans but even robots were fired by Google recently. Meta, Amazon, Twitter, Zoom, and Microsoft are some of the major companies to join the layoff bandwagon. These big tech companies have large teams with multiple people with the same skill set. Over the past few weeks, dozens of them have frozen hiring and made significant cutbacks to eliminate redundant positions in departments like HR, marketing, and design. This has led to high competition for jobs in these areas and skill sets and a decrease in job opportunities.

The value of a designer is not always acknowledged, especially in these times of economic uncertainty where cost-cutting becomes a priority. This has also resulted in a cut in the budget for design departments, which leaves designers with fewer employment options. The assumption that design is a luxury rather than a necessity also hinders people from appreciating the worth of designers. This article discusses some tips for designers to tackle layoffs and AI-based solutions that can assist them to stay relevant in UI/UX design.

Automation and technology advancements have led to an increase in the use of design software and tools, making it possible for non-designers to create designs and perform tasks that human designers previously did. This has led to a decrease in the need for human designers in specific industries such as graphic design and website design, where the use of templates and pre-designed elements has become more prevalent.

Who benefits during the recession?

Contract or Freelance Designers

Companies may have less money to spend on design work, which can lead to fewer opportunities for designers. However, designers who are willing to work on a contract or freelance basis may still be able to find jobs, as companies may look to save money by hiring contractors or freelancers rather than full-time employees.

Additionally, designers with a diverse set of skills and the ability to adapt to changing market conditions may be more likely to find work during a recession.

User Experience Designers & Researchers 

UX designers and researchers may still be able to find work, as companies may be looking to improve their online presence and user experience to stay competitive which is why improving their digital products and services may become their key focus area. This can lead to an increase in demand for UX designers and researchers.

Business leaders may also cut costs by streamlining their products and services during these tough times, which can increase demand for user research to understand customer needs and preferences.

It’s crucial to remember that the job market is extremely dynamic and subject to rapid change during a recession, making it challenging to forecast how the demand for designers will change.

Essential Product Companies

Companies that build business-essential products may see an increase in demand, as companies and organizations look to cut costs by investing in more efficient and cost-effective products. These types of products may include items such as software, hardware, and equipment that help companies streamline operations and improve productivity. Additionally, companies that specialize in cost-cutting solutions, such as supply chain optimization or cost-saving consulting, may also see an uptick in business during a recession. It’s important to note that not all product-based companies will benefit during a recession, it will depend on the type of product they produce and the industry they operate in.

How to stay relevant in designing amidst this economic uncertainty?

# Focussing on Soft Skills

While technical expertise is crucial, employers don’t hire only people who possess those skills. The workforce continues to place a high priority on soft skills, also known as employability or transferable skills, which are frequently influenced more by personality than by education or training. The soft skills that may be most important in an uncertain job market for 2023 and beyond, include:

1. Critical thinking skills

Critical thinking involves being able to think creatively and strategically, identify problems, and come up with innovative solutions. These skills are highly transferable across different industries and roles, making them an asset for any team.

2. Communication skills

Effective communication helps in building positive relationships, resolving conflicts, and promoting understanding. Employers value individuals with strong communication skills as they can work effectively with others, and can represent the company positively. It involves both verbal and written communication, and the ability to listen actively.

3. Mental Flexibility

Mental flexibility, also known as cognitive flexibility, is the ability to adapt to new situations, to think outside the box, and consider different perspectives. Employers value individuals with this type of cognitive ability that allows people to adjust their thinking and behavior to changing circumstances. 

4. Teamwork Ability

Teamwork is important in any profession, regardless of the industry or role. Employers value individuals with strong teamwork skills as they can work effectively with others and can contribute to the success of the team and the organization. Teamwork ability is a key skill for achieving common goals, fostering creativity, and promoting a positive work environment.

5. Self Leadership

Employers value individuals with strong self-leadership skills as it involves setting goals, making plans and taking action to achieve those goals, and being self-motivated, self-disciplined, and accountable for one’s own actions.

# Learning AI-based Tools to stay competitive

According to the Global AI Survey, three in four businesses (75%) are either exploring or implementing AI and are increasingly recognizing AI’s potential to transform their operations and create new business opportunities. The survey also revealed that the adoption of AI is still in its early stages, with many businesses facing challenges such as a lack of skilled talent to use these tools, difficulty integrating AI with existing systems, and concerns about data privacy and security.

If there’s one thing that can give designers a competitive edge, it is the use of AI generative tools. AI generative tools are designed to assist designers in creating new designs, patterns, and layouts using machine learning algorithms. These tools can generate a wide range of options based on a set of input parameters, allowing designers to quickly explore different possibilities and find new inspiration.

1. Natural Language Generation Tools (NLG)

NLG tools use algorithms to generate text based on predefined rules or templates. These tools are commonly used for report generation, news article writing, and chatbot interactions. For example GPT-3, Wordsmith, Quill, Articoolo, Textio, etc.

2. Content Ideation Tools

These tools use AI to generate ideas for content topics based on keyword analysis, social media trends, and other data sources. They can help the sales and marketing team identify new topics and angles to explore. For example BuzzSumo, SEMrush, ContentIdeator, Clearscope, etc.

3. Video and Image Generation Tools

There are various AI tools available for image and video generation, which use deep learning algorithms and computer vision techniques to create realistic and engaging visual content—for example Midjourney, DALL-E, Adobe Sensei, Lumen5, etc.

4. Music and Sound Generation Tools

AI can analyze existing music to create new compositions or generate realistic sound effects for video and gaming applications. For example Amper Music, AIVA, Jukedeck, etc.

The Road Ahead:

It’s important to note that while these tools can save time and resources, they also have limitations. They may lack the creativity and nuance that comes with human-generated content, and there is a risk of producing low-quality or biased content if the data inputs or algorithms used are flawed. So a designer who is familiar with these tools will have a better understanding of how to work with them and may be more in demand by companies and clients looking to integrate AI into their design processes. Embrace these opportunities and be open to new ways of thinking.

Disclaimer: I am not an economist. Everything mentioned in this article is supported by extensive research and is not my personal view. I hope this article gives you some clarity and confidence heading into these uncertain economic times. 

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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