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Enterprises investing in Workplace Mobility Can Survive Pandemics

4 minutes, 21 seconds read

Nearly one-third of the global population is under coronavirus lockdown. Large-scale quarantines and travel restrictions are posing challenges for businesses to continue their operations. While workforce protection remains the top priority for enterprises, prolonged isolation is an eye-opener to adopt workplace mobility.

As the world continues to fight the pandemic, flatten the curve and try to maintain normalcy by working from home — teams everywhere are trying to stay productive so that daily operations can continue to some degree. But this is not an easy task. By working remotely, there are a lot of challenges especially in communication and connectivity, not to mention challenges with remaining productive throughout the day. 

There was a time when mobility at work was considered a perk. Today, almost everyone, at some point, agrees that flexibility and liberty to work from home is essential. The 2020 Enterprise Mobility Trends Report anticipates that 42% (18.7 billion) of the global workforce will embrace mobility by 2020.

The need for workplace mobility

Workplace mobility empowers people to work from anywhere, at any time and from any device. It directly impacts employee productivity as well as the speed to execute business processes. How?

Dan Ariely, in his book Predictably Irrational, categorizes human behaviour in lines with the market and social norms. Market norms apply a monetary value to every transaction — salaries or payments against skill/talent. Whereas, social norms rely on the exchange of gifts, kindness, favour, etc. and is far away from any monetary transaction. 

While stringent work policies tend to inculcate market norms (skills are calculated against salaries), flexibility instils social norms (empathy and concern). People are willing to do more on their free-will.

In this 24/7 work environment social norms have a great advantage: they tend to make employees passionate, hardworking, flexible, and concerned. In a market where employees’ loyalty to their employers is often wilting, social norms are one of the best ways to make workers loyal, as well as motivated.

– suggests Ariely

How apps and AI-driven mobility solutions for employees can keep businesses operationally afloat?

By 2025, the number of unique mobile subscribers is projected to reach 5.9 billion. Market researchers also anticipate that there’ll be nearly 25 billion IoT devices, most of which will comprise business-related connected devices. However, it’s not just handy devices that are enabling mobility at work. Technologies are also empowering businesses to readily adopt mobility. 

For instance, Google has introduced a deck of enterprise mobility solutions. It provides cloud support to collaboration apps and management tools. Apart from G Suite, Google has invested in android and chrome platforms to support workplace mobility. 

Workplace mobility apps and features

Many organizations require time logs to ensure overtime and bonuses. Apps like SecurTime provide a cloud-based time-attendance workforce management solution with real-time tracking. It seamlessly integrates with payroll/HRMS and biometric systems without any dependency on hardware.

When people work remotely, creating a virtual collaborative environment can concern businesses. While email is the channel for all formal communication, it’s usual to lose track of conversations in emails and messengers. To organize work and priorities at the team level, Slack and Trello are popular apps.

Organizations with in-house software development teams often face hassles while planning, tracking, resolving bugs & issues and releasing products. Jira — an agile project management tool helps organizations to track every phase of product development and team progress irrespective of their physical location.

AI-driven enterprise mobility solutions

Mobile devices and cloud platforms are making it easier for teams to collaborate and deliver. Moreover, employees save substantial time on travelling, which gives them time to indulge in activities that foster creativity. 

Gartner predicts that by 2021, 40% of new enterprise applications will include AI technologies. So far, the adoption of AI was seen in consumer-facing operations to enhance customer experiences. Now, organizations are also focusing on enhancing employee experiences. For example, leading organizations are using NLP-powered chatbots for handling employee-queries regarding leave, work from home intimation, business-travel, etc. 

[Related: AI in recruitment and discovering talent]

Technology can equip employees with information at hand. AI solutions like Zelros provide instant information to Insurance sales advisors regarding products, clients, etc. 

AI-powered applications are becoming more human-centred and they can execute commands without touching/pressing a button. For example, with gesture recognition technology and voice user interface, simple tasks like sharing a file, reading a report, etc. can be done while driving, spending time with kids, evening walks, etc. removing dependencies that delay work.

[Related: How does AI recognize hand gestures]

The use of AI is evolving to automatically prioritize problems and send notifications to the concerned departments. SVM (Support Vector Machine) and CNN (Convolutional Neural Network) are machine learning algorithms for building classification models.

The bottom line

While one can prevent wars, natural calamities and pandemics are unavoidable. In the current context, the heat of the Corona outbreak is severely impacting industries including aviation, e-commerce, education, tourism, entertainment, hospitality, electronics, consumer and luxury goods. Businesses are thriving to remain operationally afloat. 

Embracing mobility at work today can prepare organizations for tomorrow’s pandemic resilience. 

Mantra Labs is helping enterprises invest in building their pandemic resilience by planning and scaling their mobility infrastructures, and enable greater use of mobility as a service. Talk to us today to know how we can help you, or reach out to us at hello@mantralabsglobal.com.

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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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