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How to Win the War Against Bad Master Data


Master data lays the foundation for your supplier and customer relationships. It identifies who you are doing business with, how you will do business with them, and how you will pay them or vice versa – not to mention it can prevent fraud, fines, and errors. However, teams often fail to reap the full benefits […]

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Author: Danny Thompson

How Cloud Technology Can Transform Product Management in a Complex Market


In the rapidly evolving and increasingly complex product development landscape, it is essential that product teams prioritize the adoption of tools that can help streamline development management. Cloud-based solutions can offer a way for product teams to better navigate challenges posed by these complexities, helping them to optimize workflows and better meet the demands of […]

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Author: Maziar Adl

Scaling Out to Keep Data Out of the Lost and Found


How much data would you be comfortable with losing? In the world of high-performance computing (HPC), the simple answer should be none. Given that HPC systems involve massive amounts of data, any loss – whether big or small – can have a catastrophic impact on customer and shareholder relationships, finances, complex simulations, and organizational reputation.  Any system lacking in […]

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Author: Erik Salo

GenAI at the Edge: The Power of TinyML and Embedded Databases

The convergence of artificial intelligence (AI) and edge computing is ushering in a new era of intelligent applications. At the heart of this transformation lies GenAI (Generative AI), which is rapidly evolving to meet the demands of real-time decision-making and data privacy. TinyML, a subset of machine learning that focuses on running models on microcontrollers, and embedded databases, which store data locally on devices, are key enablers of GenAI at the edge.

This blog delves into the potential of combining TinyML and embedded databases to create intelligent edge applications. We will explore the challenges and opportunities, as well as the potential impact on various industries.

Understanding GenAI, TinyML, and Embedded Databases

GenAI is a branch of AI that involves creating new content, such as text, images, or code. Unlike traditional AI models that analyze data, GenAI models generate new data based on the patterns they have learned.

TinyML is the process of optimizing machine learning models to run on resource-constrained devices like microcontrollers. These models are typically small, efficient, and capable of performing tasks like image classification, speech recognition, and sensor data analysis.

Embedded databases are databases designed to run on resource-constrained devices, such as microcontrollers and embedded systems. They are optimized for low power consumption, fast access times, and small memory footprints.

The Power of GenAI at the Edge

The integration of GenAI with TinyML and embedded databases presents a compelling value proposition:

  • Real-time processing: By running large language models (LLMs) at the edge, data can be processed locally, reducing latency and enabling real-time decision-making.
  • Enhanced privacy: Sensitive data can be processed and analyzed on-device, minimizing the risk of data breaches and ensuring compliance with privacy regulations.
  • Reduced bandwidth consumption: Offloading data processing to the edge can significantly reduce network traffic, leading to cost savings and improved network performance.

Technical Considerations

To successfully implement GenAI at the edge, several technical challenges must be addressed:

  • Model optimization: LLMs are often computationally intensive and require significant resources. Techniques such as quantization, pruning, and knowledge distillation can be used to optimize models for deployment on resource-constrained devices.
  • Embedded database selection: The choice of embedded database is crucial for efficient data storage and retrieval. Factors to consider include database footprint, performance, and capabilities such as multi-model support.
  • Power management: Optimize power consumption to prolong battery life and ensure reliable operation in battery-powered devices.
  • Security: Implement robust security measures to protect sensitive data and prevent unauthorized access to the machine learning models and embedded database

A Case Study: Edge-Based Predictive Maintenance

Consider a manufacturing facility equipped with sensors that monitor the health of critical equipment. By deploying GenAI models and embedded databases at the edge, the facility can:

  1. Collect sensor data: Sensors continuously monitor equipment parameters such as temperature, vibration, and power consumption.
  2. Process data locally: GenAI models analyze the sensor data in real-time to identify patterns and anomalies that indicate potential equipment failures.
  3. Trigger alerts: When anomalies are detected, the system can trigger alerts to notify maintenance personnel.
  4. Optimize maintenance schedules: By predicting equipment failures, maintenance can be scheduled proactively, reducing downtime and improving overall efficiency.

The Future of GenAI at the Edge

As technology continues to evolve, we can expect to see even more innovative applications of GenAI at the edge. Advances in hardware, software, and algorithms will enable smaller, more powerful devices to run increasingly complex GenAI models. This will unlock new possibilities for edge-based AI, from personalized experiences to autonomous systems.

In conclusion, the integration of GenAI, TinyML, and embedded databases represents a significant step forward in the field of edge computing. By leveraging the power of AI at the edge, we can create intelligent, autonomous, and privacy-preserving applications. 

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. Optimized for embedded systems and edge computing, Zen boasts small-footprint with fast read and write access, making it ideal for resource-constrained environments.

Additional Resources:

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Author: Kunal Shah

Sync Your Data From Edge-to-Cloud With Actian Zen EasySync

Welcome back to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 3 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen.

Establishing consistency and consolidating data across different devices and servers are essential for most edge-to-cloud solutions. Syncing data is necessary for almost every mobile, edge, or IoT application, and developers are familiar with the basic concepts and challenges. That’s why many experienced developers value efficient solutions. The Actian Zen EasySync tool is a new utility specifically designed for this purpose.

This blog will guide you through the steps for setting up and running EasySync.

What is EasySync?

Zen EasySync is a versatile data synchronization tool that automates the synchronization of newly created or updated records from one Zen database server to another. This tool transfers data across multiple servers, whether you’re working on the edge or within a centralized network. Key features of EasySync include:

  • Flexible Syncing Schedule: Sync data can be scheduled to poll for changes on a defined interval or can be used as a batch transfer tool, depending on your needs.
  • Logging: Monitor general activity, detect errors, and troubleshoot unexpected results with logging capabilities.

Prerequisites

Before using EasySync, ensure the following in your Zen installation:

  • System Data: The files must have system data v2 enabled, with file format version 13 or version 16.
  • ZEN 16.0  installed.
  • Unique Key: Both source and destination files must have a user-defined unique key.

EasySync Usage Scenarios

EasySync supports various data synchronization scenarios, making it a flexible tool for different use cases. Here are some common usage scenarios depicted in the diagram below:

  1. Push to Remote: Synchronize data from a local database to a remote database.
  2. Pull from Remote: Synchronize data from a remote database to a local database.
  3. Pull and Push to Remotes: Synchronize data between multiple remote databases.
  4. Aggregate Data from Edge: Collect data from multiple edge databases and synchronize it to a central database.
  5. Disseminate Data to Edge: Distribute data from a central database to multiple edge databases.

actian edge easysync chart

Getting Started With EasySync

To demonstrate how to use EasySync, we will create a Python application that simulates sensor data and synchronizes it using EasySync. This application will create a sensor table on your edge device and remote server, insert random sensor data, and sync the data with a remote database. The remote database can contain various sets of data from several edge devices.

Step 1: Create the Configuration File

First, we need to create a JSON configuration file (config.json). This file will define the synchronization settings and the files to be synchronized, where files are stored in a source (demodata) and destination (demodata) folders.

Here is an example of what the configuration file might look like:

{
  "version": 1,
  "settings": {
    "polling_interval_sec": 10,
    "log_file": " C:/ProgramData/Actian/Zen/logs/datasync.log",
    "record_err_log": " C:/ProgramData/Actian/Zen/logs/recorderrors.log",
    "resume_on_error": true
  },
  "files": [
    {
      "id": 1,
      "source_file": "btrv://localhost/demodata?dbfile= sensors.mkd",
      "source_username": "",
      "source_password": "",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile= sensors.mkd",
      "destination_username": "",
      "destination_password": "",
      "unique_key": 0
    },
    {
      "id": 2,
      "source_file": "btrv://localhost/demodata?dbfile=bookstore.mkd",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile=bookstore.mkd",
      "create_destination": true,
      "unique_key": 1
    }
  ]
}

Step 2: Write the Python Script

Next, we create a Python script that simulates sensor data, creates the necessary database table, and inserts records into the database. 

Save the following Python code in a file named run_easysync.py. Run the script to create the sensors table on your local edge device and server, and to insert data on your edge device.

import pyodbc
import random
import time
from time import sleep
random.seed()
def CreateSensorTable(server, database):
    try:
db_connection_string = f"Driver={{Pervasive ODBC Interface}};
ServerName={server};
DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
       # cursor.execute("DROP TABLE IF EXISTS sensors;")
        cursor.execute("""
            CREATE TABLE sensors SYSDATA_KEY_2(
                id IDENTITY,
                ts DATETIME NOT NULL,
                temperature INT NOT NULL,
                pressure FLOAT NOT NULL,
                humidity INT NOT NULL
            );
        """)
        print(f"Table 'sensors' created successfully on {server}")
     except pyodbc.DatabaseError as err:
         print(f"Failed to create table on {server} with error: {err}")
def GetTemperature():
     return random.randint(70, 98)
def GetPressure():
     return round(random.uniform(29.80, 30.20), 3)
def GetHumidity():
     return random.randint(40, 55)
def InsertSensorRecord(server, database):
     temp = GetTemperature()
     press = GetPressure()
     hum = GetHumidity()
     try:
      insert = 'INSERT INTO sensors (id, ts, temperature, pressure, humidity) VALUES (0, NOW(), ?, ?, ?)'
        db_connection_string = f"Driver={{Pervasive ODBC Interface}};ServerName={server};DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
        cursor.execute(insert, temp, press, hum)
        print(f"Inserted record [Temperature {temp}, Pressure {press}, Humidity {hum}] on {server}")
    except pyodbc.DatabaseError as err:
        print(f"Failed to insert record on {server} with error: {err}")
# Main
local_server = "localhost"
local_database = "Demodata"
remote_server = "remote-server_name"
remote_database = "demodata"

# Create sensor table on both local and remote servers
CreateSensorTable(local_server, local_database)
CreateSensorTable(remote_server, remote_database)

while True:
    InsertSensorRecord(local_server, local_database)
    sleep(0.5)

Syncing Data from IoT Device to Remote Server

Now, let’s incorporate the data synchronization process using the EasySync tool to ensure the sensor data from the IoT device is replicated to a remote server.

Step 3: Run EasySync

To synchronize the data using EasySync, follow these steps:

  1. Ensure the easysync utility is installed and accessible from your command line.
  2. Run the Python script to start generating and inserting sensor data.
  3. Execute the EasySync command to start the synchronization process.

Open your command line and navigate to the directory containing your configuration file and Python script. Then, run the following command:

easysync -o config.json

This command runs the EasySync utility with the specified configuration file and ensures that the synchronization process begins.

Conclusion

Actian Zen EasySync is a simple but effective tool for automating data synchronization across Zen database servers. By following the steps outlined in this blog, you can easily set up and run EasySync. EasySync provides the flexibility and reliability you need to manage your data on the edge. Remember to ensure your files are in the correct format, have system data v2 enabled, and possess a user-defined unique key for seamless synchronization. With EasySync, you can confidently manage data from IoT devices and synchronize it to remote servers efficiently.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

The post Sync Your Data From Edge-to-Cloud With Actian Zen EasySync appeared first on Actian.


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Author: Johnson Varughese

How Data is Revolutionizing Transportation and Logistics

In today’s fast-paced world, the transportation and logistics industry is the backbone that keeps the global economy moving. Logistics is expected to be the fastest-growing industry by 2030. As demand for faster, more efficient, and cost-effective services grows, you’ll need to be able to connect, manage, and analyze data from all parts of your business to make fast, efficient decisions that improve your supply chain, logistics, and other critical areas.  

Siloed data, poor data quality, and a lack of integration across systems can hinder you from optimizing your operations, forecasting demand accurately, and providing top-tier customer service. By leveraging advanced data integration, management, and analytics, you can transform these challenges into opportunities, driving efficiency, reliability, and customer satisfaction. 

The Challenges: Harnessing Data in Transportation and Logistics 

One of the most significant hurdles in the transportation and logistics sector is accessing quality data across departments. Data is often scattered across multiple systems—such as customer relationship management (CRM), enterprise resource planning (ERP), telematics systems, and even spreadsheets—without a unified access point. This fragmentation creates data silos, where crucial information is isolated across individuals and business units, making it difficult for different departments to access the data they need. For instance, the logistics team might not have access to customer data stored in the CRM, which can hinder their ability to accurately plan deliveries, personalize service, proactively address potential issues, and improve overall communication.   

Furthermore, the lack of integration across these systems exacerbates the problem of fragmented data. Different data sources often store information in varied and incompatible formats, making it challenging to compare or combine data across systems. This leads to inefficiencies in several critical areas, including demand forecasting, route optimization, predictive maintenance, and risk management. Without a unified view of operations, companies struggle to leverage customer behavior insights from CRM data to improve service quality or optimize delivery schedules, and face other limitations.  

The Impact: Inefficiencies and Operational Risks 

The consequences of these data challenges are far-reaching. Inaccurate demand forecasts can lead to stockouts, overstock, and poor resource allocation, all of which directly impact your bottom line. Without cohesive predictive maintenance, operational downtime increases, negatively impacting delivery schedules and customer satisfaction. Inefficient routing, caused by disparate data sources, results in higher fuel costs and delayed deliveries, further eroding profitability and customer trust. 

Additionally, the lack of a unified customer view can hinder your ability to provide personalized services, reducing customer satisfaction and loyalty. In the absence of integrated data, risk management becomes reactive rather than proactive, with delayed data processing increasing exposure to risks and limiting your ability to respond quickly to emerging threats. 

The Solution: A Unified Data Platform 

Imagine a scenario where your transportation and logistics operations are no longer bogged down by data fragmentation and poor integration. With a unified view across your entire organization, you can access accurate, real-time insights across the end-to-end supply chain, enabling youto make data-driven decisions that reduce delays and improve overall efficiency. 

A unified data platform integrates fragmented data from multiple sources into a single, accessible system. This integration eliminates data silos, ensuring that all relevant information—whether from CRM, ERP, telematics, or GPS tracking systems—is available in real-time to decision-makers across your organization.

For example, predictive maintenance becomes significantly more effective when historical data, sensor data, and telematics are integrated and analyzed consistently. This approach minimizes unplanned downtime, extends the lifespan of assets, and ensures that vehicles and equipment are always operating at peak efficiency, leading to substantial cost savings.  

Similarly, advanced route optimization algorithms that utilize real-time traffic data, weather conditions, and historical delivery performance can dynamically adjust routes for drivers. The result is consistently on-time deliveries, reduced fuel costs, and enhanced customer satisfaction through reliable and efficient service. 

A unified data platform also enables the creation of a 360-degree customer view by consolidating customer data from various touchpoints—such as transactions, behaviors, and support interactions—into a comprehensive and up-to-date profile. This holistic view allows you to offer personalized services and targeted marketing, leading to higher customer satisfaction, increased loyalty, and more successful sales strategies. 

Proactive risk management is another critical benefit of a unified data platform. By analyzing real-time data from multiple sources, you can identify potential risks before they escalate into critical issues. Whether you’re experiencing supply chain disruptions, regulatory compliance challenges, or logistical issues, the ability to respond swiftly to emerging risks reduces potential losses and ensures smooth operations, even in the face of unforeseen challenges. 

Face the Future of Transportation and Logistics With Confidence  

As the transportation and logistics industry continues to evolve, the role of data will only become more critical. The Actian Data Platform can help you overcome the current challenges of data fragmentation, poor quality, and lack of integration in addition to helping you position yourself at the forefront of innovation in the industry. By leveraging data to optimize operations, improve customer service, and proactively manage risks, you will achieve greater efficiency, cost-effectiveness, and customer satisfaction—driving greater success in a competitive and dynamic market.

The post How Data is Revolutionizing Transportation and Logistics appeared first on Actian.


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Author: Kasey Nolan

Avoiding the Pitfalls: Don’t Rush Chatbot Deployment


AI has rapidly emerged as a status symbol for companies worldwide because it signifies innovation and a commitment to staying ahead of technological trends. This has prompted the critical question, “Who can implement it first?” by businesses eager to position themselves as leaders in the field and distinguish themselves from competitors lagging in the AI […]

The post Avoiding the Pitfalls: Don’t Rush Chatbot Deployment appeared first on DATAVERSITY.


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Author: Cláudio Rodrigues

5 Misconceptions About Data Quality and Governance

The quality and governance of data has never been more critical than it is today. 

In the rapidly evolving landscape of business technology, advanced analytics and generative AI have emerged as game-changers, promising unprecedented insights and efficiencies. However, as these technologies become more sophisticated, the adage GIGO or “garbage in, garbage out” has never been more relevant. For data and IT professionals, understanding the critical role of data quality in these applications is not just important—it’s imperative for success.

Going Beyond Data Processing

Advanced analytics and generative AI don’t just process data; they amplify its value. This amplification can be a double-edged sword:

Insight Magnification: High-quality data leads to sharper insights, more accurate predictions, and more reliable AI-generated content.

Error Propagation: Poor quality data can lead to compounded errors, misleading insights, and potentially harmful AI outputs.

These technologies act as powerful lenses—magnifying both the strengths and weaknesses of your data. As the complexity of models increases, so does their sensitivity to data quality issues.

Effective Data Governance is Mandatory

Implementing robust data governance practices is equally important. Governance today is not just a regulatory checkbox—it’s a fundamental requirement for harnessing the full potential of these advanced technologies while mitigating associated risks.

As organizations rush to adopt advanced analytics and generative AI, there’s a growing realization that effective data governance is not a hindrance to innovation, but rather an enabler.

Data Reliability at Scale: Advanced analytics and AI models require vast amounts of data. Without proper governance, the reliability of these datasets becomes questionable, potentially leading to flawed insights.

Ethical AI Deployment: Generative AI in particular raises significant ethical concerns. Strong governance frameworks are essential for ensuring that AI systems are developed and deployed responsibly, with proper oversight and accountability.

Regulatory Compliance: As regulations like GDPR, CCPA, and industry-specific mandates evolve to address AI and advanced analytics, robust data governance becomes crucial for maintaining compliance and avoiding hefty penalties.

But despite the vast mines of information, many organizations still struggle with misconceptions that hinder their ability to harness the full potential of their data assets. 

As data and technology leaders navigate the complex landscape of data management, it’s crucial to dispel these myths and focus on strategies that truly drive value. 

For example, Gartner offers insights into the governance practices organizations typically follow, versus what they actually need:

why modern digital organizations need adaptive data governance

Source: Gartner

5 Data Myths Impacting Data’s Value

Here are five common misconceptions about data quality and governance, and why addressing them is essential.

Misconception 1: The ‘Set It and Forget It’ Fallacy

Many leaders believe that implementing a data governance framework is a one-time effort. They invest heavily in initial setup but fail to recognize that data governance is an ongoing process that requires continuous attention and refinement mapped to data and analytics outcomes. 

In reality, effective data governance is dynamic. As business needs evolve and new data sources emerge, governance practices must adapt. Successful organizations treat data governance as a living system, regularly reviewing and updating policies, procedures, and technologies to ensure they remain relevant and effective for all stakeholders. 

Action: Establish a quarterly review process for your data governance framework, involving key stakeholders from across the organization to ensure it remains aligned with business objectives and technological advancements.

Misconception 2: The ‘Technology Will Save Us’ Trap

There’s a pervasive belief that investing in the latest data quality tools and technologies will automatically solve all data-related problems. While technology is undoubtedly crucial, it’s not a silver bullet.

The truth is, technology is only as good as the people and processes behind it. Without a strong data culture and well-defined processes, even the most advanced tools will fall short. Successful data quality and governance initiatives require a holistic approach that balances technology with human expertise and organizational alignment.

Action: Before investing in new data quality and governance tools, conduct a comprehensive assessment of your organization’s data culture and processes. Identify areas where technology can enhance existing strengths rather than trying to use it as a universal fix.

Misconception 3:. The ‘Perfect Data’ Mirage

Some leaders strive for perfect data quality across all datasets, believing that anything less is unacceptable. This pursuit of perfection can lead to analysis paralysis and a significant resource drain.

In practice, not all data needs to be perfect. The key is to identify which data elements are critical for decision-making and business operations, and focus quality efforts there. For less critical data, “good enough” quality that meets specific use case requirements may suffice.

Action: Conduct a data criticality assessment to prioritize your data assets. Develop tiered quality standards based on the importance and impact of different data elements on your business objectives.

Misconception 4: The ‘Compliance is Enough’ Complacency

With increasing regulatory pressures, some organizations view data governance primarily through the lens of compliance. They believe that meeting regulatory requirements is sufficient for good data governance.

However, true data governance goes beyond compliance. While meeting regulatory standards is crucial, effective governance should also focus on unlocking business value, improving decision-making, and fostering innovation. Compliance should be seen as a baseline, not the end goal.

Action: Expand your data governance objectives beyond compliance. Identify specific business outcomes that improved data quality and governance can drive, such as enhanced customer experienced or more accurate financial forecasting.

Misconception 5: The ‘IT Department’s Problem’ Delusion

There’s a common misconception that data quality and governance are solely the responsibility of the IT department or application owners. This siloed approach often leads to disconnects between data management efforts and business needs.

Effective data quality and governance require organization-wide commitment and collaboration. While IT plays a crucial role, business units must be actively involved in defining data quality standards, identifying critical data elements, and ensuring that governance practices align with business objectives.

Action: Establish a cross-functional data governance committee that includes representatives from IT, business units, and executive leadership. This committee should meet regularly to align data initiatives with business strategy and ensure shared responsibility for data quality.

Move From Data Myths to Data Outcomes

As we approach the complexities of data management in 2025, it’s crucial for data and technology leaders to move beyond these misconceptions. By recognizing that data quality and governance are ongoing, collaborative efforts that require a balance of technology, process, and culture, organizations can unlock the true value of their data assets.

The goal isn’t data perfection, but rather continuous improvement and alignment with business objectives. By addressing these misconceptions head-on, data and technology leaders can position their organizations for success in an increasingly competitive world.

The post 5 Misconceptions About Data Quality and Governance appeared first on Actian.


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Author: Dee Radh

Cloud Transition for Startups: Overcoming Data Management Challenges and Best Practices


For startups, transitioning to the cloud from on-prem is more than a technical upgrade – it’s a strategic pivot toward greater agility, innovation, and market responsiveness. While the cloud promises unparalleled scalability and flexibility, navigating the transition can be complex. Here’s a straightforward guide to overcoming key challenges and making the most of cloud computing. Streamlining […]

The post Cloud Transition for Startups: Overcoming Data Management Challenges and Best Practices appeared first on DATAVERSITY.


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Author: Paul Pallath

Understanding the Role of Data Quality in Data Governance

The ability to make informed decisions hinges on the quality and reliability of the underlying data. As organizations strive to extract maximum value from their data assets, the critical interplay between data quality and data governance has emerged as a fundamental imperative. The symbiotic relationship between these two pillars of data management can unlock unprecedented insights, drive operational efficiency, and, ultimately, position enterprises for sustained success.

Understanding Data Quality

At the heart of any data-driven initiative lies the fundamental need for accurate, complete, and timely information. Data quality encompasses a multifaceted set of attributes that determine the trustworthiness and fitness-for-purpose of data. From ensuring data integrity and consistency to minimizing errors and inconsistencies, a robust data quality framework is essential for unlocking the true potential of an organization’s data assets.

Organizations can automate data profiling, validation, and standardization by leveraging advanced data quality tools. This improves the overall quality of the information and streamlines data management processes, freeing up valuable resources for strategic initiatives.

Profiling Data With Precision

The first step in achieving data quality is understanding the underlying data structures and patterns. Automated data profiling tools, such as those offered by Actian, empower organizations to quickly and easily analyze their data, uncovering potential quality issues and identifying areas for improvement. By leveraging advanced algorithms and intelligent pattern recognition, these solutions enable businesses to tailor data quality rules to their specific requirements, ensuring that data meets the necessary standards.

Validating and Standardizing Data

With a clear understanding of data quality, the next step is implementing robust data validation and standardization processes. Data quality solutions provide a comprehensive suite of tools to cleanse, standardize, and deduplicate data, ensuring that information is consistent, accurate, and ready for analysis. Organizations can improve data insights and make more informed, data-driven decisions by integrating these capabilities.

The Importance of Data Governance

While data quality is the foundation for reliable and trustworthy information, data governance provides the overarching framework to ensure that data is effectively managed, secured, and leveraged across the enterprise. Data governance encompasses a range of policies, processes, and technologies that enable organizations to define data ownership, establish data-related roles and responsibilities, and enforce data-related controls and compliance.

Our parent company, HCLSoftware, recently announced the intent to acquire Zeenea, an innovator in data governance. Together, Zeenea and Actian will provide a highly differentiated solution for data quality and governance.

Unlocking the Power of Metadata Management

Metadata management is central to effective data governance. Solutions like Zeenea’s data discovery platform provide a centralized hub for cataloging, organizing, and managing metadata across an organization’s data ecosystem. These platforms enable enterprises to create a comprehensive, 360-degree view of their data assets and associated relationships by connecting to a wide range of data sources and leveraging advanced knowledge graph technologies.

Driving Compliance and Risk Mitigation

In today’s increasingly regulated business landscape, data governance is critical in ensuring compliance with industry standards and data privacy regulations. Robust data governance frameworks, underpinned by powerful metadata management capabilities, empower organizations to implement effective data controls, monitor data usage, and mitigate the risk of data breaches and/or non-compliance.

The Synergistic Relationship Between Data Quality and Data Governance

While data quality and data governance are distinct disciplines, they are inextricably linked and interdependent. Robust data quality underpins the effectiveness of data governance, ensuring that the policies, processes, and controls are applied to data to extract reliable, trustworthy information. Conversely, a strong data governance framework helps to maintain and continuously improve data quality, creating a virtuous cycle of data-driven excellence.

Organizations can streamline the data discovery and access process by integrating data quality and governance. Coupled with data quality assurance, this approach ensures that users can access trusted data, and use it to make informed decisions and drive business success.

As organizations embrace transformative technologies like artificial intelligence (AI) and machine learning (ML), the need for reliable, high-quality data becomes even more pronounced. Data governance and data quality work in tandem to ensure that the data feeding these advanced analytics solutions is accurate, complete, and fit-for-purpose, unlocking the full potential of these emerging technologies to drive strategic business outcomes.

In the age of data-driven transformation, the synergistic relationship between data quality and data governance is a crucial competitive advantage. By seamlessly integrating these two pillars of data management, organizations can unlock unprecedented insights, enhance operational efficiency, and position themselves for long-term success.

The post Understanding the Role of Data Quality in Data Governance appeared first on Actian.


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Author: Traci Curran

Edge vs. Cloud: The Data Dilemma of AI-Powered IoT


As artificial intelligence (AI) integrates with the Internet of Things (IoT), a trillion-dollar question emerges: Is it better to process device data at the edge or in the cloud? This decision carries significant implications for privacy, performance, and the future of smart devices. So, let’s explore the growth of autonomous smart devices, examine the key […]

The post Edge vs. Cloud: The Data Dilemma of AI-Powered IoT appeared first on DATAVERSITY.


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Author: Carsten Rhod Gregersen

5 Technologies You Need to Protect Data Privacy


Data privacy is the practice of handling personal information with care and respect, ensuring it is only accessed, processed, and stored in ways that align with legal requirements and individual consent. It protects personal data from unauthorized access and misuse. This includes securing data both at rest and in transit, applying best practices for encryption, […]

The post 5 Technologies You Need to Protect Data Privacy appeared first on DATAVERSITY.


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Author: Gilad David Maayan

Using Data to Build Democratized AI Applications: The Actian Approach

Artificial intelligence (AI) has become a cornerstone of modern technology, powering innovations from personalized recommendations to self-driving cars. Traditionally, AI development was limited to tech giants and specialized experts.

However, the concept of democratized AI aims to broaden access, making it possible for a wider audience to develop and use AI applications. In this post, we’ll explore the pivotal role data plays in democratizing AI and how Actian’s cutting-edge solutions are enabling this shift.

What is Democratized AI?

Democratized AI is all about making AI tools and technologies accessible to a broad range of users—whether they’re analysts at small businesses, individual developers, or even those without technical backgrounds. It’s about breaking down the barriers to AI development and enabling more people to incorporate AI into their projects and business operations to transform ideas into actionable solutions, accelerate innovation, and deliver desired business outcomes faster. Actian is a key player in this movement, offering tools that simplify data management and integration for AI applications.

The Role of Data in AI Democratization

Data is essential to AI. It trains AI models and informs their predictions and decisions. When it comes to democratized AI, data serves several critical functions, including these four:

  1. Training Resources: Open datasets and pre-trained models empower developers to create AI applications without needing extensive proprietary data.
  2. Personalization: User-generated data allows even small applications to deliver personalized AI experiences.
  3. Transparency: Open data practices enhance the transparency of AI systems, which is vital for building trust.
  4. Continuous Improvement: User feedback data helps refine AI models over time, making them more accurate and relevant.

Actian’s DataConnect and Actian Data Platform are central to these processes, providing powerful, easy-to-use tools for data integration, management, and analysis.

5 Key Components of Data-Driven, Democratized AI Applications

  1. User-Friendly AI Platforms: Tools like AutoML simplify the creation and deployment of AI models.
  2. Data Integration and Management: Actian’s DataConnect excels here, offering robust extract, transform, and load (ETL) capabilities that make it easy to prepare data for AI.
  3. Scalable Data Processing: The Actian Data Platform offers high-performance data processing, essential for handling the large datasets required in AI.
  4. Cloud-Based AI Services: API-based services provide pre-trained models for common AI tasks like image recognition or natural language processing.
  5. Collaborative Platforms: These spaces allow developers to share models, datasets, and knowledge, fostering community-driven AI development.

Actian’s Role in Democratizing AI

Actian’s products play a crucial role in democratizing AI by addressing some of the most challenging aspects of AI development, including these four:

  1. Data Integration With Actian’s DataConnect: This tool simplifies the process of aggregating data from various sources, a critical step in preparing datasets for AI. Its intuitive interface and robust capabilities make it accessible to users with varying levels of technical expertise.
  2. Scalable Data Processing With Actian Data Platform: This platform provides the necessary infrastructure to manage large-scale data processing tasks, enabling businesses of all sizes to extract insights from their data—a fundamental step in AI applications.
  3. Real-time Data Analytics: Actian’s solutions support real-time data analytics, crucial for AI applications that require immediate decisions or predictions.
  4. Hybrid and Multi-Cloud Support: Actian’s flexible deployment options span on-premises, cloud, and hybrid, allowing organizations to build AI applications that align with their infrastructure and data governance needs.

3 Examples of Democratized AI Applications Powered by Actian

  1. Predictive Maintenance for Small Manufacturers: By using Actian’s DataConnect to integrate sensor data and the Actian Data Platform for analysis, small manufacturing businesses can implement AI-driven predictive maintenance systems.
  2. Customer Behavior Analysis: Retailers can use Actian’s tools to integrate point-of-sale data with online customer interactions, feeding this data into AI models for highly personalized marketing strategies.
  3. Supply Chain Optimization: Actian’s solutions allow businesses to integrate and analyze data from multiple supply chain points, facilitating AI-driven optimization strategies.

Understanding Challenges and Considerations

While democratized AI offers significant potential, it also presents four primary challenges:

  1. Data Quality and Bias: Ensuring high-quality, representative data is crucial. Actian’s DataConnect’s data profiling and cleansing/data quality features help address this issue.
  2. Privacy and Security: As AI becomes more accessible, safeguarding data privacy and security becomes increasingly important. Actian’s solutions include robust security features to protect sensitive information.
  3. Ethical Use: The widespread adoption of AI requires education on its ethical implications and responsible usage.
  4. Technical Limitations: While tools are becoming more user-friendly, there’s still a learning curve. Actian provides comprehensive support to help users overcome these challenges.

Future Outlook: 5 Emerging Trends

The future of democratized AI is bright, with several key trends on the horizon:

  1. No-Code/Low-Code AI Platforms: Expect more intuitive platforms that make AI development accessible without coding expertise.
  2. Edge AI: Bringing AI capabilities to resource-constrained devices will become more prevalent.
  3. Explainable AI: Emphasizing transparency in AI decisions will help build trust.
  4. Growth of AI Communities: Expanding communities and knowledge-sharing platforms will foster collaborative AI development.
  5. AI Integration in Everyday Tools: AI will become increasingly embedded in common software and tools.

Actian is well-positioned to support these trends with ongoing advancements in its data management and analytics solutions to meet the evolving needs of AI applications.

Empowering Innovation With Accessible AI

Democratized AI, driven by accessible data and tools, has the potential to revolutionize our interaction with technology. By making AI accessible to a diverse group of creators, we unlock new possibilities for innovation.

Actian’s suite of products, including DataConnect and the Actian Data Platform, plays a crucial role in this democratization by simplifying the essential steps of data integration, management, and analysis in the AI development process. These products also ensure data is properly prepped for AI.

As we continue to democratize AI, it’s essential to prioritize responsible development practices, ensuring that AI systems are fair, transparent, and beneficial to society. With Actian’s powerful, secure, and user-friendly tools, businesses and developers are well-equipped to confidently explore the exciting possibilities of democratized AI, transforming data into actionable insights and innovative AI-driven solutions.

The post Using Data to Build Democratized AI Applications: The Actian Approach appeared first on Actian.


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Author: Steven B. Becker

Embracing Data and Emerging Technologies for Quality Management Excellence


In today’s rapidly evolving business landscape, the role of quality management (QM) is undergoing a significant transformation. No longer just a compliance checkbox, QM is emerging as a strategic asset that can drive continuous improvement and operational excellence. This shift is largely propelled by the adoption of intelligent technologies and the strategic use of data, […]

The post Embracing Data and Emerging Technologies for Quality Management Excellence appeared first on DATAVERSITY.


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Author: Anthony Hudson

The Data Difference: How SMBs Are Getting Ahead of the Competition


The cost of complacency is becoming crystal clear in the small and medium-sized business (SMB) space. There’s little room for those who rest on their laurels, especially when they make up over 95% of businesses globally emerging all the time. Amid fierce and crowded competition, innovation increasingly sets apart the high performers from those struggling to stand their […]

The post The Data Difference: How SMBs Are Getting Ahead of the Competition appeared first on DATAVERSITY.


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Author: Claire Gribbin

The Relationship Between Storage Consolidation and a Hybrid Multi-Cloud IT Strategy


When you are presenting a way for IT to save money and have a better strategy to leverage the cloud, here’s a pro tip that can benefit any and all enterprises: A hybrid multi-cloud approach, with a strong private cloud configuration, creates the opportunity to consolidate storage arrays for maximum efficiency. Consolidation of storage saves on […]

The post The Relationship Between Storage Consolidation and a Hybrid Multi-Cloud IT Strategy appeared first on DATAVERSITY.


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Author: Eric Herzog

Change Management in Data Projects: Why We Ignored It and Why We Can’t Afford to Anymore
For decades, we’ve heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals.[1] So why do we keep making the same mistake, and more importantly, […]


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Author: Christine Haskell

Synergy: Data Security Posture Management and Data Security Governance
Several years ago, while working for a firm developing groundbreaking software, I proposed to my boss that we were, in fact, creating an entirely new market class of software. My boss quickly dismissed this notion, stating that software firms don’t create market categories — analyst firms do. Fast forward to today, and those very analyst […]


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Author: Myles Suer

Eyes on Data: Understanding Data Products and Their Role in Data Marketplaces
In the rapidly evolving landscape of data management, the concept of data products has emerged as a cornerstone for effective data utilization and governance. Industry experts have shed light on the critical nature of data products, their distinction from data assets, and their pivotal role in data marketplaces. As organizations strive to maximize the value […]


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Author: EDM Council

A Step Ahead: IoT Sensors – Where Vast Data Comes From
The insight we gain from an IoT system is derived from the data obtained by its sensors. Driven by innovations in materials and nanotechnology, sensor technology has been developing at unprecedented speeds and has resulted in lower-cost sensors that have better accuracy, are smaller in size, and able to detect or measure the presence of […]


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Author: The MITRE Corporation

3 Examples of LLM Use in Business Intelligence


Large language models (LLMs) are advanced AI systems designed to process and generate human-like text by training on extensive datasets. They excel in tasks ranging from translation and summarization to answering questions and writing content, effectively simplifying what used to be labor-intensive, complex interactions between humans and machines. LLMs represent a transformative leap in artificial […]

The post 3 Examples of LLM Use in Business Intelligence appeared first on DATAVERSITY.


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Author: Gaurav Belani

A Day in the Life of an Application Owner

The role of an application owner is often misunderstood within businesses. This confusion arises because, depending on the company’s size, an application owner could be the CIO or CTO at a smaller startup, or a product management lead at a larger technology company. Despite the variation in titles, the core responsibilities remain the same: managing an entire application from top to bottom, ensuring it meets the business’s needs (whether it’s an internal or customer-facing application), and doing so cost-effectively.

Being an application owner is a dynamic and multifaceted role that requires a blend of technical expertise, strategic thinking, and excellent communication skills. Here’s a glimpse into a typical day in the life of an application owner.

Morning: Planning and Prioritizing

6:30 AM – 7:30 AM: Start the Day Right 

The day begins early with a cup of coffee and a quick review of emails and messages. This is the time to catch up on any overnight developments, urgent issues, or updates from global teams.

7:30 AM – 8:30 AM: Daily Stand-Up Meeting 

The first official task is the daily stand-up meeting with the development team. This meeting is crucial for understanding the current status of ongoing projects, identifying any roadblocks, and setting priorities for the day. It’s also an opportunity to align the team’s efforts with the overall business goals and discuss any new application needs.

Mid-Morning: Deep Dive into Projects

8:30 AM – 10:00 AM: Project Reviews and Code Reviews 

After the stand-up, it’s time to dive into project reviews. This involves going through the latest code commits, reviewing progress on key features, and ensuring that everything is on track, and if it’s not, create a strategy to address the issues. Code reviews are essential to maintain the quality and integrity of the application.

10:00 AM – 11:00 AM: Stakeholder Meetings 

Next up are meetings with stakeholders. These could be product managers, business analysts, or even end-users. The goal is to gather feedback, discuss new requirements, and ensure that the application is meeting the needs of the business.

Late Morning: Problem Solving and Innovation

11:00 AM – 12:00 PM: Troubleshooting and Bug Fixes 

No day is complete without some troubleshooting. This hour is dedicated to addressing any critical issues or bugs that have been reported. It’s a time for quick thinking and problem-solving to ensure minimal disruption to users.

12:00 PM – 1:00 PM: Lunch Break and Networking 

Lunch is not just a break but also an opportunity to network with colleagues, discuss ideas, and sometimes even brainstorm solutions to ongoing challenges. 

Afternoon: Strategic Planning and Development

1:00 PM – 2:30 PM: Strategic Planning 

The afternoon kicks off with strategic planning sessions. These involve working on the application’s roadmap, planning future releases, incorporating customer input, and aligning with the company’s long-term vision. It’s a time to think big and set the direction for the future.

2:30 PM – 4:00 PM: Development Time 

This is the time to get hands-on with development. Whether it’s coding new features, optimizing existing ones, or experimenting with new technologies, this block is dedicated to building and improving the application.

Late Afternoon: Collaboration and Wrap-Up

4:00 PM – 5:00 PM: Cross-Functional Team Standup 

Collaboration is key to the success of any application. This hour is spent working with cross-functional teams such as sales, UX/UI designers, and marketing to analyze and improve the product onboarding experience. The goal is to ensure that everyone is aligned and working toward the same objectives.

5:00 PM – 6:00 PM: End-of-Day Review and Planning for Tomorrow 

The day wraps up with a review of what was accomplished and planning for the next day. This involves updating task boards, setting priorities, and making sure that everything is in place for a smooth start the next morning.

Evening: Continuous Learning and Relaxation

6:00 PM Onwards: Continuous Learning and Personal Time 

After a productive day, it’s important to unwind and relax. However, the learning never stops. Many application owners spend their evenings reading up on the latest industry trends, taking online courses, or experimenting with new tools and technologies.

Being an application owner is a challenging yet rewarding role. It requires a balance of technical skills, strategic thinking, and effective communication. Every day brings new challenges, opportunities, and rewards, making it an exciting career for those who love to innovate and drive change.

If you need help managing your applications, Actian Application Services can help. 

>> Learn More

The post A Day in the Life of an Application Owner appeared first on Actian.


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Author: Nick Johnson

Actian’s Interns Contribute Across All Areas of the Business

As we wrap up our internship season, I want to reflect on the brilliance of this program. It’s been a great experience so far and like the other interns, I’m impressed with how much I’m learning and the opportunities to actively contribute to the business. From collaborating on real-world projects to brainstorming innovative solutions, our intern team is making tangible impacts that help drive the company forward.

Since I came on board in June, my first three impressions are what I refer to as “The Three Cs.” They consist of community, culture, and capstone projects. I am incredibly grateful that these foundational pillars are integral to the distinctive character of the program. Actian’s internship is truly structured to move its participants from interns to capable, confident employees who will be ready for the next stage of our careers.

 Experiencing a Sense of Community

Given the remote nature of my internship—I’m based in Illinois—I was initially unsure how I would be able to connect with my fellow interns and Actian employees. To my relief, when we attended the in-person orientation at the Round Rock Center of Excellence in Texas, it became abundantly clear that despite the mostly remote work environment, Actian cultivates a supportive community of employees who not only care for the success of the company, but for one another, regardless of where we’re working.

It was extremely encouraging to have such incredible support from so many individuals within the company. Every employee with whom I’ve interacted has invited me to connect with them.

Without exception, they genuinely want to see us succeed and have provided us with the individual investment, tools, and resources to do so. This strong sense of community fosters collaboration and ensures that we all thrive together. As an intern, I feel like I’m part of a team that’s making a difference in the company. 

Participating in a Culture Worth Celebrating

Every Actian employee I’ve spoken to has genuine praise for the company’s incredible people and culture. Given this fact, it is no surprise that this positive culture extends to interns as well. During our in-person orientation, interns were able to meet each other face-to-face and engage in activities that allowed us to connect with one another.

This allowed us to get to know each other on a personal and a professional level. Whether it was the group dinners or the cohort favorite “GameOn! ATX” competition—for which I would like to extend a humble apology and thanks to my team’s opponents for continuing to be gracious following their loss!—we were able to share some incredibly fun memories.

Although we have all returned to our various work environments, including remote locations, thanks to the brilliant design of Employee Experience leaders Rae Coffman and Sara Lou, we are fortunate to have a continuing calendar of upcoming fun events. This allows us to interact and share, regardless of where we’re located or what team we’re working with at Actian.

Personally, I’m looking forward to the mini campfire. For this annual Actian intern tradition, each of us is sent supplies to build a candle campfire in their home. The supplies are complete with ingredients to build s’mores, which we’ll eat while we share scary stories with each other. Eek!

This is one example of how the recognizable culture that Actian cultivates globally is scaled to the internship program. The culture ensures that each intern feels seen, supported, and connected throughout the entirety of our experience with Actian.

Delivering Powerful Results With Capstone Projects

There tends to be a cliché that an intern’s only tasks are those that are miniscule to the company. You know, making copies or running errands. That’s certainly not the case here. No Actian intern will ever find themselves simply fetching their manager a cup of coffee. Instead, we are all given a unique opportunity to learn and showcase our hard work.

Each intern is assigned a capstone project at the beginning of our 12 weeks. We work on it, collaborate with others in the company, and ultimately deliver a structured, substantive outcome at the completion of the internship.

We are each given a team consisting of our manager and a buddy who create a reliable balance of support and autonomy as we work toward our project—honing our skills while adding value to the organization. Although I do make a mean cup of coffee, I am more excited about the project management skills and transferable, real-world experiences these capstone projects afford each one of us.

Our Unique Internship Opportunities Extend Globally

The brilliance of our internship program is not limited to inside the U.S. borders. Actian has an incredible cohort of interns working in Germany as well—and they hail from various parts of the globe. One difference between the U.S. and the German program is that those interning in Germany have the ability to be hired at any time of the year. Actian provides these interns with incredible opportunities that include an internship, academic thesis supervision, or a part-time position.

In the last year alone, the Actian office in Germany has supervised 11 students. This includes three academic thesis students and one who will be joining Actian full time this fall. It’s exciting for everyone involved in the program!

Coming from all levels of education and diverse experiences, these interns work on the Actian Vector team under the leadership of Steffen Kläbe and Thomas Schweser to contribute to the success of one of the industry’s fastest analytical database management systems. These interns start their program by completing an extensive onboarding experience that introduces them to the codebase and explains how to successfully optimize it.

Following the completion of these first one to two weeks, interns are assigned a task designed to provide hands-on experience with the codebase system. This task usually entails solving a problem or something similar that delivers actual business value, such as fixing a bug in the code. The initial task allows interns to not only advance their skillset but also gain the confidence needed to move into their selected projects.

Following this task comes the fun part! Interns choose a project that aligns with their interests. So far this year, there have been 17 projects that directly influence the current usage and future innovation of our Vector database. These projects range from “Memory Loading” to “Cloud Usage” to “Data Compression.”

The impact that these interns and projects have on the company is not only recognizably impressive but also incredibly powerful. Their dedication and innovation that they bring to the company every day continues to demonstrate a significant impact that advances our products and our business.

Making a Lasting Impression

Overall, the brilliance of the Actian internship program continues to reveal itself the more I experience it. I am extremely grateful for the opportunity to be here. I am certain that this experience will be one I carry on far longer than my 12 weeks here. Thank you to everyone who makes it possible!

The post Actian’s Interns Contribute Across All Areas of the Business appeared first on Actian.


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Author: Katie Keith

Essential Skills for Data Engineers in the Age of AI


If you work in data, then AI is everywhere at this point.  But whether AI is hype or reality doesn’t change the fact that data engineers will play a major role in ensuring that the data sets that are utilized for the growing use cases are usable both by machines and humans. Whether that data…
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The post Essential Skills for Data Engineers in the Age of AI appeared first on Seattle Data Guy.


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Author: research@theseattledataguy.com

Air Travel is a miserable experience these days


I have done a fair bit of travel in the past six months both in and out of North America, across Asia and in and out of Europe. The carriers have included Alaska Airlines, AirAsia, British Airways, Finnair, Ryanair, Scoot, Singapore Airlines and United Airlines. I have also booked flights for others on British Airways with an Aer Lingus codeshare and Icelandair.

I have to say that with pretty much all of them, the experience of online booking and then checking in has been pretty awful and the costs of the tickets pretty steep. In terms of value for money, I am frustrated. In terms of how the value has declined further since COVID during which many airlines received governmental survival incentives and gouged the few remaining passengers, I am even more disappointed.

My earliest recollection of air travel was in the mid 1970s, a Vickers Viscount, the plane’s top cruising speed was about 500kph at an altitude of 25,000ft. I know these things because in-flight, as a youth, I was allowed into the cockpit to see the plane on a night run while it was in flight. Seating was a 3 + 2, with a single aisle running down the plane. The plane had gigantic oval porthole windows.

Airline tickets were a waxy carbonized booklet, often typed up with the details and the boarding pass was according to souvenir evidence, hand written. You were required to confirm your flight the day before departure by calling the airline and at check-in, luggage was weighed on a beam scale. With the country and era in which I was traveling, passengers had to explicitly identify their luggage on the tarmac before it was loaded into the hold for safety reasons. Family members stood on an open balcony at in the airport building and waved to you as you walked across the tarmac and climbed the stairs.

My next freshest memory is my first long haul flight to London in 1980; mostly unremarkable, aside from the fact that we left late at night and seemingly arrived in the morning despite flying for what seemed like a whole day. This journey was on a significantly more substantial aircraft, a Boeing 707 which flew into London Gatwick. The flight was not particularly memorable aside from the fact that I distinctly remember the plane had a smoking section!

Air travel was a luxury for many at least up until the late 1980’s. For us, tickets were bought by my parents on a layaway and planned as far as up to a year in advance. But these days, flying somewhere even only for a couple of hours is pretty much available to a broad swathe of people and is certainly not a luxurious experience. If you think commercial air travel is glamorous, you should think again.

The Golden Age of Air Travel

Post WWII Airlines competed to provide exceptional service, and passengers were treated to a level of comfort and luxury that seemingly has become a distant memory to all except those flying for obscene amounts of money.

Flying was an experience savoured, marked by exotic meals served on fine china, attentive cabin crew, and spacious seating. Passengers often dressed-up, adding to the atmosphere of sophistication and excitement.

Flying was an exotic experience accompanied by arrival at some far flung destination. The travellers would board these flying machines with a sense of anticipation, ready to enjoy the amenities that came as a part of their ticket. Long haul flights provided a collective cinema experience, passengers, especially the younger ones, were given keepsakes, games, puzzles, crayons etc and music or audio programming was piped to every seat. The experience was designed to make passengers feel some kind of privilege, a far cry from today’s reality.

The Shift in Airline Economics

As the airline industry evolved, so did its economic landscape. The deregulation of the airline industry in the late 1970s in the United States marked a significant turning point. It led to increased competition among airlines, which ultimately drove ticket prices down. While this made air travel more accessible to the general public, it also set the stage for a shift in how airlines operated. US Jimmy Carter Airline Deregulation Act, signed in 1978 witnessed the cost of air travel going down accompanied by a decline in the quality of service. Other regions would soon follow suit.

To remain competitive, airlines began to adopt cost-cutting measures that would fundamentally change the passenger experience. The focus shifted from providing an exceptional journey to maximizing profits. As a result, many of the amenities that once defined air travel were eliminated or reduced. The once-coveted in-flight meals were replaced by military rations-like snack boxes. And complimentary beverages have all but evaporated.

The Decline of Comfort and Service

Today, the experience of flying is characterized by discomfort and a complete lack of personal service.

Airlines have crammed more seats into aircraft, this has led to reduced seat pitch, reduced legroom and narrower aisles as described in the WSJ article The Incredible Shrinking Plane Seat .

An average economy class seat now offers less space than it did decades ago, seat width is down as much as four inches over the last 30 years. Seat pitch has shrunk from about 35 inches to 31 and in some cases as little as 28 inches – on some airlines, seats have NO Pitch at all — allowing airlines to add more seats they can then sell.

Many passengers now find themselves wedged between strangers for hours on end. The once spacious cabins have become cramped, and any sense of personal space has effectively been dissolved.

In-flight service has also suffered. Cabin crew are often stretched thin, serving hundreds of passengers with limited resources. The personal touch that once defined air travel has been replaced by a more transactional approach. Passengers are now often treated as numbers rather than individuals, leading to a sense of impersonal service.

The Rise of Low-Cost Carriers

The emergence of low-cost carriers has further exacerbated the decline of air travel glamour. Airlines such as Ryanair and EasyJet, WhizzAir, Scoot, AirAsia and JET have revolutionized the industry by offering significantly lower fares. However, this has come at a cost. Passengers are now faced with a plethora of additional fees for services that were once included in the ticket price.

Additional fees are now charged for checked bags, carry-on bags, seat selection, paper boarding passes, and in-flight refreshments like water, tea and coffee, all add up quickly, turning what initially appears to be a bargain into something much more expensive.

The low-cost model has led to a homogenization of the flying experience. Passengers are herded like cattle, boarding and disembarking in an industrial flow that prioritizes efficiency over comfort.

The thrill of flying has been replaced by a long list of anxiety creating circumstances including, worries about overweight luggage, getting a middle seat allocation, being unable to find overhead stowage, having to arrive hours ahead of departure, inadequate lounge seating, being unsure about whether the plane will leave on time, not being able to pay for anything with cash, ungodly departure and landing times, inconveniently located airports, crowded terminals and long and arduous security lines.

The Impact on Passenger Experience

The cumulative effect of these changes has been a significant shift in how passengers perceive air travel. While flying is now more accessible, the magic and excitement of the journey is now simply not there. Travellers approach air travel with a sense of dread rather than anticipation. The stress of getting to the airport, navigating security, and enduring cramped seating has overshadowed the joy of reaching a new destination.

There is a general lack of amenities and the service is highly impersonal. Surveys indicate that a significant percentage of travellers feel that the in-flight experience has deteriorated over the years. The once-coveted experience of enjoying a meal at 30,000 feet has been replaced by the reality of overpriced snacks and limited F&B options.

The rise of technology has not necessarily improved the passenger experience. While online check-in and mobile boarding passes have streamlined some processes, they have also contributed to a more transactional relationship between airlines and passengers. The human touch that once characterized air travel has been replaced by automated systems and self-service kiosks, not all of which are available or functioning, queues everywhere and the proverbial cattle station handling experience that passengers are subjected to, at every stage.

Cancel your ticket or have it cancelled for you, and you have no guarantees of a full refund or restitution or compensation. Instead the industry has spawned a whole world of travel insurance, and reinsurance with middle men and brokers selling you tickets, selling you travel protection and everything in between.

Image Credit :Shutterstock

Nostalgia for the Past

For me at least, the nostalgia for the golden age of air travel is palpable. I reminisce about the days when flying was truly an event, marked by some special novelty and excitement. The memory of being served a nice meal, spacious seating, and some level of personalized attention from flight attendants even in coach/economy class. It evokes in me, a sense of longing for a bygone era that I can only experience again if I am prepared to pay a massive premium.

The nostalgia is not just about comfort in flying; it’s a desire for the experience of non utilitarian travel itself. The thrill of embarking on an adventure, the anticipation of exploring new geography, cultures, and the joy of connecting with fellow passengers. It has all been overshadowed by an altogether more stressful modern air travel experience.

Looking Ahead: The Future of Air Travel

The airline industry may evolve further, balancing cost-cutting measures with passenger expectations may make low fares and air travel more accessible but there’s a growing demand for improved service and comfort. The airlines that can find a way to enhance the passenger experience while maintaining competitive pricing may stand out in an increasingly crowded market.

Improved in-flight entertainment systems won’t cut it, in fact some airlines are cutting back on these too. Enhanced seating designs might help, but not if the designs continue to shrink personal space and add discomfort.

Improved customer service training could help restore some of the lost glamour of flying but a renewed focus on customer satisfaction and personalized service is what is really needed in order for airlines to regain the trust and loyalty of veteran travellers.


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Author: Clinton Jones

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