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Why GenAI Won’t Change the Role of Data Professionals


The recent rise of GenAI has sparked numerous discussions across industries, with many predicting revolutionary changes across a broad range of professional landscapes. While the processes data professionals use and the volume of work they can sustain will change because of GenAI, it will not fundamentally change their roles. Instead, it will enhance their abilities, […]

The post Why GenAI Won’t Change the Role of Data Professionals appeared first on DATAVERSITY.


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Author: Itamar Ben Hemo

The AI Chasm: Bridging the Divide Between Research and Real-World Applications


Imagine a world where AI can accurately predict earthquakes, giving us precious time to save lives. Yet, the same technology struggles to understand essential voice commands through your home assistant during a noisy family dinner. This striking dichotomy between the potential of cutting-edge AI research and its often underwhelming real-world applications underscores a significant yet […]

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Author: Srinivasa Rao Bogireddy

The future of generative AI’s form factor


As artificial intelligence (AI) continues to advance, the form factor of generative AI is evolving rapidly. The concept of “form factor” encompasses the systems, interfaces, and user experiences that allow us to interact with AI. It’s what bridges the gap between complex machine learning models and practical, everyday use cases. Today, the most familiar form […]

The post The future of generative AI’s form factor appeared first on LightsOnData.


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Author: George Firican

Legal Issues for Data Professionals: Current Leading U.S. AI Laws
There is no nationwide federal law in the U.S. that specifically regulates the development, deployment, and use of AI in the private sector. (This contrasts with AI use in U.S. federal agencies, as discussed below.) This absence of such a federal law contrasts with the recently enacted AU AI law.  Instead, in the U.S., there […]


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Author: William A. Tanenbaum

How Data Is Used in Fraud Detection Techniques in Fintech Business


In the rapidly changing world of financial technology (fintech), fraud is a developing area seething with vigor. Digital banking and online financial services are booming every day, bringing with them new techniques for thieves to ply their trade – not ones that can be easily dismissed. Fintech firms must now relentlessly deploy data and artificial intelligence […]

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Author: Harsh Daiya

Fundamentals of AI Automation
Experts predict the AI market will grow from $184 billion in 2024 to $826 billion by 2030. And considering the wide range of use cases for AI tools, that’s not much of a surprise. However, while solutions like ChatGPT continue growing in popularity among everyday users, the most significant potential of artificial intelligence lies in […]


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Author: Sarah Kaminski

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

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 […]

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Author: Claire Gribbin

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

Artificial vs. Augmented Intelligence
Terms like artificial intelligence (AI) and augmented intelligence are often used interchangeably. However, they represent fundamentally different approaches to utilizing technology, especially when it comes to data governance. Understanding these differences is crucial for organizations looking to implement non-invasive and effective data governance frameworks. This article explores the distinctions between artificial intelligence and augmented intelligence, […]


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Author: Robert S. Seiner

The Book Look: AI Governance
Technics Publications has started publishing a line of Data-Driven AI books, and one of the first books in this series is “AI Governance” by Dr. Darryl J Carlton. The goal of the book in one sentence is to enable the reader to gain the knowledge and tools to effectively govern and oversee the use of […]


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Author: Steve Hoberman

Streamlining Your Data Needs for Generative AI


Companies are investing heavily in AI projects as they see huge potential in generative AI. Consultancies have predicted opportunities to reduce costs and improve revenues through deploying generative AI – for example, McKinsey predicts that generative AI could add $2.6 to $4.4 trillion to global productivity. Yet at the same time, AI and analytics projects have historically […]

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Author: Dom Couldwell

The Rising Importance of AI Governance
AI governance has become a critical topic in today’s technological landscape, especially with the rise of AI and GenAI. As CEOs express concerns regarding the potential risks with these technologies, it is important to identify and address the biggest risks. Implementing effective guardrails for AI governance has become a major point of discussion, with a […]


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Author: Myles Suer

Through the Looking Glass: Metaphors, MUNCH, and Large Language Models
“What’s a metaphor?”  Mr. Biergel posed the question one morning to my high school grammar class. Being typical teenagers, we looked at him with blank-eyed stares. We expected that if we waited long enough, he’d write a paragraph-long definition on the blackboard.  “What’s a metaphor?” he repeated.  “A place for cows to graze!”  We groaned. […]


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Author: Randall Gordon

Legal Issues for Data Professionals: Pros and Cons of AI in Healthcare (Part 1)
The use of Artificial Intelligence (AI) in healthcare provides promises, risks, and unintended consequences. This column addresses the evolving AI issues in connection with the following topics: As used in this column, “AI” covers both generative and non-generative AI, with a focus on machine learning as part of non-generative AI. Reducing Administrative Burdens on Physicians  […]


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Author: William A. Tanenbaum

Lost in Translation: Language Gap Holds Back GenAI in Life Sciences Industries


Across industries, organizations continue to seek out a range of use cases in which to deploy advanced intelligence. With the development of generative artificial intelligence (GenAI), various industries are leveraging the technology to process and analyze complex data, identify hidden patterns, automate repetitive tasks and generate creative content. The promise of GenAI is transformative, offering […]

The post Lost in Translation: Language Gap Holds Back GenAI in Life Sciences Industries appeared first on DATAVERSITY.


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Author: Sanmugam Aravinthan

Conversational AI’s Quantum Leap: How RAG Is Enabling Smarter Chatbots


Chatbots were among the first apps that testified to the mainstream adoption of AI and inspired further innovations in the conversational space. Now, it’s time to move on from just responding bots to emphatic companions that further reduce the dependency on human intelligence.  RAG-enabled chatbots are proactive in responding to and addressing queries in real […]

The post Conversational AI’s Quantum Leap: How RAG Is Enabling Smarter Chatbots appeared first on DATAVERSITY.


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Author: Yash Mehta

Revolutionizing Healthcare Through Responsible AI Integration


In late 2023, significant attention was given to building artificial intelligence (AI) algorithms to predict post-surgery complications, surgical risk models, and recovery pathways for patients with surgical needs. This naturally elevated the appropriate debate of whether using AI in this manner would result in hospitals and providers prioritizing revenue from automation over excellence in patient […]

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Author: Archie Mayani

Demystifying AI: What Is AI and What Is Not AI?


In recent months, particularly following the release of ChatGPT, there has been an unprecedented surge in interest surrounding artificial intelligence (AI). This heightened attention spans across a multitude of sectors, including business enterprises, technology companies, venture capital firms, universities, governments, media outlets, and more. As the interest in AI is intensifying, some companies have even […]

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Author: Prashanth Southekal

How a Neuro-Symbolic AI Approach Can Improve Trust in AI Apps


As a cognitive scientist, I’ve been immersed in AI for more than 30 years – specifically in speech and natural language understanding, as well as machine-based learning and rule-based decision-making. Progress in our field is always uneven, unfolding in fits and starts. Those of us in the AI field have witnessed multiple “AI winters” over the […]

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Author: Jans Aasman

Running Generative AI in Production – What Issues Will You Find?


As your data projects evolve, you will face new challenges. For new technology like generative AI, some challenges may just be variations on traditional IT projects like considering availability or distributed computing deployment problems. However, generative AI projects are also going through what Donald Rumsfeld once called the “unknown unknowns” phase, where we are discovering […]

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Author: Dom Couldwell

How AI Tools Can Help Organizations Maximize Their Enterprise Knowledge


AI and machine learning can be revelatory for organizations inundated with a sea of data and grappling to find ways to generate meaningful insights from it. AI tools help identify patterns and trends within large datasets that are often challenging for humans to discern. These tools can be trained to make predictions based on historical […]

The post How AI Tools Can Help Organizations Maximize Their Enterprise Knowledge appeared first on DATAVERSITY.


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Author: AJ Abdallat

How to Assess GenAI’s Impact on Your Business


Since the beginning of 2023, generative AI (GenAI) has quickly made a significant impact across an expanding range of industries and applications. In just over a year since its groundbreaking debut, there’s much to celebrate about GenAI – and even more to still uncover and understand. Today, 79% of employees report at least some exposure to AI, […]

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Author: Madhukar Kumar

The Risk and Promise of AI
Artificial intelligence (AI) is rapidly reshaping our world, influencing everything from the way we work to the way we live. It’s like a double-edged sword, offering incredible potential while also posing significant risks. At the heart of this transformation lies data, the fuel that powers AI systems. How we manage this data can determine whether […]


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Author: Robert S. Seiner

The Analytics Sandwich: Understanding the Business Value of Data and AI
In discussions with data management professionals, conversations often veer toward the technical intricacies of migration to the cloud or algorithm optimization, overshadowing the core business objectives that originally spurred these initiatives. Yet, conversations with chief information officers (CIOs) and chief data officers (CDOs) reveal a relentless pursuit of concrete business value, a metric that determines […]


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Author: Myles Suer