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AI Predictions for 2025: Embracing the Future of Human and Machine Collaboration


Predictions are funny things. They often seem like a bold gamble, almost like trying to peer into the future with the confidence we inherently lack as humans. Technologyā€™s rapid advancement surprises even the most seasoned experts, especially when it progresses exponentially, as it often does. As physicist Albert A. Bartlett famously said, ā€œThe greatest shortcoming [ā€¦]

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Author: Philip Miller

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?
I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, Iā€™ve seen a trend ā€” more and more emphasis on AI.Ā Ā  Iā€™ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of [ā€¦]


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Author: Randall Gordon

Book of the Month: ā€œAI Governance Comprehensiveā€


Welcome to December 2024ā€™s ā€œBook of the Monthā€ column. This month, weā€™re featuring ā€œAI Governance Comprehensive: Tools, Vendors, Controls, and Regulationsā€ by Sunil Soares, available for free download on the YourDataConnect (YDC) website.Ā  This book offers readers a strong foundation in AI governance. While the emergence of generative AI (GenAI) has brought AI governance to [ā€¦]

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Author: Mark Horseman

Why GenAI Wonā€™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, [ā€¦]

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Author: Itamar Ben Hemo

Exploring the Fundamental Truths of Generative AI

In recent years, Generative AI has emerged as a revolutionary force in artificial intelligence, providing businesses and individuals with groundbreaking tools to create new data and content.

So, what exactly is Generative AI? The concept refers to a type of artificial intelligence that is designed to generate new content rather than simply analyze or classify existing data. It leverages complex machine learning models to create outputs such as text, images, music, code, and even video by learning patterns from vast datasets.

Generative AI systems, like large language models (LLMs), use sophisticated algorithms to understand context, style, and structure. They can then apply this understanding to craft human-like responses, create art, or solve complex problems. These models are trained on enormous amounts of data, allowing them to capture nuanced patterns and relationships. As a result, they can produce outputs that are often indistinguishable from human-created contentā€“and do it in a fraction of the time as humans.

The following survey conducted by TDWI shows that utilizing Generative AI is a major priority for companies in 2024. It ranks alongside other top initiatives like machine learning and upskilling business analysts, indicating that businesses are keen to explore and implement Generative AI technologies to enhance their analytics capabilities.

tdwi graph for analytics

Given that high level of priority, understanding five core truths around Generative AI helps to demystify its capabilities and limitations while showcasing its transformative potential:

  1. Generative AI Uses Predictions to Generate Data

At its core, Generative AI leverages predictions made by deep learning algorithms to generate new data, as opposed to traditional AI models that use data to make predictions. This inversion of function makes Generative AI unique and powerful, capable of producing realistic images, coherent text, audio, or even entire datasets that have never existed before.

Example: Consider Generative Pre-trained Transformer, better known as GPT, models that predict the next word in a sentence based on the preceding words. With each prediction, these models generate fluid, human-like text, enabling applications like chatbots, content creation, and even creative writing. This capability is a radical shift from how traditional AI models simply analyze existing data to make decisions or classifications.

Why It Matters: The ability to generate data through predictive modeling opens the door to creative applications, simulation environments, and even artistic endeavors that were previously unimaginable in the AI world.

  1. Generative AI is Built on Deep Learning Foundations

Generative AI stands on the shoulders of well-established deep learning algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models like GPT. These frameworks power the generation of realistic images, text, and other forms of content.

    • GANs: Used extensively for creating high-quality images, GANs pit two networks against each otherā€”a generator and a discriminator. The generator creates images, while the discriminator judges their quality, gradually improving the output.
    • VAEs: These models enable the creation of entirely new data points by understanding the distribution of the data itself, often used in generative tasks involving audio and text.
    • Transformers (GPT): The backbone of LLMs, transformers utilize self-attention mechanisms to handle large-scale text generation with impressive accuracy and fluency.

Why It Matters: These deep learning foundations provide the generative power to these models, enabling them to create diverse types of outputs. Understanding these algorithms also helps developers and AI enthusiasts choose the right architecture for their Generative AI tasks, whether for generating art, music, text, or something entirely different.

  1. Generative AI Stands Out in Conversational Use Cases

A key strength of Generative AI is in applications where humans interact conversationally with AI systems. This differs from traditional AI and machine learning applications, which typically stand out in scenarios where the system is making decisions on behalf of humans. In Generative AI, dialogue-driven interactions come to the forefront.

Example: Chatbots powered by GPT models can converse with users in natural language, answering questions, providing recommendations, or even assisting in customer service. These models shine in areas where continuous interaction with users is essential for delivering valuable outputs.

Why It Matters: The conversational capability of Generative AI redefines user experiences. Instead of using structured, predefined outputs, users can ask open-ended questions and get context-aware responses, which makes interactions with machines feel more fluid and human-like. This represents a monumental leap in fields like customer service, education, and entertainment, where AI needs to respond dynamically to human inputs.

  1. Generative AI Fosters ā€˜Conversations with Dataā€™

One of the most exciting developments in Generative AI is its ability to let users have ā€œconversations with data.ā€ Through Generative AI, even non-technical users can interact with complex datasets and receive natural-language responses based on the data.

Example: Imagine a business analyst querying a vast dataset: Instead of writing SQL queries, the analyst simply asks questions in plain language (e.g., ā€œWhat were the sales in Q3 last year?ā€). The generative model processes the query and produces accurate, data-driven answersā€”making analytics more accessible and democratized.

Why It Matters: By lowering the barrier to entry for data analysis, Generative AI makes it easier for non-technical users to extract insights from data. This democratization is a huge leap forward in industries like finance, healthcare, and logistics, where data-driven decisions are crucial, but data skills may be limited.

  1. Generative AI Facilitates ā€˜Conversations with Documentsā€™

Another pivotal truth about Generative AI is its capacity to facilitate ā€œconversations with documents,ā€ allowing users to access knowledge stored in vast repositories of text. Generative AI systems can summarize documents, answer questions, and even pull relevant sections from large bodies of text in response to specific queries.

Example: In a legal setting, a lawyer could use a Generative AI system to analyze large case files. Instead of manually combing through hundreds of pages, the lawyer could ask Generative AI to summarize key rulings, precedents, or legal interpretations, greatly speeding up research and decision-making.

Why It Matters: In industries where professionals deal with large amounts of documentationā€”such as law, medicine, or academiaā€”the ability to have a ā€œconversationā€ with documents saves valuable time and resources. By providing context-aware insights from documents, Generative AI helps users find specific information without wading through reams of text.

Changing How We Interact with Technology

These truths about Generative AI shed some light on the capabilities and potential of this groundbreaking technology. By generating data through predictions, leveraging deep learning foundations, and enabling conversational interactions with both data and documents, Generative AI is reshaping how businesses and individuals interact with technology.

As we continue to push the boundaries of Generative AI, it is crucial to understand how these truths will shape future applications, driving innovation across industries. Whether organizations are building chatbots, analyzing data, or interacting with complex documents, Generative AI stands as a versatile and powerful tool in the modern AI toolbox. To make sure an organizationā€™s data is ready for Generative AI, get our checklist.

The post Exploring the Fundamental Truths of Generative AI appeared first on Actian.


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Author: Steven B. Becker

Data vs. AI Literacy: Why Both Are Key When Driving Innovation and Transformation


I have written before about theĀ 5Ws of dataĀ and how important metadata ā€“ data about data ā€“ really is. This knowledge helps connect and contextualize data in ways that previously would take hours of knowledge and information mining.Ā We have the tools now to automate this process and display it in a knowledge model of the data, [ā€¦]

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Author: Philip Miller

Unstructured Data Hinders Safe GenAI Deployment


Enterprises are going all in on generative AI (GenAI), with the technology driving aĀ massive 8% increase in worldwide IT spending this year, according to Gartner. But just because businesses are investing in GenAI doesnā€™t mean theyā€™re broadly implementing it in actual production. Organizations are eager to wield the power of GenAI. However, deploying it safely [ā€¦]

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Author: Rehan Jalil

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.Ā  Instead, in the U.S., there [ā€¦]


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Author: William A. Tanenbaum

Answering the Build vs. Buy Question for Generative AI


Building custom generative AI (GenAI) technology solutions is the best way to gain a competitive edge by leveraging GenAI tools and services tailored to your business. On the other hand, building GenAI models from scratch is a costly and complicated endeavor ā€“ which is why many businesses instead settle for a genAI strategy wherein they [ā€¦]

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Author: Daniel Avancini

Generative AI Is Accelerating Data Pipeline Management


Data pipelines are like insurance. You only know they exist when something goes wrong. ETL processes are constantly toiling away behind the scenes, doing heavy lifting to connect the sources of data from the real world with the warehouses and lakes that make the data useful. Products like DBT and AirTran demonstrate the repeatability and [ā€¦]

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Author: Mike Finley

Data Backup Essentials: Zero Trust Strategies for System Administrators


Time is always of the essence in the case of system administration and IT operations teams. They address the countless issues coming in from other departments: ā€œMy network is acting funky, and Iā€™m about to go on a meeting with an important client,ā€ ā€œI accidentally deleted every important email I have ever received from the [ā€¦]

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Author: Anthony Cusimano

The Data Governance Wake-Up Call From the OpenAI Breach


Shockwaves reverberated throughout the political and tech ecosystems this summer when OpenAI ā€“ the creator of ChatGPT ā€“Ā admitted it had been breached. The breach, which involved an outsider gaining access to internal messaging systems, left many worried that a national adversary could do the same and potentially weaponize generative AI technologies. National security aside, the [ā€¦]

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Author: Jessica Smith

The Secret to RAG Optimization: Expert Human Intervention


As the use of generative AI (GenAI) grows exponentially, developers have turned their attention to improving the technology. According to EMARKETER, nearlyĀ 117 million peopleĀ in the U.S. are expected to use GenAI in 2025, a 1,400% increase over just 7.8 million users in 2022. More demand means more scrutiny and increased demand for higher-quality products, and [ā€¦]

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Author: Christopher Stephens

5 Misconceptions About Data Quality and Governance

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

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 ā€œgarbage in, garbage outā€ has never been more relevant. For data and IT professionals, understanding the critical role of data quality in these applications is not just importantā€”itā€™s imperative for success.

Going Beyond Data Processing

Advanced analytics and generative AI donā€™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 lensesā€”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 checkboxā€”itā€™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, thereā€™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.Ā 

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

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 Dataā€™s Value

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

Misconception 1: The ā€˜Set It and Forget Itā€™ 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.Ā 

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.Ā 

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 ā€˜Technology Will Save Usā€™ Trap

Thereā€™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, itā€™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 organizationā€™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 ā€˜Perfect Dataā€™ 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, ā€œgood enoughā€ 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 ā€˜Compliance is Enoughā€™ 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 ā€˜IT Departmentā€™s Problemā€™ Delusion

Thereā€™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, itā€™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 isnā€™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

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, weā€™ll explore the pivotal role data plays in democratizing AI and how Actianā€™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 usersā€”whether theyā€™re analysts at small businesses, individual developers, or even those without technical backgrounds. Itā€™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.

Actianā€™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: Actianā€™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.

Actianā€™s Role in Democratizing AI

Actianā€™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 Actianā€™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 dataā€”a fundamental step in AI applications.
  3. Real-time Data Analytics: Actianā€™s solutions support real-time data analytics, crucial for AI applications that require immediate decisions or predictions.
  4. Hybrid and Multi-Cloud Support: Actianā€™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 Actianā€™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 Actianā€™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: Actianā€™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. Actianā€™s DataConnectā€™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. Actianā€™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, thereā€™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.

Actianā€™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, itā€™s essential to prioritize responsible development practices, ensuring that AI systems are fair, transparent, and beneficial to society. With Actianā€™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

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 [ā€¦]

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Author: Gaurav Belani

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 ā€“ for example, McKinseyĀ predictsĀ 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 [ā€¦]

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Author: Dom Couldwell

Why Effective Data Management is Key to Meeting Rising GenAI Demands


OpenAIā€™s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, itā€™s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized. While the possibilities are [ā€¦]

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Author: Matt McDonough

The Rising Importance of AI Governance
AI governance has become a critical topic in todayā€™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 [ā€¦]


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Author: Myles Suer

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 [ā€¦]

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Author: Sanmugam Aravinthan

Data Retention Policies Must Evolve to Address Emerging Technologies and Data Growth


The emergence of new technologies, including AI, IoT, and blockchain, in addition to the widespread embrace of digital transformation, has driven a dramatic increase in data. The reliance on data analytics to drive data-driven decision-making also requires large volumes of data for meaningful insights. While AI and generative AI (GenAI) tools and systems contribute to [ā€¦]

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Author: Fredrik Forslund

Your Company is Ready for Gen AI. But is Your Data?

The buzz around Generative AI (Gen AI) is palpable, and for good reason. This powerful technology promises to revolutionize how businesses like yours operate, innovate, and engage with customers. From creating compelling marketing content to developing new product designs, the potential applications of Gen AI are vast and transformative. But hereā€™s the kicker: to unlock these benefits, your data needs to be in tip-top shape. Yes, your company might be ready for Gen AI, but the real question isā€”are your data and data preparation up to the mark? Letā€™s delve into why data preparation and quality are the linchpins for Gen AI success.

Ā 

The Foundation: Data Preparation

Think of Gen AI as a master chef. No matter how skilled the chef is, the quality of the dish hinges on the ingredients. In the realm of Gen AI, data is the primary ingredient. Just as a chef needs fresh, high-quality ingredients to create a gourmet meal, Gen AI needs well-prepared, high-quality data to generate meaningful and accurate outputs.

Garbage In, Garbage Out

Thereā€™s a well-known adage in the data world: ā€œGarbage in, garbage out.ā€ This means that if your Gen AI models are fed poor-quality data, the insights and outputs they generate will be equally flawed. Data preparation involves cleaning, transforming, and organizing raw data into a format suitable for analysis. This step is crucial for several reasons:

Accuracy

Ensuring data is accurate prevents AI models from learning incorrect patterns or making erroneous predictions.

Consistency

Standardizing data formats and removing duplicates ensure that the AI modelā€™s learning process is not disrupted by inconsistencies.

Completeness

Filling in missing values and ensuring comprehensive data coverage allows AI to make more informed and holistic predictions.

The Keystone: Data Quality

Imagine youā€™ve meticulously prepared your ingredients, but theyā€™re of subpar quality. The dish, despite all your efforts, will be a disappointment. Similarly, even with excellent data preparation, the quality of your data is paramount. High-quality data is relevant, timely, and trustworthy. Hereā€™s why data quality is non-negotiable for Gen AI success:

Relevance

Your Gen AI models need data that is pertinent to the task at hand. Irrelevant data can lead to noise and outliers, causing the model to learn patterns that are not useful or, worse, misleading. For example, if youā€™re developing a Gen AI model to create personalized marketing campaigns, data on customer purchase history, preferences, and behavior is crucial. Data on their shoe size? Not so much.

Timeliness

Gen AI thrives on the latest data. Outdated information can result in models that are out of sync with current trends and realities. For instance, using last yearā€™s market data to generate this yearā€™s marketing strategies can lead to significant misalignment with the current market demands and changing consumer behavior.

Trustworthiness

Trustworthy data is free from errors and biases. Itā€™s about having confidence that your data reflects the true state of affairs. Biases in data can lead to biased AI models, which can have far-reaching negative consequences. For example, if historical hiring data used to train an AI model contains gender bias, the model might perpetuate these biases in future hiring recommendations.

Real-World Implications

Letā€™s put this into perspective with some real-world scenarios:

Marketing and Personalization

A retail company leveraging Gen AI to create personalized marketing campaigns can see a substantial boost in customer engagement and sales. However, if the customer data is riddled with inaccuraciesā€”wrong contact details, outdated purchase history, or incorrect preferencesā€”the generated content will miss the mark, leading to disengagement and potentially damaging the brandā€™s reputation.

Product Development

In product development, Gen AI can accelerate the creation of innovative designs and prototypes. But if the input data regarding customer needs, market trends, and existing product performance is incomplete or outdated, the resulting designs may not meet current market demands or customer needs, leading to wasted resources and missed opportunities.

Healthcare and Diagnostics

In healthcare, Gen AI has the potential to revolutionize diagnostics and personalized treatment plans. However, this requires precise, up-to-date, and comprehensive patient data. Inaccurate or incomplete medical records can lead to incorrect diagnoses and treatment recommendations, posing significant risks to patient health.

The Path Forward: Investing in Data Readiness

To truly harness the power of Gen AI, you must prioritize data readiness. Hereā€™s how to get started:

Data Audits

Conduct regular data audits to assess the current state of your data. Identify gaps, inconsistencies, and areas for improvement. This process should be ongoing to ensure continuous data quality and relevance.

Data Governance

Implement robust data governance frameworks that define data standards, policies, and procedures. This ensures that data is managed consistently and remains high-quality across the organization.

Advanced Data Preparation Tools

Leverage advanced data preparation tools that automate the cleaning, transformation, and integration of data. These tools can significantly reduce the time and effort required to prepare data, allowing your team to focus on strategic analysis and decision-making.

Training and Culture

Foster a culture that values data quality and literacy. Train employees on the importance of data integrity and equip them with the skills to handle data effectively. This cultural shift ensures that everyone in the organization understands and contributes to maintaining high data standards.

The Symbiosis of Data and Gen AI

Gen AI holds immense potential to drive innovation and efficiency across various business domains. However, the success of these initiatives hinges on the quality and preparation of the underlying data. As the saying goes, ā€œA chain is only as strong as its weakest link.ā€ In the context of Gen AI, the weakest link is often poor data quality and preparation.

By investing in robust data preparation processes and ensuring high data quality, you can unlock the full potential of Gen AI. This symbiosis between data and AI will not only lead to more accurate and meaningful insights but also drive sustainable competitive advantage in the rapidly evolving digital landscape.

So, your company is ready for Gen AI. But the million-dollar question remainsā€”is your data?

Download our free Gen AI Data Readiness Checklist shared at the Gartner Data & Analytics Summit.

The post Your Company is Ready for Gen AI. But is Your Data? appeared first on Actian.


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