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May 3 2024

Now Is the Time for Executives to Deploy Ethical Rules Around AI

For better or worse, AI is causing disruption in almost every field imaginable. Corporations around the world are embracing its possibilities to make work more efficient. The success of ChatGPT and other generative AI tools has also caught the attention of nearly every industry in an effort to meet profitability, efficiency, and sustainability goals. Money […]

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Six Data Quality Dimensions to Get Your Data AI-Ready

If you look at Google Trends, you’ll see that the explosion of searches for generative AI (GenAI) and large language models correlates with the introduction of ChatGPT back in November 2022. GenAI has brought hope and promise for those who have the creativity and innovation to dream big, and many have formulated impressive and pioneering […]

The Rise of Augmented Analytics: Combining AI with BI for Enhanced Data Insights

Businesses today are drowning in data. The sheer volume and complexity of information available have made it increasingly difficult for organizations to extract meaningful insights using traditional business intelligence (BI) tools and the expertise of specialized data scientists. This is where augmented analytics comes in. This game-changing technology combines the power of artificial intelligence (AI) […]

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Data Labeling Challenges and Solutions

In the rush to adopt AI across diverse sectors, today’s enterprises face a common hurdle: efficient data labeling at scale. Numerous enterprises are grappling with generating usable data despite having huge amounts of raw information. Organizations are overwhelmed by the influx of image data, highlighting the need to process and label it for practical use. Data […]

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Human-AI Collaboration: How AI Can Enhance Human Capabilities and Ethical Considerations

Artificial intelligence is here to change the world, and it is up to us to embrace this technology and use it responsibly to reap its full potential. Although critics have expressed some valid concerns about the potential harm that AI technology could bring about, fostering an AI-human collaboration can allow us to use this powerful […]

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Preparing for La Niña: Adopting Predictive Maintenance Before Hurricane Season

With a La Niña watch issued for the summer, businesses operating in hurricane-prone regions face heightened concerns about the impending storm season. La Niña heavily impacts the wind shear and atmospheric conditions over the Atlantic, where most hurricanes form thanks to its warm waters. It’s rare to go a year without a hurricane hitting some part of […]

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How AI Can Improve Company Performance Through Better DEX

In tech circles these days, one topic drives every conversation: artificial intelligence (AI). Whether discussing the potential benefits of increased productivity or the potential risks, the role of AI in business is on the minds of everyone, from C-suite leaders to recent college graduate hires. AI is a hot topic because while people can see […]

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Data-Driven Defense: AI as the New Frontier in Business Security

Major business setbacks due to risk management failures happen every year. They are also some of the costliest, adding up to millions of dollars in regulatory fines, lawsuits, payouts, and lost brand value. Leaders want to avoid these types of issues and rely on sound internal data management to mitigate risk and maintain confidence and […]

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Crossing the Data Divide: AI Data Assistants — A Data Leader’s Force Multiplier

The focus of my last column, titled Crossing the Data Divide: Data Catalogs and the Generative AI Wave, was on the impact of large language models (LLM) and generative artificial intelligence (AI) and how we disseminate knowledge throughout the enterprise and the future role of the data catalogs. Spoiler alert if you have not read […]

Ask a Data Ethicist: How Can We Address the Ethics of Reusing Data?

Reusing data is a fundamental part of artificial intelligence and machine learning. Yet, when we collect data for one purpose, and use it for other purposes, we could be crossing both legal and ethical boundaries. How can we address the ethics of reusing data? Understand Your Data Before we address the issue of reuse, we […]

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How Artificial Intelligence Will First Find Its Way Into Mental Health

Artificial intelligence (AI) startup Woebot Health made the news recently for some of its disastrously flawed artificial bot responses to text messages that were sent to it mimicking a mental health crisis. Woebot, which raised $90 million in a Series B round, responded that it is not intended for use during crises. Company leadership woefully […]

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The Drive Toward the Autonomous Enterprise Is a Key Focus for IT Leaders in 2024

According to Gartner, 80% of executives see automation as a vital thread that supports informed business decisions. And they’re right. In today’s business landscape, automation has transcended a mere “nice-to-have” and become a fundamental driver of organizational success. It’s not just transforming tasks but reshaping businesses from the inside out. Enhanced resilience, richer customer experiences, and a sharper […]

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The Best Methodology for Moving AI Data and Keeping It Safe

Artificial intelligence (AI) has the power to change the global economy and potentially, one day, every aspect of our lives. There are numerous possible uses for the technology across industries, and new AI projects and applications are frequently released to the public. The only restriction on AI’s use appears to be the inventiveness of human beings. AI […]

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How to Effectively Prepare Your Data for Gen AI

Many organizations are prioritizing the deployment of generative AI for a number of mission-critical use cases. This isn’t surprising. Everyone seems to be talking about Gen AI, with some companies now moving forward with various applications.

While company leaders may be ready to unleash the power of Gen AI, their data may not be as ready. That’s because a lack of proper data preparation is setting up many organizations for costly and time-consuming setbacks.

However, when approached correctly, proper data prep can help accelerate and enhance Gen AI deployments. That’s why preparing data for Gen AI is essential, just like for other analytics, to avoid the “garbage in, garbage out” principle and to prevent skewed results.

As Actian shared in our presentation at the recent Gartner Data & Analytics Summit, there are both promises and pitfalls when it comes to Gen AI. That’s why you need to be skeptical about the hype and make sure your data is ready to deliver the Gen AI results you’re expecting.

Data Prep is Step One

We noted in our recent news release that comprehensive data preparation is the key to ensuring generative AI applications can do their job effectively and deliver trustworthy results. This is supported by the Gartner “Hype Cycle for Artificial Intelligence, 2023” that says, “Quality data is crucial for generative AI to perform well on specific tasks.”

In addition, Gartner explains that “Many enterprises attempt to tackle AI without considering AI-specific data management issues. The importance of data management in AI is often underestimated, so data management solutions are now being adjusted for AI needs.”

A lack of adequately prepared data is certainly not a new issue. For example, 70% of digital transformation projects fail because of hidden challenges that organizations haven’t thought through, according to McKinsey. This is proving true for Gen AI too—there are a range of challenges many organizations are not thinking about in their rush to deploy a Gen AI solution. One challenge is data quality, which must be addressed before making data available for Gen AI use cases.

What a New Survey Reveals About Gen AI Readiness

To gain insights into companies’ readiness for Gen AI, Actian commissioned research that surveyed 550 organizations in seven countries—70% of respondents were director level or higher. The survey found that Gen AI is being increasingly used for mission-critical use cases:

44% of survey respondents are implementing Gen AI applications today.
24% are just starting and will be implementing it soon.
30% are in the planning or consideration stage.

The majority of respondents trust Gen AI outcomes:

75% say they have a good deal or high degree of trust in the outcomes.
5% say they do not have very much or not much trust in them.

It’s important to note that 75% of those who trust Gen AI outcomes developed that trust based on their use of other Gen AI solutions such as ChatGPT rather than their own deployments. This level of undeserved trust has the potential to lead to problems because users do not fully understand the risk that poor data quality poses to Gen AI outcomes in business.

It’s one issue if ChatGPT makes a typo. It’s quite another issue if business users are turning to Gen AI to write code, audit financial reports, create designs for physical products, or deliver after-visit summaries for patients—these high value use cases do not have a margin for error. It’s not surprising, therefore, that our survey found that 87% of respondents agree that data prep is very or extremely important to Gen AI outcomes.

Use Our Checklist to Ensure Data Readiness

While organizations may have a high degree of confidence in Gen AI, the reality is that their data may not be as ready as they think. As Deloitte notes in “The State of Generative AI in the Enterprise,” organizations may become less confident over time as they gain experience with the larger challenges of deploying generative AI at scale. “In other words, the more they know, the more they might realize how much they don’t know,” according to Deloitte.

This could be why only four percent of people in charge of data readiness say they were ready for Gen AI, according to Gartner’s “We Shape AI, AI Shapes Us: 2023 IT Symposium/Xpo Keynote Insights.” At Actian, we realize there’s a lot of competitive pressure to implement Gen AI now, which can prompt organizations to launch it without thinking through data and approaches carefully.

In our experience at Actian, there are many hidden risks related to navigating and achieving desired outcomes for Gen AI. Addressing these risks requires you to:

Ensure data quality and cleanliness
Monitor the accuracy of training data and machine learning optimization
Identify shifting data sets along with changing use case and business requirements over time
Map and integrate data from outside sources, and bring in unstructured data
Maintain compliance with privacy laws and security issues
Address the human learning curve

Actian can help your organization get your data ready to optimize Gen AI outcomes. We have a “Gen AI Data Readiness Checklist” that includes the results of our survey and also a strategic checklist to get your data prepped. You can also contact us and then our experts will help you find the fastest path to the Gen AI deployment that’s right for your business.

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Creative Ways to Surf Your Data Using Virtual and Augmented Reality

Organizations often struggle with finding nuggets of information buried within their data to achieve their business goals. Technology sometimes comes along to offer some interesting solutions that can bridge that gap for teams that practice good data management hygiene. We’re going to take a look deep into the recesses of creativity and peek at two […]

Legal Issues for Data Professionals: AI Creates Hidden Data and IP Legal Problems

As data has catapulted to a new and valuable business asset class, and as AI is increasingly used in business operations, the use of AI has created hidden data and IP risks. These risks must be identified and then measures must be taken to protect against both a loss of rights and an infringement of […]

The AI Trust Gap: A Psychological Perspective

Only 52% of employees are confident their organization will ensure AI is implemented in a responsible and trustworthy way, according to Workday’s Closing the AI Trust Gap report. Trust will be key to getting employees engaged in the change needed to realize AI’s full potential. In my last post I looked at what can be done from a cultural perspective. In this […]

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The Rise of Generative AI in Insurance

The global market for artificial intelligence (AI) in insurance is predicted to reach nearly $80 billion by 2032, according to Precedence Research. This growth is being driven by the increased adoption of AI within insurance companies, enhancing their operational efficiency, risk management, and customer engagement. Despite widespread integration of AI in the industry today, its full […]

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2024’s Predominant Technology Trend? The Cloud

As AI becomes an integral part of business processes and strategic planning, organizations have increasingly based their data strategies around its capabilities. Businesses are generating more data than ever from sources like IoT sensors, customer transactions, social media, and more; managing and extracting value from this explosion of big data has become a key priority. […]

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7 Ways AI Will Transform Data Storage

The rapid adoption of artificial intelligence and machine learning (AI/ML) over the past year has transformed just about everything – ushering in a new era of innovation and growth the world has never seen. The same goes for data storage, where the technologies’ impact will be transformative, enabling greater business agility that companies need to […]

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AI Could Save Your Data Governance Program, but It’s Unlikely

In the 1980s, there was a flurry of movies about robots coming to imprison or terrorize humanity. Forty years later, almost every business and technology publication seems to have reimagined the army of robots and artificial intelligence as trading their quest for world domination for the exciting world of business processing. It’s unlikely that most […]

Explainable AI: 5 Open-Source Tools You Should Know

Explainable AI refers to ways of ensuring that the results and outputs of artificial intelligence (AI) can be understood by humans. It contrasts with the concept of the “black box” AI, which produces answers with no explanation or understanding of how it arrived at them. Explainable AI tools are software and systems that provide transparency […]

How to Achieve Self-Service Data Transformation for AI and Analytics

Data transformation is the critical step that bridges the gap between raw data and actionable insights. It lays the foundation for strong decision-making and innovation, and helps organizations gain a competitive edge. Traditionally, data transformation was relegated to specialized engineering teams employing complex extract, transform, and load (ETL) processes using highly complex tooling and code. […]

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The AI Trust Gap: A Cultural Perspective

Only 52% of employees are confident their organization will ensure AI is implemented in a responsible and trustworthy way, according to Workday’s Closing the AI Trust Gap report. Trust will be key to getting employees engaged in the change needed to realize AI’s full potential. This post will look at what can be done from a cultural perspective. What Is […]

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How AI Tools Like Conversational Intelligence Improve Healthcare Customer Journeys

According to a recent report, a continuous loop of disruptions impacts 20% of customer interactions in healthcare, with nearly half of these disruptions delaying or preventing patient care. However, organizations using conversational intelligence to listen to and analyze the voice of the customer (VOC) are realizing benefits, citing a 25% increase in first-call resolution rates and […]

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🔴 Red Button Data

🔴 Red Button Data

Category Archives: AI

Data Prep is Step One

What a New Survey Reveals About Gen AI Readiness

Use Our Checklist to Ensure Data Readiness