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Dec 12 2024

From Input to Insight: How Quality Data Drives AI and Automation

More and more enterprises are looking to automation and AI to deliver new efficiencies and give their organizations an edge in the market. Data is the engine that powers both automation and AI. But data must be clean and user-friendly for these systems to work effectively and deliver on their promise. Lots of organizations are […]

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Beyond Ownership: Scaling AI with Optimized First-Party Data

Brands, publishers, MarTech vendors, and beyond recently gathered in NYC for Advertising Week and swapped ideas on the future of marketing and advertising. The overarching message from many brands was one we’ve heard before: First-party data is like gold, especially for personalization. But it takes more than “owning” the data to make it valuable. Scale and accuracy […]

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Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database

You never know what’s going to happen when you click on a LinkedIn job posting button. I’m always on the lookout for interesting and impactful projects, and one in particular caught my attention: “Far North Enterprises, a global fabrication and distribution establishment, is looking to modernize a very old data environment.” I clicked the button […]

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5 Data Management Tool and Technology Trends to Watch in 2025

The market surrounding data management tools and technologies is quite mature. After all, the typical business has been making extensive use of data to help streamline its operations and decision-making for years, and many companies have long had data management tools in place. But that doesn’t mean that little is happening in the world of […]

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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, I’ve seen a trend — more and more emphasis on AI. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]

Data Insights Ensure Quality Data and Confident Decisions

Every business (large or small) creates and depends upon data. One hundred years ago, businesses looked to leaders and experts to strategize and to create operational goals. Decisions were based on opinion, guesswork, and a complicated mixture of notes and records reflecting historical results that may or may not be relevant to the future. Today, […]

Technical and Strategic Best Practices for Building Robust Data Platforms

In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for data architecture, […]

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Chatbot Quality Control: Why Data Hygiene Is a Necessity

The rush is on to deploy chatbots. Chatbots rely on data to power their outputs; however, companies that prioritize data quantity over quality risk creating systems that produce unreliable, inappropriate, and simply incorrect responses. Success in this field depends on rigorous data standards and ongoing quality control rather than simply accumulating more training data. When […]

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Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?

In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]

The Art of Lean Governance: A Systems Thinking Approach to Data Governance

A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem. Data Governance is about gaining trust and […]

Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Even industry leaders like Charles Schwab and Citibank have been severely impacted by poor data management, revealing the urgent need for more effective data quality processes across the sector. Key Examples of Data Quality Failures — […]

New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI

Organizations are racing to adopt AI for its promise of efficiency and insights, yet the path to successful AI integration remains fraught with obstacles. Despite advancements in tools like ChatGPT and Google’s Gemini, fundamental issues with data governance – such as high costs, poor data quality, and security concerns – continue to hinder progress. Stop me […]

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Data Crime: Cartoon Signatures

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of  data management to someone.  The Story The state of Rhode […]

AI-Powered Data Preparation: The Key to Unlocking Powerful AI Use Cases

Generative AI (GenAI), specifically as it pertains to the public availability of large language models (LLMs), is a relatively new business tool, so it’s understandable that some might be skeptical of a technology that can generate professional documents or organize data instantly across multiple repositories. On the one hand, AI-powered data preparation sounds too good to be […]

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Key Insights From the ISG Buyers Guide for Data Intelligence 2024

Modern data management requires a variety of technologies and tools to support the people responsible for ensuring that data is trustworthy and secure. Conquering the data challenge has led to a massive number of vendors offering solutions that promise to solve data issues.

With the evolving vendor landscape, it can be difficult to know where to start. It can also be difficult to understand how to determine the best way to evaluate vendors to be sure you’re seeing a true representation of their capabilities—not just sales speak. When it comes to data intelligence, it can be difficult to even define what that means to your business.

With budgets continuously stretched even thinner and new demands placed on data, you need data technologies that meet your needs for performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and a reputation for service you can count on, giving you the confidence to meet current and future needs.

Independent Assessments Are Key to Informing Buying Decisions

Independent analyst reports and buying guides can help you make informed decisions when evaluating and ultimately purchasing software that aligns with your workloads and use cases. The reports offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing jargon to help you understand how technologies truly perform, helping you choose a solution with confidence.

These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity helps you avoid the pitfalls of biased or incomplete information.

For example, the 2024 Buyers Guide for Data Intelligence by ISG Research, which provides authoritative market research and coverage on the business and IT aspects of the software industry, offers insights into several vendors’ products. The guide offers overall scoring of software providers across key categories, such as product experience, capabilities, usability, ROI, and more.

In addition to the overall guide, ISG Research offers multiple buyers guides that focus on specific areas of data intelligence, including data quality and data integration.

ISG Research Market View on Data Intelligence

Data intelligence is a comprehensive approach to managing and leveraging data across your organization. It combines several key components working seamlessly together to provide a holistic view of data assets and facilitate their effective use.

The goal of data intelligence is to empower all users to access and make use of organizational data while ensuring its quality. As ISG Research noted in its Data Quality Buyers Guide, the data quality product category has traditionally been dominated by standalone products focused on assessing quality.

“However, data quality functionality is also an essential component of data intelligence platforms that provide a holistic view of data production and consumption, as well as products that address other aspects of data intelligence, including data governance and master data management,” according to the guide.

Similarly, ISG Research’s Data Integration Buyers Guide notes the importance of bringing together data from all required sources. “Data integration is a fundamental enabler of a data intelligence strategy,” the guide points out.

Companies across all industries are looking for ways to remove barriers to easily access data and enable it to be treated as an important asset that can be consumed across the organization and shared with external partners. To do this effectively and securely, you must consider various capabilities, including data integration, data quality, data catalogs, data lineage, and metadata management solutions.

These capabilities serve as the foundation of data intelligence. They streamline data access and make it easier for teams to consume trusted data for analytics and business intelligence that inform decision making.

ISG Research Criteria for Choosing Data Intelligence Vendors

ISG Research notes that software buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data integration technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to the company.

In the 2024 Data Intelligence Buyers Guide, ISG Research evaluated software and presented findings in key categories that are important to modern businesses. The evaluation offers a framework that allows you to shorten the cycle time when considering and purchasing software.

For example, ISG Research encourages you to follow a process to ensure the best possible outcomes by:

Defining the business case and goals. Understand what you are trying to accomplish to justify the investment. This should include defining the specific needs of people, processes, and technology. Ventana Research, which is part of ISG Research, predicts that through 2026, three-quarters of enterprises will be engaged in data integrity initiatives to increase trust in their data.
Assessing technologies that align with business needs. Based on your business goals, you should determine the technological capabilities needed for success. This will ensure you maximize your technology investments and avoid paying for tools that you may not require. ISG Research notes that “too many capabilities may be a negative if they introduce unnecessary complexity.”
Including people and defining processes. While choosing the right software will help enforce data quality and facilitate getting data to more people across your organization, it’s important to consider the people who need to be involved in defining and maintaining data quality processes.
Evaluating and selecting technology properly. Determine the business and technology approach that best aligns with your requirements. This allows you to create criteria for meeting your needs, which can be used for evaluating technologies.

As ISG Research points out in its buyers guide, all the products it evaluated are feature-rich. However, not all the capabilities offered by a software provider are equally valuable to all types of users or support all business requirements needed to manage products on a continuous basis. That’s why it’s important to choose software based on your specific and unique needs.

Buy With Confidence

It can be difficult to keep up with the fast-changing landscape of data products. Independent analyst reports help by enabling you to make informed decisions with confidence.

Actian is providing complimentary access to the ISG Research Data Quality Buyers Guide that offers a detailed software provider and product assessment. Get your copy to find out why Actian is ranked in the “Exemplary” category.

If you’re looking for a single, unified data platform that offers data integration, data warehousing, data quality, and more at unmatched price-performance, Actian can help. Let’s talk.

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Charting a Course Through the Data Mapping Maze in Three Parts

Companies are dealing with more data sources than ever – sales figures, customer profiles, inventory updates, you name it. Data professionals say, on average, data volumes are growing by 63% per month in their organizations. Data teams are struggling to ensure all that data hangs together across systems and is accurate and consistent. Bad data is bad […]

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Data Quality: The Hidden Cornerstone of Digital Transformation Success

As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous […]

Data Crime: Your Phone Isn’t Here

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of data management to someone. The Story Last year, a […]

How Integrated, Quality Data Can Make Your Business Unstoppable

Successful organizations use data in different ways for different purposes, but they have one thing in common—data is the cornerstone of their business. They use it to uncover hidden opportunities, streamline operations, and predict trends with remarkable accuracy. In other words, these companies realize the transformative potential of their data.

As noted in a recent article by KPMG, a data-driven culture differentiates companies. “For one, it enables organizations to make informed decisions, improve productivity, enhance customer experiences, and confidently respond to challenges with a factual basis,” according to the article.

That’s because the more people throughout your organization with access to timely, accurate, and trusted data, the more it improves everything from decision-making to innovation to hyper-personalized marketing. Successful organizations ensure their data is integrated, governed, and meets their high-quality standards for analytical use cases, including Gen AI.

Data is the Catalyst for Incremental Success

Data is regularly likened to something of high value, from gold that can be mined for insights to the new oil—an invaluable resource that when refined and properly utilized, drives unprecedented growth and innovation. However, unlike oil, data’s value doesn’t diminish with usage or time. Instead, it can be used repeatedly for continuous insights and ongoing improvements.

When integrated effectively with the proper preparation and quality, data becomes an unstoppable force within your organization. It enables you to make strategic decisions with confidence, giving you a competitive edge in the market.

Organizations that invest in modern data analytics and data management capabilities position themselves to identify trends, predict market shifts, and better understand every aspect of their business. Moreover, the ability to leverage data in real-time enables you to be agile, responding swiftly to emerging opportunities, and identify business, customer, and partner needs.

In addition, making data readily accessible to everyone who benefits from it amplifies the potential. Empowering employees at all skill levels with barrier-free access to relevant data and easy-to-use tools actively promotes a data-driven culture.

Solve the Challenge: Overcome Fragmented and Poor-Quality Data

Despite the clear benefits of trusted, well-managed data, many organizations continue to struggle to get the data quality needed for their use cases. Data silos, resulting from lack of data integration across systems, create barriers to delivering meaningful insights.

Likewise, poor data governance erodes trust in data and can result in decision-making based on incomplete or inaccurate information. To solve the poor data quality challenge, you must first prioritize robust data integration practices that break down silos and unify data from disparate sources. Leveraging a modern data platform that facilitates seamless integration and data flows across systems is crucial.

A unified platform helps ensure data consistency by connecting data, transforming it into a reliable asset, then making it available across the entire organization. The data can then be leveraged for timely reports, informed decision making, automated processes, and other business uses.

Implementing a strong data governance framework that enforces data quality standards will give you confidence that your data is reliable, accurate, and complete. The right framework continuously monitors your data to identify and address issues proactively. Investing in both data integration and governance removes the limitations caused by fragmented and poor-quality data, ensuring you have trusted insights to propel your business forward.

5 Surprising Wins From Modern Data Integration and Data Quality

The true value of data becomes evident when it leads to tangible business outcomes. When you have data integrated from all relevant sources and have the quality you need, every aspect of your business becomes unstoppable.

Here are five surprising wins you can gain from your data:

1. Hyper-Personalized Customer Experiences

Integrating customer data from multiple touchpoints gives you the elusive 360-degree view of your customers. This comprehensive understanding of each individual’s preferences, buying habits, spending levels, and more enables you to hyper personalize marketing. The result? Improved customer service, tailored product recommendations, increased sales, and loyal customers.

Connecting customer data on a single platform often reveals unexpected insights that can drive additional value. For example, analysis might reveal emerging trends in customer behaviors that lead to new product innovations or identify previously untapped customer segments with high growth potential. These surprise benefits can provide a competitive edge, allowing you to anticipate customer needs, optimize your inventory, and continually refine targeted marketing strategies to be more effective.

2. Ensure Ongoing Operational Efficiency

Data integration and quality management can make operations increasingly efficient by providing real-time insights into supply chain performance, inventory levels, and production processes. For instance, a manufacturer can use its data to predict potential supply chain delays or equipment breakdowns with enough time to take action, making operations more efficient and mitigating interruptions.

Plus, performing comprehensive analytics on operational data can uncover opportunities to save costs and improve efficiency. For instance, you might discover patterns that demonstrate the most optimal times for maintenance, reducing downtime even further. Likewise, you could find new ways to streamline procurement, minimize waste, or better align production schedules and forecasting with actual demand, leading to leaner operations and more agile responses to changing market conditions.

3. Mitigate Current and Emerging Risk With Precision

All businesses face some degree of risk, which must be minimized to ensure compliance, avoid penalties, and protect your business reputation. Quality data is essential to effectively identify and mitigate risk. In the financial industry, for example, integrated data can expose fraudulent activities or non-compliance with regulatory requirements.

By leveraging predictive analytics, you can anticipate potential risks and implement preventive measures, safeguarding your assets and reputation. This includes detecting subtle patterns or anomalies that could indicate emerging threats, allowing you to address them before they escalate. The surprise benefit? A more comprehensive, forward-looking risk management strategy that protects your business while positioning you to thrive in an increasingly complex business and regulatory landscape.

4. Align Innovation and Product Development With Demand

Data-driven insights can accelerate innovation by highlighting unmet customer needs and understanding emerging market trends. For example, an eCommerce company can analyze user feedback and usage patterns to develop new website features or entirely new products to meet changing demand. This iterative, data-driven approach to product development can significantly enhance competitiveness.

Aligning product development with demand is an opportunity to accelerate growth and sales. One way to do this is to closely monitor customer feedback and shifts in buying patterns to identify new or niche markets. You can also use data to create tailored products or services that resonate with target audiences. One surprise benefit is a more agile and responsive product development process that predicts and meets customer demand.

5. Get Trusted Outcomes From Gen AI

Generative AI (Gen AI) offers cutting-edge use cases, amplifying your company’s capabilities and delivering ultra-fast outcomes. With the right approach, technology, and data, you can achieve innovative breakthroughs in everything from engineering to marketing to research and development, and more.

Getting trusted results from Gen AI requires quality data. It also requires a modern data strategy that realizes the importance of using data that meets your quality standard in order to fuel the Gen AI engine, enabling it to produce reliable, actionable insights. When your data strategy aligns with your Gen AI initiatives, the potential for growth and innovation is endless.

Have Confidence That Data is Working for You

In our era where data is a critical asset, excelling in data management and analytics can deliver remarkable outcomes—if you have the right platform. Actian Data Platform is our modern and easy-to-use data management solution for data-driven organizations. It provides a powerful solution for connecting, managing, and analyzing data, making it easier than you probably thought possible to get trusted insights quickly.

Investing in robust data management practices and utilizing a modern platform with proven price performance is not just a strategic move. It’s a necessity for staying competitive in today’s fast-paced, data-driven world. With the right tools and a commitment to data quality, your company can become unstoppable. Get a custom demo of the Actian Data Platform to experience how easy data can be.

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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, nearly 117 million people 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 […]

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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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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 […]

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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.

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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, […]

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Mind the Gap: The Product in Data Product Is Reliability

Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored analytics architecture stuck in the 1990s. This month, we’ll look at the rise of the data product. It wasn’t so long ago […]

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🔴 Red Button Data

🔴 Red Button Data

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