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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 […]

The post New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI appeared first on DATAVERSITY.


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Author: Bryan Eckle

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 […]


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Author: Merrill Albert

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.

isg report 2024

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. 

 

The post Key Insights From the ISG Buyers Guide for Data Intelligence 2024 appeared first on Actian.


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Author: Actian Corporation

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 […]

The post Charting a Course Through the Data Mapping Maze in Three Parts appeared first on DATAVERSITY.


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Author: Eric Crane

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 […]


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Author: Christine Haskell

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 […]


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Author: Merrill Albert

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.

The post How Integrated, Quality Data Can Make Your Business Unstoppable appeared first on Actian.


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Author: Derek Comingore

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 […]

The post The Secret to RAG Optimization: Expert Human Intervention appeared first on DATAVERSITY.


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Author: Christopher Stephens

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 […]

The post How to Win the War Against Bad Master Data appeared first on DATAVERSITY.


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Author: Danny Thompson

Avoiding the Pitfalls: Don’t Rush Chatbot Deployment


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

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


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Author: Cláudio Rodrigues

Understanding the Role of Data Quality in Data Governance

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

Understanding Data Quality

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

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

Profiling Data With Precision

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

Validating and Standardizing Data

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

The Importance of Data Governance

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

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

Unlocking the Power of Metadata Management

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

Driving Compliance and Risk Mitigation

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

The Synergistic Relationship Between Data Quality and Data Governance

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

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

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

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

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


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Author: Traci Curran

Embracing Data and Emerging Technologies for Quality Management Excellence


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

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


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Author: Anthony Hudson

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 […]

The post Mind the Gap: The Product in Data Product Is Reliability appeared first on DATAVERSITY.


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Author: Mark Cooper

The Cool Kids Corner: Non-Invasive Data Governance


Hello! I’m Mark Horseman, and welcome to The Cool Kids Corner. This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month we’re talking about Non-Invasive Data Governance (NIDG). If I haven’t already given it away, our featured Cool Kid is none other […]

The post The Cool Kids Corner: Non-Invasive Data Governance appeared first on DATAVERSITY.


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Author: Mark Horseman

Building a Modern Data Platform with Data Fabric Architecture


In today’s data-driven landscape, organizations face the challenge of integrating diverse data sources efficiently. Whether due to mergers and acquisitions (M&A) or the need for advanced insights, a robust data platform with streamlined data operations are essential. Shift in Mindset Data fabric is a design concept for integrating and managing data. Through flexible, reusable, augmented, and […]

The post Building a Modern Data Platform with Data Fabric Architecture appeared first on DATAVERSITY.


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Author: Tejasvi Addagada

The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks

In today’s data-driven business landscape, the quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that poor data quality is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.

Defining Poor Data Quality

Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.

The Hidden Costs of Poor Data Quality

  1. Loss of Revenue
    Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
  2. Reduced Operational Efficiency
    When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
  3. Flawed Analytics and Decision-Making
    Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
  4. Compliance Risks
    Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
  5. Missed Opportunities
    Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
  6. Reputational Damage
    Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.

Measuring the Financial Impact of Poor Data Quality

  1. Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities​ (Data Ladder)​.
  2. GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden​ (Experian Data Quality)​​ (Anodot)​.
  3. Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity​ (Anodot)​.
  4. Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts​ (Data Ladder)​.
  5. Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting​ (CamSpark)​.

Strategies for Improving Data Quality

Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.

  1. Fostering a Data-Driven Culture
    Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
  2. Implementing Robust Data Governance
    Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
  3. Leveraging Data Quality Solutions
    Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.

The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.

The post The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks appeared first on Actian.


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Author: Traci Curran

The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks

In today’s data-driven business landscape, the quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that poor data quality is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.

Defining Poor Data Quality

Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.

The Hidden Costs of Poor Data Quality

  1. Loss of Revenue
    Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
  2. Reduced Operational Efficiency
    When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
  3. Flawed Analytics and Decision-Making
    Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
  4. Compliance Risks
    Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
  5. Missed Opportunities
    Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
  6. Reputational Damage
    Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.

Measuring the Financial Impact of Poor Data Quality

  1. Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities​ (Data Ladder)​.
  2. GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden​ (Experian Data Quality)​​ (Anodot)​.
  3. Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity​ (Anodot)​.
  4. Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts​ (Data Ladder)​.
  5. Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting​ (CamSpark)​.

Strategies for Improving Data Quality

Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.

  1. Fostering a Data-Driven Culture
    Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
  2. Implementing Robust Data Governance
    Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
  3. Leveraging Data Quality Solutions
    Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.

The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.

The post The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks appeared first on Actian.


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Author: Traci Curran

Introducing Actian’s Enhanced Data Quality Solutions: Empower Your Business with Accurate and Reliable Data

We are pleased to announce that data profiling is now available as part of the Actian Data Platform. This is the first of many upcoming enhancements to make it easy for organizations to connect, manage, and analyze data. With the introduction of data profiling, users can load data into the platform and identify focus areas, such as duplicates, missing values, and non-standard formats, to improve data quality before it reaches its target destination.

Why Data Quality Matters

Data quality is the cornerstone of effective data integration and management. High-quality data enhances business intelligence, improves operational efficiency, and fosters better customer relationships. Poor data quality, on the other hand, can result in costly errors, compliance issues, and loss of trust.

Key Features of Actian’s Enhanced Data Quality Solutions

  1. Advanced Data Profiling
    Our advanced data profiling tools provide deep insights into your data’s structure, content, and quality. You can quickly identify anomalies, inconsistencies, and errors by analyzing your data sources and leveraging pre-defined rule sets to detect data problems. Users can also create rules based on the use case to ensure data is clean, correct, and ready for use.
    Data Quality Overview
  2. Data Cleansing and Enrichment
    Actian’s data cleansing and enrichment capabilities ensure your data is accurate, complete, and up-to-date. Our automated processes isolate data that does not meet quality standards so data teams can act before data is moved to its target environment.
  3. Data Quality Monitoring
    With real-time data quality monitoring, you can continuously assess the health of your data. Our solution provides ongoing validation, enabling you to monitor deviations from predefined quality standards. This continuous oversight helps you maintain data integrity for operational and analytics use.
    Data Quality Run History
  4. Flexible Integration Options
    Actian’s data quality solutions seamlessly integrate with various data sources and platforms. Whether you’re working with on-premises databases, cloud-based applications, or hybrid environments, our tools can connect, cleanse, and harmonize your data across all systems.
  5. User-Friendly Interface and Dashboards
    Our intuitive interface makes managing data quality tasks easy for users of all skill levels. Detailed reporting and dashboards provide clear visibility into data quality metrics, enabling you to track improvements and demonstrate compliance with data governance policies.

Transform Your Data into a Strategic Asset

Actian’s enhanced Data Quality solutions empower you to transform raw data into a strategic asset. Ensuring your data is accurate, reliable, and actionable can drive better business outcomes and gain a competitive edge.

Get Started Today

Don’t let poor data quality hold your business back. Discover how Actian’s enhanced Data Quality solutions can help you achieve your data management goals. Visit our Data Quality page to learn more and request a demo.

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The post Introducing Actian’s Enhanced Data Quality Solutions: Empower Your Business with Accurate and Reliable Data appeared first on Actian.


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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 here’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 is—are your data and data preparation up to the mark? Let’s delve into why data preparation and quality are the linchpins for Gen AI success.

 

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

There’s a well-known adage in the data world: “Garbage in, garbage out.” 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 model’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 you’ve meticulously prepared your ingredients, but they’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. Here’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 you’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 year’s market data to generate this year’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. It’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

Let’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 inaccuracies—wrong contact details, outdated purchase history, or incorrect preferences—the generated content will miss the mark, leading to disengagement and potentially damaging the brand’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. Here’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, “A chain is only as strong as its weakest link.” 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 remains—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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