The Evolving Roles of the Data Steward and Data Citizen
What's covered in this article?
Augmented Data Quality: the what, why, when and how
What is Augmented Data Quality?
Augmented Data Quality (ADQ) leverages advanced technologies such as AI and ML to enhance traditional data management practices. It automates the detection and correction of data issues, enabling data leaders, data stewards, and domain experts to ensure higher data accuracy, consistency, and reliability. This approach is vital in today's fast-paced data environments, where manual oversight is impractical due to the sheer volume and complexity of data. ADQ represents a shift towards more dynamic, responsive data governance models, aligning closely with strategic business objectives by providing a cleaner, more trustworthy data foundation.
When is Augmented Data Quality needed?
ADQ becomes crucial in various scenarios, such as when organizations face rapid data growth, deal with data from multiple, disparate sources, or require real-time data analysis for decision-making. It's also essential when businesses undertake digital transformation projects that necessitate clean, reliable data for new applications. In addition, industries regulated by strict data compliance standards need augmented solutions to ensure accuracy and adherence to regulations. Essentially, any situation where the volume, velocity, or variety of data overwhelms traditional management approaches calls for augmented data quality solutions.
5 Reasons Why CluedIn is the Optimal Choice for MDM on Microsoft Azure
Enterprises constantly seek solutions that not only streamline their data processes but also align seamlessly with their existing technology stack. For businesses that have committed to the Microsoft Azure ecosystem, CluedIn is the go-to Master Data Management (MDM) solution. Here we explore five fundamental reasons why CluedIn stands as the most natural choice for Master Data Management on Microsoft Azure.
CluedIn unveils next-generation Master Data Management powered by AI
New CluedIn AI Assistant helps organizations to integrate data 60% faster, validate and deduplicate data 50% quicker, and improve overall data quality by 30%
CluedIn today announced that its new release is now available in private preview with an anticipated GA date of early 2024. The latest version represents another significant leap forward in reinforcing CluedIn’s position as the most advanced MDM system available, in terms of both enabling domain experts and accelerating the process of extracting value from enterprise data.
Your personal MDM assistant
In an industry first, CluedIn now includes an AI Assistant feature to support data stewards, engineers, and domain experts with the day-to-day management and operationalization of data. The CluedIn AI Assistant will help with the generation of rules, mapping, and providing additional context to specific pieces of data. CluedIn customers can also expect AI-aided generation of deduplication rules and project recommendations in the near future. Learn more about how the CluedIn AI Assistant could help you save days of manual effort and aid in bridging the data literacy gap here.
The new release builds on CluedIn’s commitment to pioneering the use of Generative AI in MDM, as demonstrated by the announcement that CluedIn was the first MDM vendor to integrate with Azure OpenAI in April 2023.
How to improve data quality without a Master Data Management platform
The quality of your data can make or break your business. While Master Data Management (MDM) solutions are often touted as the go-to for ensuring high-quality data, it is possible to make improvements without them.
Here are six ways to increase the quality of your data without using an MDM solution:
Which data types should be managed by a Master Data Management solution?
Tradition dictates that there certain types of data that should be handled by a Master Data Management (MDM) platform, and others that shouldn’t. However, the sheer diversity and complexity of data mean that these notions are being challenged, as organizations realize that in order to achieve a comprehensive and actionable view of any domain, they need the bigger picture.
Before we examine the types of data that an MDM solution is suitable for, we should first define the different data types:
AI Readiness Checklist for Data Leaders and Practitioners
The advent of Artificial Intelligence (AI) has ushered in a new era of technological innovation, offering unprecedented opportunities for businesses to enhance efficiency, innovation, and competitiveness. However, the journey to becoming AI-ready involves navigating a complex landscape of technical, organizational, and cultural challenges. The guide below and the subsequent checklist provide actionable advice for data leaders and practitioners on preparing their organizations to incorporate AI.
Technical Preparation:
Enhance Data Accessibility and Quality
- Establish a robust data governance framework to ensure data accuracy, consistency, and security.
- Implement data cleaning and preprocessing techniques to improve data quality.
- Foster partnerships with data providers to access diverse and extensive datasets.
Upgrade Infrastructure and Computing Resources:
- Invest in scalable cloud computing services to handle large-scale AI computations.
- Conduct regular technology audits to identify and address infrastructure gaps.
- Explore high-performance computing solutions tailored to AI applications.
Boost Data Literacy Across the Organization
- Assess current skill levels and identify gaps in data literacy.
- Develop comprehensive training programs focusing on data analysis, interpretation, and AI applications.
- Encourage continuous learning and upskilling in AI and data science.
Organizational Changes
Cultivate an AI-Ready Culture
- Promote a culture of innovation and openness to new technologies.
- Educate stakeholders on the benefits and potential of AI.
- Encourage cross-departmental collaboration to integrate AI insights into various business processes.
Address Ethical and Legal Concerns
- Develop ethical guidelines for AI usage, focusing on fairness, accountability, and transparency.
- Stay informed about legal regulations related to AI and data privacy.
- Implement compliance measures and regular audits to ensure ethical AI practices.
Strategic AI Implementation Planning
- Develop a clear AI strategy aligned with business objectives.
- Identify key areas where AI can add the most value.
- Establish measurable goals and KPIs to track AI implementation success.
Cultural Adaptation
Overcome Resistance to Change
- Communicate the vision and benefits of AI clearly to all levels of the organization.
- Involve employees in the AI integration process and decision-making.
- Recognize and address concerns and fears related to AI adoption.
Foster a Collaborative Environment
- Encourage open communication and knowledge sharing about AI projects.
- Create multidisciplinary teams to bring diverse perspectives to AI initiatives.
- Celebrate successes and learn from setbacks in AI projects.
Microsoft Fabric and Purview Integration announced at Microsoft Ignite
This week, the annual Microsoft Ignite conference is taking place in Seattle. One of the main announcements to come out of the show is the General Availability of Microsoft Fabric, and an expansion of its governance and security capabilities through a tighter integration with Microsoft Purview.
As the preferred Master Data Management partner for Microsoft Azure customers, CluedIn is already fully integrated with both Microsoft Fabric and Purview. In combination with Microsoft Purview, we offer a unified, scalable, and transparent way to manage and drive insights from data. For Microsoft Fabric customers, we take raw data and make it ready for analysis by any tool in the Fabric suite. We also do this 80% faster than any other Master Data Management Platform on the market.
Bringing Microsoft Fabric and Purview together is the next natural step in helping businesses manage their end-to-end data estate with ultimate security, assurance, and accessibility. Microsoft Fabric is redefining how teams work with data by bringing everyone together on a single, AI-powered platform built for the era of AI. With Purview and CluedIn providing the pipeline of high-quality, trusted data needed to fuel Fabric, a new data and analytics powerhouse has emerged.
Why keeping MDM in the IT Domain is bad for business
The value that engineers bring to the data stack is undeniable. They play a crucial role in ensuring data is primed for insights. However, the immense value and necessity of involving Domain Experts in the data supply chain, not merely as consumers but as contributors, cannot be overstated.
IT and Engineering have distinct focuses within the data supply chain. Their work aligns with the overarching goal of preparing data for insights. Yet, there's a specific persona that is interested in enhancing the intrinsic value of the data itself. Consider the analogy of a Netflix movie director. While Netflix's ability to deliver content globally, start streaming in seconds, and function across devices is impressive, the content—the movie—remains the star. It requires directors, writers, and editors to refine the content to the point where it captivates the audience. Similarly, one might ask, who is the "director" for your customer data?