The Five Levels Essential to Scaling Your Data Strategy
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Author: Jason Foster
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Author: Jason Foster
Today, organizations and individuals face an ever-growing challenge: the sheer volume of data being generated and stored across various systems. This data needs to be properly organized, categorized, and made easily accessible for efficient decision-making. One critical aspect of organizing data is through the use of metadata, which serves as a descriptive layer that helps users understand, find, and utilize data effectively.
Among the various types of metadata, structural metadata plays a crucial role in facilitating improved data management and discovery. This article will define what structural metadata is, why it is useful, and how the Actian Data Intelligence Platform can help organizations better organize and manage their structural metadata to enhance data discovery.
Metadata is often classified into various types, such as descriptive metadata, administrative metadata, and structural metadata. While descriptive metadata provides basic information about the data (e.g., title, author, keywords), and administrative metadata focuses on the management and lifecycle of data (e.g., creation date, file size, permissions), structural metadata refers to the organizational elements that describe how data is structured within a dataset or system.
In simpler terms, structural metadata defines the relationships between the different components of a dataset. It provides the blueprint for how data is organized, linked, and formatted, making it easier for users to navigate complex datasets. In a relational database, for example, structural metadata would define how tables, rows, columns, and relationships between entities are arranged. In a document repository, it could describe the format and organization of files, such as chapters, sections, and subsections.
Here are some key aspects of structural metadata:
Structural metadata plays a fundamental role in ensuring that data is understandable, accessible, and usable. Here are several reasons why it is essential:
Despite its importance, managing structural metadata is not without challenges.
The Actian Data Intelligence Platform provides organizations with the tools to handle their metadata efficiently. By enabling centralized metadata management, organizations can easily catalog and manage structural metadata, thereby enhancing data discovery and improving overall data governance. Here’s how the platform can help:
The Actian Data Intelligent Platform allows organizations to centralize all metadata, including structural metadata, into a single, unified repository. This centralization makes it easier to manage, search, and access data across different systems and platforms. No matter where the data resides, users can access the metadata and understand how datasets are structured, enabling faster data discovery.
The platform supports the automated ingestion of metadata from a wide range of data sources, including databases, data lakes, and cloud storage platforms. This automation reduces the manual effort required to capture and maintain metadata, ensuring that structural metadata is always up to date and accurately reflects the structure of the underlying datasets.
With Actian’s platform, organizations can visualize data lineage and track the relationships between different data elements. This feature allows users to see how data flows through various systems and how different datasets are connected. By understanding these relationships, users can better navigate complex datasets and conduct more meaningful analyses.
The Actian Data Intelligence Platform provides powerful data classification and tagging capabilities that allow organizations to categorize data based on its structure, type, and other metadata attributes. This helps users quickly identify the types of data they are working with and make more informed decisions about how to query and analyze it.
The platform’s metadata catalog enables users to easily search and find datasets based on specific structural attributes. Whether looking for datasets by schema, data format, or relationships, users can quickly pinpoint relevant data, which speeds up the data discovery process and improves overall efficiency.
Actian’s platform fosters collaboration by providing a platform where users can share insights, metadata definitions, and best practices. This transparency ensures that everyone in the organization is on the same page when it comes to understanding the structure of data, which is essential for data governance and compliance.
Using a federated knowledge graph, organizations can automatically identify, classify, and track data assets based on contextual and semantic factors. This makes it easier to map assets to key business concepts, manage regulatory compliance, and mitigate risks.
Managing and organizing metadata is more important than ever in the current technological climate. Structural metadata plays a crucial role in ensuring that datasets are organized, understandable, and accessible. By defining the relationships, formats, and hierarchies of data, structural metadata enables better data discovery, integration, and analysis.
However, managing this metadata can be a complex and challenging task, especially as datasets grow and become more fragmented. That’s where the Actian Data Intelligence Platform comes in. With Actian’s support, organizations can unlock the full potential of their data, streamline their data management processes, and ensure that their data governance practices are aligned with industry standards, all while improving efficiency and collaboration across teams.
Take a tour of the Actian Data Intelligence Platform or sign up for a personalized demonstration today.
The post Understanding Structural Metadata appeared first on Actian.
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Author: Actian Corporation
In Part 1 of this series, we established the strategic foundation for external data success: defining your organizational direction, determining specific data requirements, and selecting the right data providers. We also introduced the critical concept of external data stewardship — identifying key stakeholders who bridge the gap between business requirements and technical implementation. This second part […]
The post External Data Strategy: Governance, Implementation, and Success (Part 2) appeared first on DATAVERSITY.
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Author: Subasini Periyakaruppan
In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for […]
The post External Data Strategy: From Vision to Vendor Selection (Part 1) appeared first on DATAVERSITY.
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Author: Subasini Periyakaruppan
Do you want your business users to embrace and use analytics? You want your business to enjoy the benefits of fact-based decision making? You want your business to use the tools of business intelligence to improve market presence, customer satisfaction and team productivity and collaboration? A scarcity of data scientists will no longer hinder the […]
The post Analytics and Citizen Data Scientists Ensure Business Advantage appeared first on DATAVERSITY.
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Author: Kartik Patel
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Author: Gopi Maren
As data volumes continue to rapidly grow and organizations become increasingly data driven in the AI age, the data landscape of 2025 is poised to be more dynamic and complex than ever before.
For businesses to excel in this fast-evolving environment, chief data officers (CDOs) of the future must move beyond their traditional roles to become strategic transformation leaders. Key priorities will shape their agenda and be a driving force for success in an era of sweeping change.
The eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” explores seven key priorities that will define successful data leadership in 2025. From crafting unified data strategies that feel less like governance manifestos and more like business transformation blueprints, to preparing trusted data for the AI revolution, you will learn:
The role of the CDO has undergone a significant change over the last few years—and it’s continuing to be redefined as CDOs prove their value. CDOs are now unlocking competitive advantages by implementing and optimizing comprehensive data initiatives. That’s part of the reason why organizations with a dedicated CDO are better equipped to handle the complexities of modern data ecosystems and maintain a competitive edge than those without this role.
As noted in our eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” this critical position will become even more strategic. The role will highlight a distinct difference between good companies that use data and great companies that rely on data to drive every business decision, accelerate growth, and confidently embrace whatever is next.
The idea for this eBook began with a simple observation: The role of CDO has become a sort of organizational Rorschach test. Ask 10 executives what a CDO should do, and you’ll get 11 different answers, three strategic frameworks, and at least one person insisting it’s all about AI (it’s not).
While researching this piece, a fascinating pattern emerged. Data strategy isn’t just about governance and quality metrics, but about fundamental business transformation. But perhaps most intriguing is the transformation of the CDO role itself. What started as a data custodian and governance guru has morphed into something far more nuanced: Part strategist, part innovator, part ethicist, and increasingly, part business transformer.
The eBook dives deeper into these themes, offering insights and frameworks for navigating this evolution. But more than that, it attempts to capture this moment of transformation–where data leadership is becoming something new and, potentially, revolutionary.
The seven priorities outlined in the eBook aren’t just predictions; they’re emerging patterns. When McKinsey tells us that 72% of organizations struggle with managing data for AI use cases, they’re really telling us something profound about the gap between our technological ambitions and our organizational readiness. We’re all trying to build the plane while flying it–and some of us are still debating whether we need wings.
This eBook is for leaders who find themselves at this fascinating intersection of technology, strategy, and organizational change. Whether you’re a CDO looking to validate your roadmap, or an executive trying to understand why your data initiatives feel like pushing boulders uphill, we hope you’ll find something here that makes you think differently about the journey ahead.
Download the eBook if you’re curious about what data leadership looks like when we stop treating it like a technical function and start seeing it as a strategic imperative.
The post The 7 Fundamentals That Are Crucial for CDO Success in 2025 appeared first on Actian.
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Author: Dee Radh
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Author: Kartik Patel
Marketing leaders and data professionals often grapple with a familiar challenge: how to transform marketing data into tangible business growth. During a recent episode of The Lights on Data Show, I had the privilege of speaking with Kasper Bossen-Rasmussen, founder and CEO of Accutics, about this very topic. Together, we explored key takeaways for addressing […]
The post Transforming Marketing Data into Business Growth: Key Insights and Strategies appeared first on LightsOnData.
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Author: George Firican
Unlocking the value of data is a key focus for business leaders, especially the CIO. While in its simplest form, data can lead to better insights and decision-making, companies are pursuing an entirely different and more advanced agenda: the holy grail of data monetization. This concept involves aggregating a variety of both structured and unstructured […]
The post Data Monetization: The Holy Grail or the Road to Ruin? appeared first on DATAVERSITY.
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Author: Tony Klimas
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 […]
The post Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database appeared first on DATAVERSITY.
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Author: Mark Cooper
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Author: Kartik Patel
As artificial intelligence (AI) continues to advance, the form factor of generative AI is evolving rapidly. The concept of “form factor” encompasses the systems, interfaces, and user experiences that allow us to interact with AI. It’s what bridges the gap between complex machine learning models and practical, everyday use cases. Today, the most familiar form […]
The post The future of generative AI’s form factor appeared first on LightsOnData.
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Author: George Firican
From Fortune 500 companies to local startups, everyone’s swimming in a sea of numbers, charts, and graphs. But here’s the thing: While structured data like sales figures and customer demographics have long been the backbone of analytics, there’s a growing realization that unstructured data is the real goldmine. Think about it. Every tweet, email, customer review, and social […]
The post The Hidden Language of Data: How Linguistic Analysis Is Transforming Data Interpretation appeared first on DATAVERSITY.
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Author: Nahla Davies
In the high-stakes world of data governance, where organizations strive to protect and leverage their most valuable asset, one truth stands out: technology alone won’t get you there. The secret sauce? People and culture. They are the lifeblood of any successful data governance strategy, the pulse that drives data literacy, and the force that propels […]
The post Unleashing the Power of People and Culture: The Ultimate Drivers of Data Governance Success appeared first on DATAVERSITY.
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Author: Gopi Maren
Table of contentsÂ
Understanding the Basics
What is a Data Warehouse?
The Business Imperative of Data Warehousing
The Technical Role of Data Warehousing
Understanding the Differences: Databases, Data Warehouses, and Analytics Databases
The Human Side of Data: Key User Personas and Their Pain Points
Data Warehouse Use Cases For Modern Organizations
6 Common Business Use Cases
9 Technical Use Cases
Welcome to data warehousing 101. For those of you who remember when “cloud” only meant rain and “big data” was just a database that ate too much, buckle up—we’ve come a long way. Here’s an overview:
Data warehouses are large storage systems where data from various sources is collected, integrated, and stored for later analysis. Data warehouses are typically used in business intelligence (BI) and reporting scenarios where you need to analyze large amounts of historical and real-time data. They can be deployed on-premises, on a cloud (private or public), or in a hybrid manner.
Think of a data warehouse as the Swiss Army knife of the data world – it’s got everything you need, but unlike that dusty tool in your drawer, you’ll actually use it every day!
Prominent examples include Actian Data Platform, Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure Synapse Analytics, and IBM Db2 Warehouse, among others.
Proper data consolidation, integration, and seamless connectivity with BI tools are crucial for a data strategy and visibility into the business. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.
“Proper data consolidation, integration, and seamless connectivity with BI tools are crucial aspects of a data strategy. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.”
Data warehouses are instrumental in enabling organizations to make informed decisions quickly and efficiently. The primary value of a data warehouse lies in its ability to facilitate a comprehensive view of an organization’s data landscape, supporting strategic business functions such as real-time decision-making, customer behavior analysis, and long-term planning.
But why is a data warehouse so crucial for modern businesses? Let’s dive in.
A data warehouse is a strategic layer that is essential for any organization looking to maintain competitiveness in a data-driven world. The ability to act quickly on analyzed data translates to improved operational efficiencies, better customer relationships, and enhanced profitability.
The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself. The BI team configures the data warehouse to align with its analytical needs. Essentially, a data warehouse acts as a structured repository, comprising tables of rows and columns of carefully curated and frequently updated data assets. These assets feed BI applications that drive analytics.
“The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself.”
Achieving the business imperatives of data warehousing relies heavily on these four key technical capabilities:
1. Real-Time Data Processing: This is critical for applications that require immediate action, such as fraud detection systems, real-time customer interaction management, and dynamic pricing strategies. Real-time data processing in a data warehouse is like a barista making your coffee to order–it happens right when you need it, tailored to your specific requirements.
2. Scalability and Performance: Modern data warehouses must handle large datasets and support complex queries efficiently. This capability is particularly vital in industries such as retail, finance, and telecommunications, where the ability to scale according to demand is necessary for maintaining operational efficiency and customer satisfaction.
3. Data Quality and Accessibility: The quality of insights directly correlates with the quality of data ingested and stored in the data warehouse. Ensuring data is accurate, clean, and easily accessible is paramount for effective analysis and reporting. Therefore, it’s crucial to consider the entire data chain when crafting a data strategy, rather than viewing the warehouse in isolation.
4. Advanced Capabilities: Modern data warehouses are evolving to meet new challenges and opportunities:
“In the world of data warehousing, scalability isn’t just about handling more data—it’s about adapting to the ever-changing landscape of business needs.”
Databases, data warehouses, and analytics databases serve distinct purposes in the realm of data management, with each optimized for specific use cases and functionalities.
A database is a software system designed to efficiently store, manage, and retrieve structured data. It is optimized for Online Transaction Processing (OLTP), excelling at handling numerous small, discrete transactions that support day-to-day operations. Examples include MySQL, PostgreSQL, and MongoDB. While databases are adept at storing and retrieving data, they are not specifically designed for complex analytical querying and reporting.
Data warehouses, on the other hand, are specialized databases designed to store and manage large volumes of structured, historical data from multiple sources. They are optimized for analytical processing, supporting complex queries, aggregations, and reporting. Data warehouses are designed for Online Analytical Processing (OLAP), using techniques like dimensional modeling and star schemas to facilitate complex queries across large datasets. Data warehouses transform and integrate data from various operational systems into a unified, consistent format for analysis. Examples include Actian Data Platform, Amazon Redshift, Snowflake, and Google BigQuery.
Analytics databases, also known as analytical databases, are a subset of databases optimized specifically for analytical processing. They offer advanced features and capabilities for querying and analyzing large datasets, making them well-suited for business intelligence, data mining, and decision support. Analytics databases bridge the gap between traditional databases and data warehouses, offering features like columnar storage to accelerate analytical queries while maintaining some transactional capabilities. Examples include Actian Vector, Exasol, and Vertica. While analytics databases share similarities with traditional databases, they are specialized for analytical workloads and may incorporate features commonly associated with data warehouses, such as columnar storage and parallel processing.
“In the data management spectrum, databases, data warehouses, and analytics databases each play distinct roles. While all data warehouses are databases, not all databases are data warehouses. Data warehouses are specifically tailored for analytical use cases. Analytics databases bridge the gap, but aren’t necessarily full-fledged data warehouses, which often encompass additional components and functionalities beyond pure analytical processing.”
Welcome to Data Warehouse Personalities 101. No Myers-Briggs here—just SQL, Python, and a dash of data-induced delirium. Let’s see who’s who in this digital zoo.
Note: While these roles are presented distinctly, in practice they often overlap or merge, especially in organizations of varying sizes and across different industries. The following personas are illustrative, designed to highlight the diverse perspectives and challenges related to data warehousing across common roles.
In this section, we’ll feature common use cases for both the business and IT sides of the organization.
This section highlights how data warehouses directly support critical business objectives and strategies.
1. Supply Chain and Inventory Management: Enhances supply chain visibility and inventory control by analyzing procurement, storage, and distribution data. Think of it as giving your supply chain a pair of X-ray glasses—suddenly, you can see through all the noise and spot exactly where that missing shipment of left-handed widgets went.
Examples:
2. Customer 360 Analytics: Enables a comprehensive view of customer interactions across multiple touchpoints, providing insights into customer behavior, preferences, and loyalty.
Examples:
3. Operational Efficiency: Improves the efficiency of operations by analyzing workflows, resource allocations, and production outputs to identify bottlenecks and optimize processes. It’s the business equivalent of finding the perfect traffic route to work—except instead of avoiding road construction, you’re sidestepping inefficiencies and roadblocks to productivity.
Examples:
4. Financial Performance Analysis: Offers insights into financial health through revenue, expense, and profitability analysis, helping companies make informed financial decisions.
Examples:
5. Risk Management and Compliance: Helps organizations manage risk and ensure compliance with regulations by analyzing transaction data and audit trails. It’s like having a super-powered compliance officer who can spot a regulatory red flag faster than you can say “GDPR.”
Examples:
6. Market and Sales Analysis: Analyzes market trends and sales data to inform strategic decisions about product development, marketing, and sales strategies.
Examples:
These use cases demonstrate how data warehouses have become the backbone of data-driven decision making for organizations. They’ve evolved from mere data repositories into critical business tools.
In an era where data is often called “the new oil,” data warehouses serve as the refineries, turning that raw resource into high-octane business fuel. The real power of data warehouses lies in their ability to transform vast amounts of data into actionable insights, driving strategic decisions across all levels of an organization.
Ever wonder how boardroom strategies transform into digital reality? This section pulls back the curtain on the technical wizardry of data warehousing. We’ll explore nine use cases that showcase how data warehouse technologies turn business visions into actionable insights and competitive advantages. From powering machine learning models to ensuring regulatory compliance, let’s dive into the engine room of modern data-driven decision making.
1. Data Science and Machine Learning: Data warehouses can store and process large datasets used for machine learning models and statistical analysis, providing the computational power needed for data scientists to train and deploy models.
Key features:
2. Data as a Service (DaaS): Companies can use cloud data warehouses to offer cleaned and curated data to external clients or internal departments, supporting various use cases across industries.
Key features:
3. Regulatory Compliance and Reporting: Many organizations use cloud data warehouses to meet compliance requirements by storing and managing access to sensitive data in a secure, auditable manner. It’s like having a digital paper trail that would make even the most meticulous auditor smile. No more drowning in file cabinets!
Key features:
4. Administration and Observability: Facilitates the management of data warehouse platforms and enhances visibility into system operations and performance. Consider it your data warehouse’s health monitor—keeping tabs on its vital signs so you can diagnose issues before they become critical.
Key features:
5. Seasonal Demand Scaling: The ability to scale resources up or down based on demand makes cloud data warehouses ideal for industries with seasonal fluctuations, allowing them to handle peak data loads without permanent investments in hardware. It’s like having a magical warehouse that expands during the holiday rush and shrinks during the slow season. No more paying for empty shelf space!
Key features:
6. Enhanced Performance and Lower Costs: Modern data warehouses are engineered to provide superior performance in data processing and analytics, while simultaneously reducing the costs associated with data management and operations. Imagine a race car that not only goes faster but also uses less fuel. That’s what we’re talking about here—speed and efficiency in perfect harmony.
Key features:
7. Disaster Recovery: Cloud data warehouses often feature built-in redundancy and backup capabilities, ensuring data is secure and recoverable in the event of a disaster. Think of it as your data’s insurance policy—when disaster strikes, you’re not left empty-handed.
Key features:
Note: The following use cases are typically driven by separate solutions, but are core to an organization’s warehousing strategy.
8. (Depends on) Data Consolidation and Integration: By consolidating data from diverse sources like CRM and ERP systems into a unified repository, data warehouses facilitate a comprehensive view of business operations, enhancing analysis and strategic planning.
Key features:
9. (Facilitates) Business Intelligence: Data warehouses support complex data queries and are integral in generating insightful reports and dashboards, which are crucial for making informed business decisions. Consider this the grand finale where all your data prep work pays off—transforming raw numbers into visual stories that even the most data-phobic executive can understand.
Key features:
The technical capabilities we’ve discussed showcase how modern data warehouses are breaking down silos and bridging gaps across organizations. They’re not just tech tools; they’re catalysts for business transformation. In a world where data is the new currency, a well-implemented data warehouse can be your organization’s most valuable investment.
However, as data warehouses grow in power and complexity, many organizations find themselves grappling with a new challenge: managing an increasingly intricate data ecosystem. Multiple vendors, disparate systems, and complex data pipelines can turn what should be a transformative asset into a resource-draining headache.
“In today’s data-driven world, companies need a unified solution that simplifies their data operations. Actian Data Platform offers an all-in-one approach, combining data integration, data quality, and data warehousing, eliminating the need for multiple vendors and complex data pipelines.”
This is where Actian Data Platform shines, offering an all-in-one solution that combines data integration, data quality, and data warehousing capabilities. By unifying these core data processes into a single, cohesive platform, Actian eliminates the need for multiple vendors and simplifies data operations. Organizations can now focus on what truly matters—leveraging data for strategic insights and decision-making, rather than getting bogged down in managing complex data infrastructure.
As we look to the future, the organizations that will thrive are those that can most effectively turn data into actionable insights. With solutions like Actian Data Platform, businesses can truly capitalize on their data warehouse investment, driving meaningful transformation without the traditional complexities of data management.
Experience the data platform for yourself with a custom demo.
The post Data Warehousing Demystified: Your Guide From Basics to Breakthroughs appeared first on Actian.
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Author: Fenil Dedhia
In today’s hyper-competitive business environment, data is one of the most valuable assets an organization can have. However, the sheer volume, variety, and velocity of data can overwhelm traditional data management solutions. Enter the data lake – a centralized repository designed to store all types of data, whether structured, semi-structured, or unstructured. Unlike traditional data warehouses, data […]
The post Data Lake Strategy: Its Benefits, Challenges, and Implementation appeared first on DATAVERSITY.
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Author: Rohail Abrahani
Traditionally, IT departments have been viewed as support functions primarily responsible for maintaining hardware, troubleshooting software issues, and ensuring basic connectivity. IT leaders are often treated like technical experts running a cost center, relegated to a dim back office and only emerging when summoned to address a problem. But that couldn’t be further from the […]
The post Beyond the “IT Guy” Stereotype: How Technology Leaders Drive Business Success appeared first on DATAVERSITY.
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Author: Manjula Mahajan
Welcome to our new series, “Book of the Month.” In this series, we will explore new books in the data management space, highlighting how thought leaders are driving innovation and shaping the future. This month, we’re grabbing a cup of coffee, settling into our favorite reading nook, and diving into “Humanizing Data Strategy: Leading Data […]
The post Book of the Month: Insights from “Humanizing Data Strategy” appeared first on DATAVERSITY.
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Author: Mark Horseman
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
The cost of complacency is becoming crystal clear in the small and medium-sized business (SMB) space. There’s little room for those who rest on their laurels, especially when they make up over 95% of businesses globally emerging all the time. Amid fierce and crowded competition, innovation increasingly sets apart the high performers from those struggling to stand their […]
The post The Data Difference: How SMBs Are Getting Ahead of the Competition appeared first on DATAVERSITY.
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Author: Claire Gribbin
When you are presenting a way for IT to save money and have a better strategy to leverage the cloud, here’s a pro tip that can benefit any and all enterprises: A hybrid multi-cloud approach, with a strong private cloud configuration, creates the opportunity to consolidate storage arrays for maximum efficiency. Consolidation of storage saves on […]
The post The Relationship Between Storage Consolidation and a Hybrid Multi-Cloud IT Strategy appeared first on DATAVERSITY.
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Author: Eric Herzog
You may not be an analytics expert and you may find terms like PMML integration somewhat daunting. But, in reality, the concept is not complex, and the value is outstanding. So, what is PMML integration? PMML stands for “predictive model markup language.” It is an interchange format that provides a method by which analytical applications and […]
The post What Is PMML and Why Is It Important? appeared first on DATAVERSITY.
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Author: Kartik Patel
What are the biggest data strategy challenges facing you and your company? If you are like most, the main reason for developing a data strategy is to be capable of supporting the growth strategy of each type of business in an exclusive way – to offer competitive resilience with balance and maturity to defend and […]
The post When Business Growth Strategy Drives Data Strategy appeared first on DATAVERSITY.
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Author: Carlos Cruz
I was privileged to deliver a workshop at Enterprise Data World 2024. Publishing this review is a way to express my gratitude to the fantastic team at DATAVERSITY and Tony Shaw personally for organizing this prestigious live event. Participating in such events has multiple advantages, including becoming familiar with trending topics in the data management […]
The post Enterprise Data World 2024 Takeaways: Trending Topics in Data Management appeared first on DATAVERSITY.
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Author: Irina Steenbeck