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• 6 dimensions of data quality boost data performance

  Poor data quality can lead to costly mistakes and bad decision making. The six dimensions of data quality ensure accurate, complete, consistent, timely, valid and unique data. Continue Reading

• Understand Microsoft Copilot security concerns

  Microsoft Copilot raises security concerns around unauthorized or unintentional data access. Prevent leaks with vigilant oversight and comprehensive user access reviews. Continue Reading

• How to make a metadata management framework

  Don't wait until you have a metadata management problem to address the issue. Put a metadata management framework in place to prepare for potential issues. Continue Reading

• 10 top vector database options for similarity searches

  Vector databases excel in different areas of vector searches, including sophisticated text and visual options. Choose the platform that best fits organizational needs. Continue Reading

• How to use a data governance maturity model

  A data governance maturity model identifies where current operations are lacking and how to make improvements that better protect and use data. Continue Reading

• IoT data management extends best practices to the edge

  IoT devices generate and collect data from points all over the network. Organizations must apply general data management best practices to get the most value from IoT data.Continue Reading

• Evaluating data quality requires clear and measurable KPIs

  KPIs and metrics are necessary to measure the quality of data. Organizations can use the dimensions of data quality to establish metrics and KPIs for their specific needs.Continue Reading

• Multi-cloud databases: How to deploy and manage them

  Deploying databases on different cloud platforms offers various benefits. Here's a set of 10 best practices for building a multi-cloud database architecture.Continue Reading

• Evaluate and choose from the top 10 data profiling tools

  Any effective data quality process needs data profiling. Evaluate key criteria to select which of the top 10 data profiling tools best fits your needs.Continue Reading

• RDBMS (relational database management system)

  A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database.Continue Reading

• Managing databases in a hybrid cloud: 10 key considerations

  To manage hybrid cloud database environments, consider business and application goals, consistency, configuration management, synchronization, latency, security and stability.Continue Reading

• 10 best practices for managing data in microservices

  Data architects managing loosely coupled microservices applications need to make the right decisions about databases, data ownership, sharing, consistency and failure recovery.Continue Reading

• AI boosts efficiency in data management

  AI can automate tasks across every aspect of the data management process, enabling data teams to focus on models, not labeling and graphing.Continue Reading

• 7 steps to create a data loss prevention policy

  Data loss prevention is an ever-changing process of proactive and reactive protection and planning. Read on to learn how to set up a successful DLP policy.Continue Reading

• Use these 10 steps to successfully build your data culture

  Building a data culture starts at the top level of any organization. These 10 steps can help guide leadership through the key aspects any data culture needs to succeed.Continue Reading

• Data management trends: GenAI, governance and lakehouses

  The top data management trends of 2023 -- generative AI, data governance, observability and a shift toward data lakehouses -- are major factors for maximizing data value in 2024.Continue Reading

• Should you run your database on premises or in the cloud?

  Use of cloud databases is surging, but there are still reasons for on-premises ones. Here's a comparison of cloud and local database architectures to help you choose.Continue Reading

• Top trends in big data for 2024 and beyond

  Big data is driving changes in how organizations process, store and analyze data. The benefits are spurring even more innovation. Here are four big trends.Continue Reading

• Do traditional data stacks have a use versus modern options?

  Traditional data stacks differ from modern data stacks in the use of cloud and advanced analytics tools. Update on-premises data stacks to gain niche advantages over cloud options.Continue Reading

• Use knowledge graphs with databases to uncover new insights

  Knowledge graphs work with graph databases to offer different data storage options than a traditional database, particularly in the biomedical, financial and product sectors.Continue Reading

• Data mesh aids democratization with decentralization

  Real-time analytics enables faster decision-making and insights. As data democratization rises in importance, data mesh helps decentralize that data for all users.Continue Reading

• Enhance data governance with distributed data stewardship

  Data stewardship and distributed stewardship models bring different tools to data governance strategies. Organizations need to understand the differences to choose the best fit.Continue Reading

• Data management trends: Convergence and more money

  The past year focused heavily on data intelligence, lakehouse development and observability as vendors innovated to help enterprises make effective use of converged data and technologies.Continue Reading

• 5 steps to an improved data quality assurance plan

  Follow these steps to develop a data quality assurance plan and management strategy that can help identify data errors before they cause big business problems.Continue Reading

• Make data usability a priority on data quality for big data

  To help make big data analytics applications more effective, IT teams must augment conventional data quality processes with measures aimed at improving data usability for analysts.Continue Reading

• 7 data quality best practices to improve data performance

  Data quality is essential to operate a successful data pipeline and enable data-driven decision-making. These seven data quality best practices can help improve performance.Continue Reading

• How CDOs manage cloud adoption and hybrid cloud compliance

  Cloud advancements are changing how chief data officers approach cloud data management as they juggle security, privacy and other hybrid cloud compliance issues.Continue Reading

• 6 CDO challenges that hinder data-driven initiatives

  Chief data officers often run into difficulties getting projects off the ground. Here are six challenges hindering modern CDOs' data-driven projects and strategies.Continue Reading

• How to build a data catalog: 10 key steps

  A data catalog helps business and analytics users explore data assets, find relevant data and understand what it means. Here are 10 important steps for building one.Continue Reading

• Top benefits of data governance for businesses

  Effective data governance provides a variety of benefits to organizations, including improvements in operational efficiency, data quality and business decision-making.Continue Reading

• How to evaluate and optimize data warehouse performance

  Organizations build data warehouses to satisfy their information management needs. Data warehouse optimization can help ensure that these warehouses achieve their full potential.Continue Reading

• Understanding the benefits of a data quality strategy

  A data quality strategy can improve an organization's ability to generate value from data, but determining quality depends on the processes and use cases.Continue Reading

• 8 proactive steps to improve data quality

  Here are eight steps to take to improve your organization's data quality in a proactive way, before data errors and other issues cause business problems.Continue Reading

• 3 considerations for a data compliance management strategy

  A data compliance management strategy is key for organizations to protect data the right way. Different positions have responsibility to ensure industry regulations are met.Continue Reading

• Why businesses should know the importance of data quality

  Data quality, building data trust and identifying bias are critical for organizations to confidently make decisions based on the data they collect.Continue Reading

• 10 key elements to follow data compliance regulations

  Data privacy laws are changing around the world on a constant basis. These 10 elements can help keep organizations up to speed with data compliance regulations.Continue Reading

• NLP and AI boost the automated data warehouse

  Businesses are working to automate as many elements of their data warehouses as they can through nascent tools like augmented analytics and natural language processing.Continue Reading

• 'Building the Data Lakehouse' explores next-gen architecture

  This book excerpt by 'father of the data warehouse' Bill Inmon and experts Mary Levins and Ranjeet Srivastava explores the latest methods for wrangling data into usable intel.Continue Reading

• NoSQL database types explained: Graph

  NoSQL graph databases focus on the relationships between pieces of data. Two common frameworks bring different advantages and disadvantages over other NoSQL database types.Continue Reading

• Top 5 elements needed for a successful data warehouse

  While conventional data warehouses may struggle to keep up with growing volumes of data, these five elements best give the ability to tap into valuable BI.Continue Reading

• 6 strategies to tap into data warehouse BI

  Data warehouse BI benefits include data storage, summarization and transformation and can be unlocked with these six strategies leveraging cloud architectures.Continue Reading

• NoSQL database types explained: Key-value store

  Learn about the benefits and detriments of utilizing a key-value store -- a simply designed NoSQL database that can potentially improve data processing speeds and scalability.Continue Reading

• 6 key components of a successful data strategy

  These six elements are essential parts of an enterprise data strategy that will help meet business needs for information when paired with a solid data architecture.Continue Reading

• 5 principles of a well-designed data architecture

  Here are five core data architecture principles to help organizations build a modern architecture that successfully meets their data management and analytics needs.Continue Reading

• EY CTO outlines data governance challenges

  Multinational professional services firm EY has taken a strategic view of how to manage and use data in a federated approach powered by a trusted data fabric.Continue Reading

• Data governance strategies for today's evolving IT landscape

  With remote work becoming part of the norm and as companies migrate more data into the cloud, it's imperative that companies review their data governance strategy.Continue Reading

• The enterprise advantages of automated data collection

  Many organizations still rely on manual data entry that wastes time and results in low-quality data. Here are the latest automated data collection techniques and their benefits.Continue Reading

• How automated metadata management improves business insights

  Automating metadata management can cut down time spent on tasks such as data tagging and cataloging. Explore how automated metadata management is improving data quality.Continue Reading

• Building a big data architecture: Core components, best practices

  To process the infinite volume and variety of data collected from multiple sources, most enterprises need to get with the program and build a multilayered big data architecture.Continue Reading

• Data warehouse environment modernization tools and tips

  A data warehouse environment is made up of many tools and systems. Read on to learn the history of the modern data warehouse and how they're currently evolving.Continue Reading

• Cloud data catalog benefits for the enterprise

  Vendor cloud data catalog options are expanding and offering more automated tools to end users. Read on to find out how enterprises can benefit from these options.Continue Reading

• Pandemic exposes difficulty of data management in education

  Limited resources and a shift to remote learning have shown the inequalities across school districts when it comes to data management and the negative impact this can have.Continue Reading

• Data lineage documentation imperative to data quality

  Understanding the detailed journey of data elements throughout the data pipeline can help an enterprise maintain data quality and improve trustworthiness.Continue Reading

• Data anonymization best practices protect sensitive data

  See how data anonymization best practices can help your organization protect sensitive data and those who could be at risk if that data identified them.Continue Reading

• Maintaining data integrity key for data quality

  Maintaining data integrity through improved communication and data literacy is paramount for organizations in the enterprise seeking to ensure data quality and trust.Continue Reading

• Investment in talent key for data quality in healthcare

  Investing in training employees on proper data gathering and management practices is crucial for healthcare organizations seeking to ensuring data quality and patient care.Continue Reading

• Top 5 feature engineering tips for better models

  From understanding a model's expected goal to factoring in subject matter expertise, experts talk about the best ways to improve your feature engineering.Continue Reading

• Key points for a monitoring center pandemic action plan

  On-site monitoring centers come under stress when it's necessary for most workers to telecommute. Here are key points to include in a crisis plan to continue service availability.Continue Reading

• Surprising insight on the data governance process

  Many enterprise data governance processes have been around for a while. Read on for ways a modern approach can be beneficial, and preview a new book that can update your strategy.Continue Reading

• Data integration technologies unify multiple data stores

  Manual data integration can still benefit small and medium businesses, but a prolonged manual process can slow and detract from actionable work by data professionals.Continue Reading

• Organization and automation ease data preparation process

  By laying down proper groundwork and investing in automated checks, companies can ease the data preparation process and ensure they are getting the most out of their data.Continue Reading

• Big data security management embraces governance, privacy

  Cyberattacks, GDPR and CCPA compliance, and COVID-19 present serious security and privacy challenges for managers tasked with protecting their data stores.Continue Reading

• GDPR, CCPA, cloud drive security management tool makeovers

  As data protection and privacy laws like GDPR and CCPA take hold, data managers refine governance practices, while vendors enhance traditional big data security tools.Continue Reading

• Building a strong data analytics platform architecture

  A strong analytics platform is key to meeting demands in a data-driven business. Read on for the key components your platform architecture should include.Continue Reading

• When a DIY database management system design is the best fit

  Learn how a combination of homegrown, off-the-shelf and open source tools, plus proper motivation, can yield a DIY DBMS that meets corporate expectations, needs and ROI.Continue Reading

• Building a database application the DIY way

  Business users experience the trials, tribulations and exultations of building a DIY DBMS, especially when IT expertise is not readily available or costs are too high.Continue Reading

• How to build an effective streaming data architecture

  Data architecture can be tricky when it comes to real-time analytics. Clear objectives and scalability are important factors when determining the streaming data architecture you need.Continue Reading

• 6 best practices on data governance for big data environments

  Efforts to govern big data must corral a mix of structured and unstructured data. That's a challenge for most organizations. These six action items will help.Continue Reading

• Data governance roles and responsibilities: What's needed

  Data governance requires a team effort. Experts offer advice on how to structure and implement data governance roles that engage business users across the enterprise.Continue Reading

• Should you host your data lake in the cloud?

  On premises or in the cloud: What's the better place for your data lake? Here are some things to consider before deciding where to deploy a big data environment.Continue Reading

• Data warehousing design and value change with the times

  Big data, the cloud and analytics profoundly shape data warehouse purpose and design. Learn how companies derive value from a repository that at times needs definition.Continue Reading

• Analytics demands add loftier goals to data warehouse strategies

  As the concept of storing data and the technologies needed to do it evolve, companies with set goals in mind are building their data warehouses to maximize analytics outcomes.Continue Reading

• How to improve data governance for self-service analytics

  Citizen data scientists and self-service analytics are on the rise as the data scientist shortage continues. Here are some data management best practices to support them.Continue Reading

• Data catalog best practices rely on teamwork, governance, tools

  This handbook provides advice on creating data catalogs to help data analysts and other users find information in corporate systems, plus insight on data catalog trends.Continue Reading

• Data catalog management for analytics fraught with unique demands

  Data catalogs for analytics applications demand detailed assessment of user needs, cross-functional teams, ready access, continuous improvement and a self-sustaining system.Continue Reading

• Data warehouse technologies evolve as vendors look skyward

  The need for high-speed analytics and low latency are pushing data warehousing vendors to create and reinvent their platforms and, in some cases, reinvent themselves.Continue Reading

• How to streamline feature engineering for machine learning

  Structured data is necessary in machine learning, but sifting through data is time consuming. Streamlining the feature engineering process can help data scientists be more productive.Continue Reading

• Top database cloud migration considerations for enterprises

  Many organizations are switching to cloud databases and big data platforms. But understanding what option best meets your data needs is an important first step.Continue Reading

• Managing unstructured data is crucial to enterprises' AI goals

  Unstructured data makes up a huge portion of most businesses' data volume. But, with data-hungry AI systems coming online, making sense of these stores has never been more important.Continue Reading

• How enterprises navigate GDPR data management rules

  For businesses that operate in the EU, complying with GDPR has to be a top priority. And in many, much of the compliance burden falls on the data management staff.Continue Reading

• DOD CDO shares 7 data management best practices

  The DOD's first chief data officer, Michael Conlin, shares tips on how organizations can modernize their data management practices with the objective of matching private enterprise.Continue Reading

• Align business and IT drivers through data quality best practices

  Information management consultants talk about the importance of aligning business and IT drivers for a holistic architectural approach to ensuring strong data quality.Continue Reading

• Third-party database tools boast attractive alternatives

  For companies considering third-party database tools, this handbook provides expert advice on evaluating and deploying on-premises and cloud options from third parties.Continue Reading

• Database management tools from third parties make natives restless

  Third-party database performance tools offer attractive alternatives to management software from DBMS vendors, provided their capabilities include orchestration, governance and integration.Continue Reading

• GDPR, AI intensify privacy and data protection compliance demands

  This guide covers the challenges data management teams face on data protection and privacy, particularly with the rise of GDPR and similar laws and the growing use of AI tools.Continue Reading

• Information Builders exec talks data management ethics

  In this Q&A, James Cotton, director of the Data Management Centre of Excellence at Information Builders, offers advice on how organizations can carry out ethical data management.Continue Reading

• Using a LEFT OUTER JOIN vs. RIGHT OUTER JOIN in SQL

  In this book excerpt, you'll learn LEFT OUTER JOIN vs. RIGHT OUTER JOIN techniques and find various examples for creating SQL queries that incorporate OUTER JOINs.Continue Reading

• Data modeling techniques to overcome common business challenges

  In this interview, author and data modeling instructor Steve Hoberman discusses techniques for dealing with challenges that may arise in the data modeling process.Continue Reading

• How 4 organizations are breaking down data silos

  Siloed data continues to inhibit enterprise efficiency. Here, IT professionals discuss problems their organizations are facing around data silos -- and how they're solving them.Continue Reading

• Building leaner, meaner BI data sources

  As business intelligence analysis and reporting platforms become increasingly important in the enterprise, so does the data that feeds them. Are your BI data sources up to par?Continue Reading

• Data virtualization tools promote anywhere, anytime data access

  This online handbook examines data virtualization software and how organizations are deploying and using the technology as part of their data integration processes.Continue Reading

• Data virtualization benefits seen in unified views, IT agility

  Through in-place integration, data virtualization platforms can provide wider access to data and simplify security and governance. But they come with some limitations.Continue Reading

• Key features to create a SQL Server audit trail in databases

  SQL Server offers a set of built-in auditing tools that can help make the process of tracking logins and other database activities easier for database administrators.Continue Reading

• Data virtualization layer feeds logical data warehouse, Agile BI

  Indiana University is using data virtualization to combine data from various source systems for analysis, as part of an initiative to improve strategic decision-making.Continue Reading

• Data modeling best practices power analytics, business apps

  Data modeling is a key part of data management and analytics. This handbook highlights best practices for creating data models and new functionality in modeling tools.Continue Reading

• SQL Server performance tuning best practices for DBAs

  Tuning database performance is a complex process, but consultant Joey D'Antoni details a list of SQL Server performance tuning best practices that can make it easier.Continue Reading

• How data staging helped Walgreens transform its supply chain

  Walgreens built a centralized data warehouse to give supply chain partners a better view into its data -- but analytics were slow. That's where a data staging tier came in.Continue Reading

• How to navigate the challenges of the data modeling process

  Data modeling and curation can help businesses more efficiently use data they've collected. There are challenges, however -- beginning with ensuring data quality.Continue Reading

• 8 tips to improve the data curation process

  A data curation and modeling strategy can ensure accuracy and enhance governance. Experts offer eight best practices for curating data. First, start at the source.Continue Reading

• SQL Server auditing best practices: 3 key questions for DBAs

  Acing a SQL Server database audit starts with careful monitoring of how sensitive data is accessed and used so you can answer the top questions that auditors ask.Continue Reading