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• 10 data governance challenges that can sink data operations

  No organization can successfully use data without data governance. Addressing 10 data governance challenges is necessary to avoid financial loss and reputation damage.  Continue Reading

• Metaplane app adds data observability for Snowflake users

  The data observability specialist's new native app for the data cloud enables users to monitor data quality as they develop the analytics and AI tools that inform decisions.  Continue Reading

• How does enterprise data protection in Copilot work?

  EDP is Microsoft's way of ensuring that any data that users expose through Copilot queries doesn't end up with third parties and isn't used to train the AI model.  Continue Reading

• NoSQL database types explained: Column-oriented databases

  Learn about the uses of column-oriented databases and the large data model, data warehouses and high-performance querying benefits the NoSQL database brings to organizations.  Continue Reading

• NoSQL database types explained: Key-value store

  Utilizing a key-value store can improve data processing speeds and scalability for data operations that do not require complex queries or analytics.  Continue Reading

• 9 metadata management standards examples that guide success

  Organizations looking to implement metadata management can choose from existing standards that support archiving, sciences, finance and other kinds of digital resources.  Continue Reading

• Top 5 metadata management best practices

  Organizations must craft a strategy, assemble a team and adopt standards to develop a strong metadata management strategy and ensure data accuracy, consistency and quality.  Continue Reading

• Metadata management tools: 9 top options to fulfill key needs

  Metadata management tools can range from comprehensive packages of many features to masters of a specific niche. Consider 9 of the top options to find which fits your needs best.  Continue Reading

• 5 data governance framework examples

  Data governance isn't plug and play: Organizations must select which data governance framework best fits their business goals and needs.  Continue Reading

• 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

• Successful data operations follow a data governance roadmap

  Implementing a data governance strategy requires a roadmap to keep everyone on track and overcome challenges. Follow eight key steps for best results.  Continue Reading

• How to use Microsoft Copilot in Power BI

  Enable Microsoft Copilot in Power BI to automate key features using generative AI capabilities that improve insights and accelerate decision-making.  Continue Reading

• Use RAG with LLMs to democratize data analytics

  Pairing retrieval-augmented generation with an LLM helps improve prompts and outputs, democratizing data access and making previously elusive information available to more users.  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

• Cloud database comparison: AWS, Microsoft, Google and Oracle

  Here's a look at the rival cloud database offerings from AWS, Google, Microsoft and Oracle based on their product breadth, migration capabilities and pricing models.  Continue Reading

• Top 6 data fabric tools providers

  Organizations that want a data fabric architecture should consider how six top vendors use their suite of tools to support a range of data management, automation and AI capabilities.  Continue Reading

• 7 data modeling techniques and concepts for business

  Three types of data models and various data modeling techniques are available to data management teams to help convert data into valuable business information.  Continue Reading

• How GenAI-created synthetic data improves augmentation

  Synthetic data can enhance the performance and capabilities of data augmentation techniques. Navigate the challenges generative AI models present to reap the benefits.  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

• 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

• Data profiling vs. data mining: Why you need both

  Data mining and data profiling have different roles. Using one without the other is not an option for data management operations that want quality data and usable insights.  Continue Reading

• The 5 components of a DataOps architecture

  Reaping the benefits of DataOps requires good architecture. Use five core components to design a DataOps architecture that best fits organizational needs.  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

• Open source vs. proprietary database management

  Open source and commercial databases are alternative options to help streamline data management processes. Examine the pros and cons of each approach.  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

• Vector vs. graph vs. relational database: Which to choose?

  Vector databases enhance the use of generative AI. Organizations should consider how vector capabilities stack up vs. graph and relational databases before deciding which to use.  Continue Reading

• Top 10 industry use cases for vector databases

  Vector database popularity is rising as generative AI use increases across all industries. Here are 10 top use cases for vector databases that generate organizational value.  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

• On-premises vs. cloud data warehouses: Pros and cons

  Data warehouses increasingly are being deployed in the cloud. But both on-premises and cloud data warehouses have pluses and minuses to consider, as explained here.  Continue Reading

• Cloud DBA: How cloud changes database administrator's role

  Cloud databases change the duties and responsibilities of database administrators. Here's how the job of a cloud DBA differs from what an on-premises one does.  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

• Top 12 data observability use cases

  Experts identify 12 top data observability use cases and examine how they influence all aspects of data management and governance operations.  Continue Reading

• ESG data collection: Beginning steps and best practices

  Sustainability initiatives won't succeed without quality data. Following an ESG data collection framework and best practices ensures program and reporting success.  Continue Reading

• Assemble the 6 layers of big data stack architecture

  Assemble the six layers of a big data stack architecture to address the challenges organizations face with big data, which include increases in data size, speed and structure.  Continue Reading

• How to create a data quality management process in 5 steps

  Data quality requires accurate and complete data that fits task-based needs. These five steps establish a data quality management process to ensure data fits its purpose.  Continue Reading

• Mainframe databases teach an old dog new survival tricks

  Long predicted to fade away in favor of more modern architectures, mainframes still play an integral role in corporate IT strategies, thanks to advances in database software.  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 stewardship: Essential to data governance strategies

  As data governance gets increasingly complicated, data stewards are stepping in to manage security and quality. Without one, organizations lose speed, quality info and opportunity.  Continue Reading

• 5 pillars of data observability bolster data pipeline

  Data observability provides holistic oversight of the entire data pipeline in an organization. Use the five pillars to ensure efficient, accurate data operations.  Continue Reading

• What key roles should a data management team include?

  These 10 roles, with different responsibilities, are commonly a part of the data management teams that organizations rely on to make sure their data is ready to use.  Continue Reading

• Data tenancy maturity model boosts performance and security

  A data tenancy maturity model can boost an organization's data operations and help improve the protection of customer data. Improvement is tracked through tiers of data tenancy.  Continue Reading

• Data observability benefits entire data pipeline performance

  Data observability benefits include improving data quality and identifying issues in the pipeline process, but also has challenges organizations must solve for success.  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

• 7 expert recommended data observability tools

  Commercial data observability tools can offer organizations pre-built components and plenty of vendor support for data use cases including monitoring, security and decision-making.  Continue Reading

• 6 data observability open source tools to consider

  Learn about six data observability open source options helping organizations pursue data science experiments that are more budget-friendly and flexible than commercial tools.  Continue Reading

• 4 data quality challenges that hinder data operations

  Data quality challenges pose a threat to organizations' decision-making. Inaccurate, inconsistent, missing and duplicate data poses threats to cultivating trustworthy data sets.  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

• How to overcome the top 5 DataOps challenges

  DataOps is a new tool for effective data use and improved data-driven decision-making. Organizations should prepare for these five DataOps challenges and learn how to overcome them.  Continue Reading

• The future of DataOps trends in 2023 and beyond

  DataOps is a growing tool for organizations looking to efficiently distribute accurate data to users. Learn the DataOps trends teams must understand as it evolves.  Continue Reading

• How to build an effective DataOps team

  More organizations are turning to DataOps to bolster their data management operations. Learn how to build a team with the right people to ensure DataOps success.  Continue Reading

• 7 top data quality management tools

  Data quality management tools help organizations automate and fill gaps in data processes from lacking quality to dated analytics. Here are seven of the top tools in the market.  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

• Key roles and responsibilities of the modern chief data officer

  Chief data officer roles and responsibilities are expanding beyond data strategy, as they are increasingly tasked with cultivating a data-driven culture.  Continue Reading

• What is data lineage? Techniques, best practices and tools

  Organizations can bolster data governance efforts by tracking the lineage of data in their systems. Get advice on how to do so and how data lineage tools can help.  Continue Reading

• The evolution of the chief data officer role

  Chief data officers are taking on additional responsibilities beyond data management as they strive to transform organizations' data culture and focus on value creation.  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

• 10 trends shaping the chief data officer role

  As data use increases and organizations turn to business intelligence to optimize information, these 10 chief data officer trends are shaping the role.  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

• 6 key steps to develop a data governance strategy

  Data governance shouldn't be built around technology, but the other way around. Existing infrastructure, executive support, data literacy, metrics and proper tools are essential.  Continue Reading

• 7 best practices for successful data governance programs

  A comprehensive, companywide data governance program strengthens data infrastructure, improves compliance initiatives, supports strategic intelligence and boosts customer loyalty.  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

• 5 key elements of data tenancy

  Data tenancy is a key piece of any data protection scheme and can be crafted around five building blocks to provide safe, secure data access to users.  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

• 10 big data challenges and how to address them

  Bringing a big data initiative to fruition requires an array of data skills and best practices. Here are 10 big data challenges enterprises must be ready for.  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

• 5 challenges IT faces using open source data management

  There are a variety of open source challenges for data management software, including lack of real-time BI access, miscalculating costs and underestimating the resources required.  Continue Reading

• Business shift to a data monetization strategy elevates CDOs

  As their focus dramatically swings from compliance issues to data monetization, chief data officers are on track to take their rightful place among C-level executives, but slowly.  Continue Reading

• NoSQL database types explained: Document-based databases

  NoSQL document-based databases store information in documents with specific keys, similar to a key-value store, but with different benefits and disadvantages.  Continue Reading

• The challenges of cloud data management

  Cloud platforms are expanding rapidly, causing organizations to face new cloud management challenges keeping pace with cloud data management advancements.  Continue Reading

• The benefits and pitfalls of cloud-based data management systems

  Learn the benefits of cloud-based data management systems, common pitfalls and strategies when considering varying data levels and industry needs.  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

• Best practices for cloud database management systems

  Learn best practices to streamline cloud database management to benefit business performance, compliance audits and business continuity.  Continue Reading

• Data architecture vs. information architecture: How they differ

  Data architects collect the statistics and information architects put the numbers into context as they work symbiotically to bolster an enterprise's data and business strategies.  Continue Reading

• 9 steps to a dynamic data architecture plan

  Learn the nine steps to a comprehensive data architecture plan, including C-suite support, data personas, user needs, governance, catalogs, SWOT, lifecycles, blueprints and maps.  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

• How to build a successful cloud data architecture

  As enterprises vacate the premises and migrate their operations skyward, a cloud data architecture can provide the long-term flexibility to improve workflows, costs and security.  Continue Reading

• Data modeling vs. data architecture: What's the difference?

  Data modelers and data architects have distinctly different roles, but they work in a complementary fashion to help enterprises unlock and capitalize on data's business value.  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

• 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

• Trusted data is among governance, data integration benefits

  Well-conceived governance programs injected with data integration tools can overcome the inherent distrust companies have in their own data stored in multiple systems.  Continue Reading

• Developing an enterprise data strategy: 10 steps to take

  Consultants detail 10 to-do items for data management teams looking to create a data strategy to help their organization use data more effectively in business operations.  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

• 7 steps to a successful data lake implementation

  Flooding a Hadoop cluster with data that isn't well organized and managed can stymie analytics efforts. Take these steps to help make your data lake accessible and usable.  Continue Reading

• SQL Server database design best practices and tips for DBAs

  Good database design is a must to meet processing needs in SQL Server systems. In a webinar, consultant Koen Verbeeck offered advice on how to make that happen.  Continue Reading

• SQL Server in Azure database choices and what they offer users

  SQL Server databases can be moved to the Azure cloud in several different ways. Here's what you'll get from each of the options for migrating SQL Server to Azure.  Continue Reading

• 10 cloud database migration mistakes to avoid

  Database expert Chris Foot lists the top 10 oversights IT teams commonly make when undertaking a cloud database migration and offers tips on how to avoid them.  Continue Reading