Tips

Tips

• 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

• 6 dimensions of data quality boost data performance

  Generate accurate data analysis and predictions by mastering the six dimensions of data quality -- accuracy, consistency, validity, completeness, uniqueness and integrity.  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

• 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

• 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

• 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

• How to plan and manage a multi-cloud database environment

  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

• 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

• Managing databases in a hybrid cloud: 8 key considerations

  To manage and monitor hybrid cloud database environments, consider business and application goals plus costs, latency, security, stability, simplicity, tools and technical skills.  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

• Open source vs. proprietary database management

  Open source and cloud data management are becoming popular options to streamline information data management processes. Also, examine the benefits of on-site, proprietary strategies.  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

• 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

• 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

• 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

• 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

• 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

• 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 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

• The evolution of the data preparation process and market

  Organizations have long struggled with inconsistent data and other issues. Expert Andy Hayler explores how that has led to the rise of the data preparation tools market.  Continue Reading

• Check SQL Server Query Store performance impact before using

  Many IT teams hesitate to use SQL Server Query Store due to performance concerns. Consultant Andy Warren offers tips on how to test and get started with Query Store.  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

• Pros and cons of using SQL Server audit triggers for DBAs

  Using triggers to capture audit information in SQL Server can be instrumental in keeping track of database use and changes. But they aren't a perfect fit for all cases.  Continue Reading

• 5 things to know about deploying big data systems in data containers

  Planning for security and container APIs, and watching out for infrastructure sprawls are some issues to be aware of before deploying big data in containers.  Continue Reading

• Google Cloud Spanner overview: 4 features to consider

  Cloud Spanner's ability to offer data consistency and horizontal scalability has helped the relational database service gain traction. Learn more about its architecture.  Continue Reading

• How to resolve and avoid deadlocks in SQL Server databases

  Deadlocks are a real hindrance to SQL Server users, but database administrators can avoid them by taking steps to limit them and stop them from recurring.  Continue Reading

• Why data silos matter: Settling ownership of data issues

  Data management is often still seen as an IT task, but that can lead to data silos. Find out why the business should be in charge of its data as part of a governance process.  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

• Why organizations need a solid data governance strategy

  The flood of data flowing into data warehouses, data lakes and other systems makes effective data governance a must for successful business analytics initiatives.  Continue Reading

• SQL Server 2019 improves Linux, container support

  The SQL Server 2019 release includes new big data integration features, a collection of database engine enhancements and improved Linux and container support.  Continue Reading

• Trifacta data prep tool helps blend disparate data sources

  Handling diverse data sources usually consumes precious developer time. That led healthcare CRM company SymphonyRM to hand the data prep task to business analysts.  Continue Reading

• How data duplication in healthcare is diagnosed

  Electronic health record systems have helped reduce duplicate patient data in hospitals -- but they haven't cured the problem. Find out how organizations are addressing the issue.  Continue Reading

• 5 FAQs on SQL Server containers and how to manage them

  Running SQL Server in containers creates new challenges for database administrators. The answers to these questions can guide you through some of them.  Continue Reading

• The Power BI-PowerShell cmdlet cheat sheet

  DBAs can manage Power BI data sets, workspaces and reports with PowerShell. Using the two tools together makes for a more efficient and effective workflow.  Continue Reading

• How to build a master data index: Static vs. dynamic indexing

  Expert David Loshin explores the differences between static and dynamic indexing in master data management systems, and which queries each approach can support.  Continue Reading

• How deterministic and probabilistic matching work

  Expert David Loshin explores the benefits and challenges of the two classes of record matching in master data management systems: deterministic matching vs. probabilistic matching.  Continue Reading

• 11 features to look for in data quality management tools

  As the need for quality data has increased, so have the capabilities of data quality tools. Learn how collaboration, data lineage and other features enable data quality.  Continue Reading

• The gradual evolution of master data management software

  Master data management began with a bang, then hit roadblocks due to complexity. Now, MDM is shifting toward more pragmatic projects tied to data governance.  Continue Reading

• 4 strategies to beat the SQL Server 2008 support cutoff

  The looming end of SQL Server 2008 extended support should spur database teams into action. Consider these four options to extend security updates or migrate your aging SQL Server systems.  Continue Reading

• SQL Server high availability best practices in the cloud

  The cloud isn't immune to system failures. You can increase database uptime and prevent data losses with these high availability best practices for SQL Server in the cloud.  Continue Reading

• 2 ways to attach SQL Server database files to Linux containers

  SQL Server files can be stored outside of Docker containers in host directories or volumes. Here's how to set up SQL Server on Linux databases and attach them to containers.  Continue Reading

• Why data quality tools matter in today's organizations

  Data is everywhere -- but is it good? Data quality tools could answer that question and help save time and money. So why don't more organizations use the technology?  Continue Reading

• GDPR checklist items data managers might have overlooked

  Now that the General Data Protection Regulation has been implemented, it's a good time for organizations to make sure they haven't missed any GDPR best practices.  Continue Reading

• How to attach databases to custom SQL Server containers

  Deploying SQL Server in Docker containers for production applications typically requires custom containers. Here are guidelines on how to attach databases to them.  Continue Reading