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• '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

• Top trends in big data for 2021 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

• 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

• Should you host your database on-site or in the cloud?

  There's been a surge of movement to the cloud in recent years, but there are still some benefits to an on-site database. Read on for a comparison of on-site and cloud platforms.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

• Using DataOps to create business value from big data

  Now is the time to take all that data your organization has amassed through the years and, with DataOps, use it to make actionable business decisions.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

• What steps are key to building a data catalog?

  An enterprise data catalog can help data stewards and other users in an organization manage metadata and explore data assets. Here are 10 key steps for creating a data catalog.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

• 5 benefits of building a strong data governance strategy

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

• How data lineage tools boost data governance policies

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

• Proactive practices for data quality improvement

  Instead of waiting for data quality to become an issue, consider a proactive approach. Here are ways to improve data quality in your organization before it's a problem.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

• 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

• 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

• 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

• 5 steps to an improved data quality assurance plan

  Consultant David Loshin offers tips on developing a data quality strategy that can help identify data errors before they cause big business problems.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

• 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

• 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

• 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

• 5 best practices for managing real-time data integration

  Real-time data integration isn't like traditional data integration -- "it's moving, it's dirty and it's temporal," cautions one data pro. Experts offer up some best practices.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

• Data management trends for 2019: Governance, DataOps, cloud

  Better data governance, increased cloud use and wider DataOps adoption head the list of trends for data management teams to plan for in 2019, IT analysts say.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

• 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

• Big data platform broadens place in analytics architecture

  Big data platforms stumbled a bit getting out of the prototyping stage. But a view from the Strata Data Conference in New York sees broader use in the offing.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

• Mining equipment maker uses BI on Hadoop to dig for data

  BI on Hadoop is still new, but moving BI to data is trending. A data scientist working with IoT data at Komatsu sees the importance of getting big data to the right people.Continue Reading

• What is the best structure for data governance programs?

  The optimal approach to a data governance framework includes a program team, a data governance council and stewards. Expert Anne Marie Smith explains what role each group plays.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 manage data and the GDPR right to be forgotten

  The General Data Protection Regulation requires organizations to delete personally identifiable information if customers ask -- which can be a challenge. Experts offer four tips.Continue Reading

• How graph data modeling can help evaluate database tools

  Mapping data to a graph model can be challenging, but these steps can help you create prototype applications -- which then can aid in evaluating graph databases.Continue Reading

• When to choose an S3 big data environment over HDFS storage

  Selecting a storage service for big data in the cloud can be challenging. Expert David Loshin explains usage patterns that could lead organizations to Amazon Simple Storage Service.Continue Reading

• Four first steps for customer data management

  Forrester's Mike Gualtieri details how to develop a unified plan to manage customer data that gives business users what they need to manage CRM programs.Continue Reading

• Evolving data integration strategies target new analytics needs

  Most companies don't have a shortage of data, but it's often stored in siloed systems or inconsistent formats -- problems that data integration programs need to address.Continue Reading

• What goes into a customer analytics data integration framework

  Customer data integration is a minefield for IT teams to navigate. But incorporating a set of core technical functions into an integration architecture can ease the process.Continue Reading

• Six data risk management steps for GDPR compliance

  By layering data risk management processes into your data governance framework, you can ensure the personally identifiable information your company stores meets GDPR compliance rules.Continue Reading

• CockroachDB database wagered on for scalability boost, GDPR

  CockroachDB is an open source distributed database designed for processing on a global scale, a need that online gambling company Kindred Group hopes it can meet.Continue Reading

• GDPR data protection edicts make good data governance a must

  The European Union's new GDPR law puts the onus on companies to ensure that their data governance and management practices enable them to comply with its requirements.Continue Reading

• GDPR rules can spur broader steps to protect sensitive data

  Companies need to make compliance with GDPR's requirements on managing personal data a priority, but they should also work to implement wider data protection efforts in the future.Continue Reading

• Security of big data fashions a whole new look with GDPR

  As data managers scramble to protect their precious lakes of washed and unwashed data from the evils of hacking, malware, ransomware and botnets, there comes a privacy regulation of European Union origin that could change the way many U.S. companies...Continue Reading

• IoT, edge computing spawn new security issues

  As real-time big data increasingly hitches up to internet of things, edge computing power and fog nodes, a whole new layer of security threats emerges.Continue Reading

• IT teams take big data security issues into their own hands

  Data security needs to be addressed upfront in deployments of big data systems -- and users are likely to find they have to build some security capabilities themselves.Continue Reading

• Information architecture applied to big data streaming, AI

  New technologies challenge data professionals, but taking a step back helps with hurdles. In this interview, consultant William McKnight takes a measured look at data streaming, GDPR and AI.Continue Reading

• Cloud workloads, data lakes challenge information architecture

  Data management options are expanding; cloud workloads are an example. That means changing your approach to information architecture, says data management expert William McKnight.Continue Reading

• GDPR mandates push data quality improvement into IT spotlight

  Data quality issues cost companies significant amounts of money in lost revenue and added expenses, and their impact will only get bigger when the EU starts enforcing its GDPR law.Continue Reading

• IT, others focus efforts as GDPR implementation date looms

  The EU's General Data Protection Regulation is meant to bring better data privacy to bear in the age of big data. How GDPR implementation will proceed remains to be seen.Continue Reading

• Data expert: GDPR deadline is an opportunity, not a burden

  There is stress as the EU's General Data Protection Regulation compliance deadline nears, but the GDPR privacy movement is a good thing for data policies, advises consultant Daragh O Brien.Continue Reading