Tips

Tips

• 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 seven data modeling techniques are available to data management teams to convert mountains of information into valuable business intelligence.  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

• 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

• 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

• 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

• 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

• 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

• 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

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

  IT expert Chris Foot evaluates the leading cloud databases from AWS, Google, Microsoft and Oracle based on their product offerings and migration capabilities.  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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Good data quality for machine learning is an analytics must

  As companies add machine learning applications, they need to really understand -- and be able to improve -- their data. That's where data quality initiatives come in.  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

• The benefits of columnar storage and the Parquet file format

  What's behind Apache Parquet's growing popularity? It may be the file format's columnar storage orientation, which leads to benefits including improved query performance.  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

• Three factors for protecting sensitive data in the GDPR era

  Data privacy is a hot topic nowadays thanks to GDPR and the Facebook data scandal. But how do data security, access control and data protection differ?  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

• Data integration techniques to help keep BI data consistent

  Data integration processes that aren't managed properly can create inconsistent data in BI and analytics applications. Here are some steps to avoid that problem.  Continue Reading

• Why Spark DataFrame, lazy evaluation models outpace MapReduce

  Learn how the Spark DataFrame execution plan works and why its lazy evaluation model helps the processing engine to avoid the performance issues inherent in Hadoop MapReduce.  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

• 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

• 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

• Data analytics architecture must break down higher ed silos

  Siloed data isn't only a problem for businesses. It's also a big issue for many large universities -- one that their data infrastructures need to resolve for effective analytics.  Continue Reading

• Three ways to turn old files into Hadoop data sets in a data lake

  Hadoop data lakes offer a new home for legacy data that still has analytical value. But there are different ways to convert the data for use in Hadoop depending on your analytics needs.  Continue Reading

• Database architecture design has to guard against DBMS chaos

  The proliferation of database technologies gives organizations more options to meet data processing needs. However, a strong architecture strategy is a must to avoid a DBMS free-for-all.  Continue Reading

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

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

• Database protection methods expand to shield data from attackers

  Database vendors have beefed up the security tools in their software -- and that's a good thing because attackers are increasingly targeting database systems to steal sensitive data.  Continue Reading

• Ingesting data into a data lake may give you data indigestion

  Big data vendors routinely push the notion of ingesting all of your data into a data lake. But in many cases, doing so is an unnecessary step that could cause data ingestion problems.  Continue Reading

• Blooming of master data sources complicates customer MDM efforts

  As companies collect customer data and other types of information from more and more sources, the master data management process is becoming even more complex than it already was.  Continue Reading

• NoSQL data stores offer a mixed menu for managing diverse data sets

  NoSQL databases provide alternatives to relational software for managing pools of big data. But prospective buyers need to discern between the various types of NoSQL technologies.  Continue Reading

• Data governance initiatives get more reliant on data lineage info

  Agile development and big data applications complicate efforts to govern data, but a sharper focus on tracking and managing data lineage can help smooth the governance process.  Continue Reading

• Metadata management tools help data lake users stay on course

  Effective metadata management processes can prevent analytics teams working in data lakes from creating inconsistencies that skew the results of big data analytics applications.  Continue Reading

• Streaming, connectivity new keys to data integration architecture

  The growing use of the cloud, big data and data from outside sources has complicated data integration, making the addition of data streaming and broader connectivity a must.  Continue Reading

• Top issues for data management programs include big data, IoT, cloud

  It's time for big data systems to prove their business value, consultant Andy Hayler says. He also sees growing roles for IoT, the cloud and machine learning in the data management process.  Continue Reading

• Cross-platform integration, data preparation process grows in cloud

  While cloud computing may be convenient and more cost-effective for users, it can also lead to new challenges and requirements in regard to integrating and processing data.  Continue Reading

• Graph data model cements tight relationships between data elements

  Graph databases can help define and discover relationships between entities -- and offer increased flexibility and better usability than relational database management systems.  Continue Reading

• SQL vs. NoSQL database design debate isn't even a real fight

  Consultant David A. Teich says the SQL programming language remains the best means for accessing and querying data, whether it's in relational databases, NoSQL systems or Hadoop clusters.  Continue Reading

• SQL-on-Hadoop platforms open brave new analytics world

  Hadoop has been slowly plodding through the big data jungle, but SQL's integration may put a spring in the elephant's step.  Continue Reading

• New data preparation tools open up more info to analysts

  Consultant David Loshin says self-service data preparation software helps give data analysts broader access to business information than they typically can get in a data warehouse.  Continue Reading

• Relational database design tips to boost performance

  As relational database management systems handle more and more data, a strategic approach to data modeling has become vital.  Continue Reading

• Hadoop data lake not a place for just lounging around

  A new Gartner report says the storage repository isn’t the trouble-free panacea many observers hail it to be. New data governance practices -- and new skills -- are critical.  Continue Reading

• CDO should take lead on data governance process, info strategy

  A chief data officer should take charge of an organization's efforts to develop an enterprise data strategy and institute data governance policies, consultant David Loshin says.  Continue Reading

• Using data profiling techniques -- and estimating the effort required

  Data profiling is a key part of data quality efforts. Here's a simple formula for calculating the amount of time needed to profile a data set.  Continue Reading

• Data steward role needs some shepherding itself

  Consultant David Loshin gives suggestions for building and managing a data stewardship program that can effectively support data governance.  Continue Reading

• Data governance tools: Part, but not all, of the governance puzzle

  While tools designed for data governance are helpful, organizations must also implement best practices and standard processes to be effective.  Continue Reading

• Database administration best practice: Balance DBA team skills

  Balancing development and operations skills is a first step to achieving database administration best practices in-house.  Continue Reading

• Why IT departments should consider a large data warehouse system

  Find out how a large data warehouse system can benefit companies. Learn how enterprise data warehouses help meet business needs. Get tips on operational considerations for a central data warehouse.  Continue Reading

• Understanding the latest data integration strategies and technologies

  Learn about new data integration strategies and technologies that can improve data integration for enterprise IT and departmental data in your organization.  Continue Reading

• Enterprise data integration software: EDI definitions you need to know

  Get concise definitions of enterprise data integration (EDI) software terms -- including customer data integration (CDI), enterprise information integration (EII), enterprise application integration (EAI), master data management (MDM), service ...  Continue Reading

• What is the difference between DB2 UDB and DB2 OS/390?

  Database expert Craig Mullins explains the differences between DB2 UDB and DB2 OS/390. He begins by clarifying that the two are comprised of completely different code bases.  Continue Reading