Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• Vectorized data uses see resurgence with generative AI

  Advancements in generative AI are driving renewed interest in vector databases. Organizations have also found new uses for the established technology. 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

• Generative AI shines spotlight on data governance and trust

  Generative AI creates new opportunities for how organizations use data. Strong data governance is necessary to build trust in the data AI models use. 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

• What is data architecture? A data management blueprint

  Data architecture is a discipline that documents an organization's data assets, maps how data flows through IT systems and provides a blueprint for managing data, as this guide explains.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

• Data readiness unlocks the potential of AI

  AI models rely on data to function. Before implementing AI, make sure your data can support initiatives by evaluating its quality, accessibility, integration and governance.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

• 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

• What is a data protection officer (DPO) and what do they do?

  Today's DPO must juggle technical, legal and collaborative skills in the shadow of more sophisticated data breaches, tougher data privacy laws and generative AI deployments.Continue Reading

• 12 top open source databases to consider

  Open source databases are viable alternatives to proprietary ones. Here's information on 12 open source and source available technologies for weighing database options.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

• Different types of database management systems explained

  The various types of database software come with advantages, limitations and optimal uses that prospective buyers should be aware of before choosing a DBMS.Continue Reading

• Trusted data is the foundation of data-driven decisions, GenAI

  Any organization that wants to drive decision-making with data or use generative AI won't succeed without understanding how to cultivate trusted data.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

• Data management and governance key to successful AI use

  AI's effectiveness is limited by data quality. Building strong data management and governance programs are crucial to handling the challenges that AI presents to organizations.Continue Reading

• Real-time analytics, vector search accelerate time to insights

  Real-time analytics and vector search capabilities reduce time to insights, enabling data-driven organizations to make faster decisions and generate value from their data.Continue Reading

• 18 top data catalog software tools to consider using in 2024

  Numerous tools can be used to build and manage data catalogs. Here's a look at the key features, capabilities and components of 18 prominent data catalog tools.Continue Reading

• 18 top big data tools and technologies to know about in 2024

  Numerous tools are available to use in big data applications. Here's a look at 18 popular open source technologies, plus additional information on NoSQL databases.Continue Reading

• 16 top data governance tools to know about in 2024

  Data governance software can help organizations manage governance programs. Here's a look at the key features and capabilities of 16 prominent governance tools.Continue Reading

• Unlock the value of data through AI, modern data platforms

  Organizations looking to harness the power of generative AI to unlock data value should combine it with the modern data platform to maximize performance and efficiency.Continue Reading

• Companies still grappling with building modern data platforms

  Data platforms are crucial for managing and delivering data efficiently, but a recent survey from Enterprise Strategy Group showed that companies still struggle to implement them.Continue Reading

• Why noninvasive data governance is the best approach to use

  Organizations typically approach data governance with top-down or traditional approaches. Consultant Robert Seiner discusses what makes his noninvasive approach the best option.Continue Reading

• Explore the benefits of AI for DataOps

  Enterprise Strategy Group research shows most organizations feel they need AI to unlock the full potential of DataOps and improve data pipeline performance.Continue Reading

• Organizations identify DataOps as driver to improve data use

  An overwhelming majority of organizations report plans to increase DataOps spending. Continued investments target real-time, quality data-driven decision-making and insights.Continue Reading

• GenAI, Vector AI Search, multi-cloud top Oracle CloudWorld

  Oracle CEO paints a bright picture of the future with generative AI, shares integration plans and expands Microsoft partnership.Continue Reading

• Security a top challenge in building a modern data platform

  Modern data platforms help manage large volumes of data and empower real-time decision-making. It starts with overcoming the top challenges organizations identify.Continue Reading

• Data quality fuels analytics, AI

  Data quality is tied to organizations' ability to gain actionable data from analytics and AI processes, and orgs that implement data quality tools can make faster decisions.Continue Reading

• Cloud data warehouse book challenges data assumptions

  Dr. Barry Devlin's book lays out the principles of cloud data warehousing and challenges data managers to evaluate what they need when choosing a data warehouse approach.Continue Reading

• Evaluate cloud data warehouses based on data, outcomes

  Organizations must focus on data and desired outcomes -- and question their assumptions -- when evaluating cloud data warehouse needs, according to industry expert Dr. Barry Devlin.Continue Reading

• 6 ways Amazon Security Lake could boost security analytics

  Amazon's new security-focused data lake holds promise -- including possibly changing the economics around secure data storage.Continue Reading

• Data stack benefits evolve with modernization

  Modernizing data operations changes the way organizations use data stacks. Industry experts share definitions for the new form of data stacks, what it includes and its benefits.Continue Reading

• Empower decision-making with real-time insights

  Using the right strategies is a crucial key to unlocking the benefits of real-time analytics and empowering organizations with agile data-driven decision-making.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 vs. other data management options

  Data mesh takes a decentralized approach to data management and deriving value from data. It shares similarities with data warehouses, lakes and fabrics, but differs in philosophy.Continue Reading

• Informatica World 2023: Cloud, data and AI together

  Informatica launched the generative AI Claire GPT product and plans to offer Intelligent Data Management Cloud as a Microsoft Azure Native ISV Service.Continue Reading

• Modernizing a data warehouse for real-time decisions

  Updating a data warehouse to improve scalability, flexibility, security and speed is necessary to keep pace with real-time analytics demands.Continue Reading

• Benefits of data mesh might not be worth the cost

  Data mesh can improve an organization's data quality and insights, but significant challenges can make these benefits difficult to achieve.Continue Reading

• Book excerpt: Data mesh increases data access and value

  Zhamak Dehghani, a pioneer in data mesh technology, discusses how the concept decentralizes data to improve data-related decision-making and value in her book.Continue Reading

• What's all this talk about data mesh?

  Data mesh brings a variety of benefits to data management, but it also presents challenges if organizations don't have the right culture and infrastructure in place.Continue Reading

• ESG predicts 2023 shifts for DataOps, data management

  Organizations are using cloud technologies and DataOps to access real-time data insights and decision-making in 2023, according to Enterprise Strategy Group research.Continue Reading

• Data lake vs. data warehouse: Key differences explained

  Data lakes and data warehouses are both commonly used in enterprises. Here are the main differences between them to help you decide which is best for your data needs.Continue Reading

• Data-centric developer responsibilities evolve in 2022

  Enterprise Strategy Group Analyst Stephen Catanzano discusses how data-centric developer responsibilities are evolving as technological advancements enable more data use.Continue Reading

• Organizations capitalize on intelligent data management

  Intelligent data management concepts are opening new avenues for organizations to make better data-centric decisions and extract more value from their data.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

• Comparing DBMS vs. RDBMS: Key differences

  A relational database management system is the most popular type of DBMS for business uses. Find out how RDBMS software differs from other DBMS technologies.Continue Reading

• The differences between a data warehouse vs. data mart

  Data marts and data warehouses both play key roles in the BI and analytics process. Here's how they differ and how they can be used to help drive business decisions.Continue Reading

• AWS DataZone headlines AWS re:Invent 2022

  AWS DataZone will enable the sharing, search and discovery of data at scale with less risk. It is one of many data advancements announced at AWS re:Invent 2022.Continue Reading

• Evaluating data warehouse deployment options and use cases

  There's still a place for data warehouses in data architectures. But first, ask whether your organization needs one and what type of technology platform is the best fit.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

• Big Data London focuses on future of data-driven strategies

  At Big Data London, data quality and intelligence took center stage as companies strive for fast and efficient delivery of quality information -- and the vendors to make it happen.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

• 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

• Best practices and pitfalls of the data pipeline process

  Developing an effective data pipeline process is a key step for organizations to manage data sources, flow and quality. A data pipeline also ensures approved data access.Continue Reading

• New approaches create opportunity to turn data into value

  Bill Schmarzo, a data science industry thought leader, discusses how organizations can reframe their view of data using economic concepts to turn data into value.Continue Reading

• Improve data value by relying on economic principles

  Bill Schmarzo, author of 'The Economics of Data, Analytics, and Digital Transformation,' discusses how organizations can improve data value by incorporating economic concepts.Continue Reading

• The ultimate guide to big data for businesses

  Big data is the fuel for today's analytics applications. This in-depth big data guide explains how businesses can benefit from it and what they need to do to use it effectively.Continue Reading

• Hadoop vs. Spark: An in-depth big data framework comparison

  Hadoop and Spark are widely used big data frameworks. Here's a look at their features and capabilities and the key differences between the two technologies.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

• 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

• Bill Inmon's data warehouse approach tackles text analysis

  Learn the fine points of a concept at the heart of 'The Textual Warehouse' a new book that aims to help organizations profit through textual analysis.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

• The pros and cons of big data outsourcing

  More companies are seeking outside help to capitalize on data's value. Examine the benefits and drawbacks that come with outsourcing big data processing projects.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

• The value of PDF data extraction: Sifting for hidden data

  During the process of data cleaning, there's a way to extract valuable hidden data. Learn how in this excerpt from 'Cleaning Data for Effective Data Science.'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

• What an automated data integration implementation means

  Automated data integration can reduce time spent by data professionals on repetitive tasks. Learn about strategies to help implement automated data integration.Continue Reading

• Data governance and your master data management strategy

  Strong data governance and master data management strategies typically go hand in hand. Read on to see how key factors of data governance can support your master data management.Continue Reading

• How data governance and data quality work together

  High-quality, reliable data is essential to the data governance process. Here are strategies to ensure data quality standards are ingrained in governance processes.Continue Reading

• How to build an all-purpose big data pipeline architecture

  Like a superhighway system and its many on- and off-ramps, an enterprise's big data pipeline transports infinite amounts of collected data from its sources to its destinations.Continue Reading

• Data quality for big data: Why it's a must and how to improve it

  As data volumes increase exponentially, methods to improve and ensure big data quality are critical in making accurate, effective and trusted business decisions.Continue Reading

• Enterprise augmented data management benefits and growth

  Gartner predicts plenty of growth in the booming augmented data management market, which helps data professionals focus on insights over administrative tasks.Continue Reading

• Why consider an augmented data catalog?

  Automated and augmented data catalogs have been around for a few years, but adoption is still lagging. Find out why an enterprise may consider investing in the technology.Continue Reading

• Why consider an open source data catalog

  Enterprise data catalogs offer organizations plenty of benefits with metadata management and data organization. Find out why some enterprises choose open source data catalogs.Continue Reading

• Top open source database advantages for enterprises

  Open source databases typically offer lower upfront costs and more community support and, in recent years, have offered strong competition to commercial database offerings.Continue Reading

• Bias in big data: How to find it and mitigate influence

  It's no secret that bias exists in large data sets, ; the key is addressing it. With transparency, diversity and accountability, limiting that bias can be possible.Continue Reading

• The top 5 graph database advantages for enterprises

  Graph databases offer plenty of advantages to organizations in the way they connect data points to each other. Read on to see what experts say the top advantages are.Continue Reading

• Data catalog comparison to help you choose your best fit

  Data catalog options vary across vendors, but, as with most decisions in the data realm, it takes self-knowledge to make the right choice and understand each option's capabilities.Continue Reading

• Data fabrics help data lakes seek the truth

  Data fabrics can play a key role in aligning business goals with the integration, governance, reliability and democratization of information collected in massive data lakes.Continue Reading

• Augmented data preparation the next step for self-service BI

  Augmented data tools play a key role in expanding data use across organizations. Read on to find out how augmented data preparation tools democratize data in self-service BI.Continue Reading

• Open source database comparison to choose the right tool

  These are four of the most popular open source relational databases available to enterprises with a comparison chart to help you find the best option to fit your data.Continue Reading

• What FAIR data management means for your enterprise

  The FAIR principles were made to promote the sharing of data in the research field, but their guidance can help organizations in other industries improve their own data practices.Continue Reading

• New data warehouse schema design benefits business users

  The Unified Star Schema is a revolution in data warehouse schema design. Learn the benefits of this new architecture and read an excerpt from a new book about it.Continue Reading

• Data warehouse vs. data lake: Key differences

  Data warehouses and data lakes are both data repositories common in the enterprise, but what are the main differences between the two and which is best for your data?Continue Reading

• Apache Pulsar vs. Kafka and other data processing technologies

  David Kjerrumgaard looks at how the distributed messaging platform Apache Pulsar handles storage compared to Apache Kafka and other data processing technologies.Continue Reading

• The top 6 use cases for a data fabric architecture

  Enterprise data fabric adoption has been on the rise as a way to ensure access and data sharing in a distributed environment. Here are the top use cases for data fabrics.Continue Reading

• Healthcare data management challenges hold back adoption

  The healthcare industry has had difficulty adopting data management best practices, but a few organizations have tackled the challenges successfully.Continue Reading

• How AI data privacy can help your enterprise

  Enterprises benefit in many ways from AI data privacy tools that reduce the need for manual efforts from data professionals. Read on for top use cases for the growing technology.Continue Reading

• NewSQL databases: The bridge between SQL and NoSQL

  NewSQL databases attempt to provide enterprises with the top benefits of both relational and NoSQL databases on one platform. Here's a look at how NewSQL can benefit enterprises.Continue Reading

• Synthetic data in healthcare advances patient analytics

  Learn how synthetic data in healthcare can be beneficial across the board. Plus, get a sneak peek at a new book on synthetic data generation from O'Reilly Media.Continue Reading

• 3 growing applications of AI in data management

  There are plenty of ways AI can augment data professionals throughout the data pipeline, from sifting through large data sets for duplicates to easing the preparation process.Continue Reading

• Why you should consider a machine learning data catalog

  A machine learning data catalog can benefit an enterprise in a variety of ways, from increasing access to necessary data to keeping your data sources up to date.Continue Reading

• How to streamline your data cleansing process

  Data cleansing is an important part of maintaining data quality, and the process is easier if you keep ahead of it by upholding governance and quality standards.Continue Reading

• Top 7 data catalog use cases for enterprises

  From data lake modernization to data democratization, there are many benefits to a data catalog. Experts talk about the top ways data catalog adoption can benefit enterprises.Continue Reading

• Metamorphosis of Kafka Confluent event streaming technologies

  Kafka Confluent event streaming tools allow data managers, analysts and developers to capture, process, store, access and analyze data streams on the fly with precision.Continue Reading

• Key to data management in oil and gas is stability

  Ensuring quality data management and analytics comes from following best practices, proper commitment from your organization and sticking with the plan.Continue Reading

• How to choose the right database to fit your data model(s)

  Choosing the right database for your enterprise applications can be a difficult process. Read on for some considerations on data platforms and model needs before making the choice.Continue Reading