Problem solve
Get help with specific problems with your technologies, process and projects.
Problem solve
Get help with specific problems with your technologies, process and projects.
6 data privacy challenges and how to fix them
Fragmented data protection laws, technology disruptions, AI adoption, data governance and consumer trust are among the complex issues confronting businesses in need of remedies. Continue Reading
Data management best practices key to generative AI success
Generative AI can create data, empower decision-makers and innovate competitive advantages. Organizations need strong data management practices to use it effectively. Continue Reading
Grow data trust to avoid customer and corporate consequences
A lack of data trust can undermine customer loyalty and corporate success. To avoid the consequences, understand the effects of poor data trust and learn key steps to build it. Continue Reading
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Successful data analytics starts with the discovery process
As organizational data grows more complex, discovery processes help organizations identify patterns to solve potential issues and improve business processes. Continue Reading
Avoid data sprawl with a single source of truth
Enterprise Strategy Group research shows organizations are struggling with real-time data insights. A single source of truth can make operations more efficient and more accurate. Continue Reading
8 data integration challenges and how to overcome them
These eight challenges complicate efforts to integrate data for operational and analytics uses. Here's why, plus advice on how to deal with them.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
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
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
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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
The modern data platform drives data-centric organizations
IT leads the modern data platform, functioning as the conduit, efficiently delivering the correct data to the right users, to empower data-driven decision-making in organizations.Continue Reading
Data lake governance: Benefits, challenges and getting started
A data lake that isn't well governed may become more of a swamp. Here are key benefits and challenges of data governance in a data lake, plus initial steps to take.Continue Reading
8 proactive steps to improve data quality
Here are eight steps to take to improve your organization's data quality in a proactive way, before data errors and other issues cause business problems.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
hashing
Hashing is the process of transforming any given key or a string of characters into another value.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
Data governance and COVID-19 data security challenges
Maintaining data governance and data security best practices is essential now more than ever. But the increase in working from home can put a strain on those practices.Continue Reading
Common data lake challenges and how to overcome them
Managing the data contained in your enterprise data lake presents many challenges. From the amount of data to data inconsistencies, here are some solutions to common issues.Continue Reading
Breaking down data silos with strong data governance
Not having a single source of truth can be a huge issue for data professionals. But strong data governance policies can prevent data silos and lead to better-quality data.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
11 real-time data streaming roadblocks and how to overcome them
Experts detail common challenges that IT teams encounter when deploying and managing real-time data streaming platforms and offer advice on how to address them.Continue Reading
Using a LEFT OUTER JOIN vs. RIGHT OUTER JOIN in SQL
In this book excerpt, you'll learn LEFT OUTER JOIN vs. RIGHT OUTER JOIN techniques and find various examples for creating SQL queries that incorporate OUTER JOINs.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
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
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
What's the difference between DDL and DML?
What's the difference between DDL and DML? Get the answer and see examples of data manipulation language and data definition language commands for SQL databases.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
Kubernetes container orchestration gets big data star turn
The new thing in big data is Kubernetes container orchestration. While it's still early, there are signs of activity, which are cited in this edition of the Talking Data podcast.Continue Reading
Dimension tables vs. fact tables: What's the difference?
Fact tables and dimension tables are used together in star schemas to support data analytics applications. But they play different roles and hold different types of data.Continue Reading
Operational data store vs. data warehouse: How do they differ?
Operational data stores and data warehouses both store operational data, but the similarities between them end there -- and they both have a role to play in analytics architectures.Continue Reading
What are key features for choosing the best ETL tools for your needs?
Choosing the right ETL tool for your data integration requirements can be a challenge. Here's a rundown on what to look for in ETL software and potential vendors to consider.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
Big data, fast: Avoiding Hadoop performance bottlenecks
A variety of performance issues can bog down Hadoop clusters. But there are ways to sidestep the pitfalls and keep your big data environment humming.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
Gartner describes the building blocks for a strong MDM program
MDM teams should consider these seven building blocks, outlined by Saul Judah, research director on the information management team at Gartner Inc.Continue Reading
Key issues to consider when building a data warehouse
There are numerous issues, both technical and cultural, that organizations need to consider before building a data warehouse. Learn what they are from our data warehousing expert.Continue Reading
A must to avoid: Worst practices in enterprise data governance
Consultant Rick Sherman details 10 common data governance mistakes that can send governance initiatives down the wrong path – the one to failure.Continue Reading
Six tips for improving data warehouse performance
Get six expert tips for improving data warehouse performance. Learn how database engines, SSDs and MOLAP cubes can affect your data warehouse performance.Continue Reading
How to get senior execs to buy into a data governance plan
Learn how to get senior management to buy into data governance. Get tips on selling data governance policies and processes to executives who can approve data governance programs.Continue Reading
What’s the best way to conduct an MDM implementation?
Is it better for companies to go with an enterprise-wide master data management (MDM) implementation or deploy MDM departmentally? Find out which approach our MDM expert prefers.Continue Reading
What is the difference between data definition language (DDL) and SQL?
Learn the difference between a data definition language (DDL), also known as data descriptive language, and the Structured Query Language (SQL). Find examples of both DDL and SQL.Continue Reading
Steps for converting a logical data model into a physical data model
Learn how to convert a logical data model into a physical data model via data modeling best practices. Find out how DDL and your data modeling practices play a role in conversions.Continue Reading
Data modeling tools: Best practices for selection and evaluation
Get best-practices advice and tips for choosing and evaluating data modeling tools. Also, learn how to define data modeling requirements and use your data governance council.Continue Reading
What are aggregate tables and aggregate fact tables?
Here you'll see examples and read definitions of aggregate tables and aggregate fact tables. You'll also learn how to use aggregate tables in data warehouses and databases.Continue Reading
Data warehouse concepts: Data flow and sending data to source systems
Learn about a data warehouse concept: data flow. And find out if it's a good idea to flow data from your data warehouse or data marts back to source systems.Continue Reading
What is the difference between a logical and physical warehouse design?
What's the difference between logical design and physical design? Get an expert's take, plus learn about three data warehouse models -- the user model, physical model and logical model -- and how they differ.Continue Reading
Six criteria for master data management (MDM) tool evaluation
Starting to evaluate MDM tools and software? Know what six areas you should look at to make sure you choose the best vendor and tool for your business needs.Continue Reading
Is it better to have a centralized or decentralized master data structure?
Should your master data be centralized or decentralized? Get an expert's take, plus find out how different MDM architecture styles affect your master data and MDM hub.Continue Reading
How to select an MPP database: DB2 vs. Teradata
Should you run a massively parallel processing (MPP) database with DB2 or Teradata? Find out how to compare DB2 vs. Teradata and how to choose a database system and with advice from a data warehouse expert.Continue Reading
Four must-have master data management project skills
There are four important master data management project skills needed for MDM projects and development efforts. Discover what they are and learn about MDM job roles and responsibilities in this tip.Continue Reading
Defining dimensional vs. normalized database design, dimension vs. fact tables
Find out if your existing database is in dimensional style or normalized database design style, learn the benefits and drawbacks of fact-dimension and normalized database schemas and read a comparison of dimension tables vs. fact tables.Continue Reading
What are the most common system implementation mistakes?
Learn about some of the biggest mistakes in system implementation and why successful implementations are few and far between. Find out how to avoid these pitfalls with careful planning and more tips from our project management expert.Continue Reading
What are the advantages/disadvantages of database abstraction layers?
Discover the advantages and disadvantages of using database abstraction layers, as opposed to database-specific coding, and learn why abstraction layers offer maximum flexibility.Continue Reading
What are the components of service-oriented architecture (SOA)?
What are the components that make up service-oriented SOA? Find out in this expert response.Continue Reading
What's the difference between SOA and Web services?
How are Web services and service oriented architecture (SOA) different? Find out in this expert response.Continue Reading
Definition of data abstraction and data abstraction layers
Data abstraction simplifies database design. Learn the definition of data abstraction and find out the three formal kinds of abstraction layers, from a database expert.Continue Reading
How to evaluate data warehouse software in five steps
Get five steps to evaluating data warehouse software from a data warehouse expert.Continue Reading
Enterprise versus project level conceptual data modeling
Learn the differences between enterprise level and project level conceptual data modeling, and find out how both approaches are compatible.Continue Reading
Data model conversion: Conceptual design to logical design using an ER model
Learn two common approaches to converting a data model from a conceptual design to a logical design.Continue Reading
Can a dimension table be a fact table for another data mart?
Find out if a dimension table can be a fact table for another data mart -- and get an explanation of the answer.Continue Reading
The ETL process and MySQL
Get advice on the ETL process and ETL tools for transforming raw data in MySQL.Continue Reading
What are the benefits of a conceptual data model?
Learn the functions and potential benefits of a conceptual data model, from data modeling expert Pete Stiglich.Continue Reading
Ralph Kimball vs. Bill Inmon approaches to data warehouse design
Learn about the debate between the Ralph Kimball and Bill Inmon approaches to data warehouse design.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
VSAM vs. DB2
The path to an executable DB2 program
POS vs. EDI
How to limit the number of records in a cursor using DB2 native SQL?
How can I find the DB2 version and release level info in my CICS program?
Multiplying fields from different tables
Difference between BI in the public sector and private sector
Find out the major difference between business intelligence (BI) in the public sector and BI in the private sector and get an expert's take on trends for those using BI in the public sector.Continue Reading
Facts about Packet Sniffing and Spoofing