Data Management Strategies for the CIOEmail Alerts
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BI Trends + Strategies Issue 11
The lead article in this issue of SearchBusinessAnalytics.com’s BI Trends + Strategies will examine the potential benefits and challenges of data visualization deployments, with insight and advice from consultants and experienced users. Data v... E-Zine
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Optimizing data quality efforts in everyday processes
Poor data quality at the most minor level can have major ramifications. Read this expert e-book to gain advice on how to make data quality a high priority in your day-to-day business operations. Discover tips on implementing internal data quality sta... E-Book
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How to get funding for data quality programs
Read this exclusive e-book to find tips on how to build a case and gain approval for implementing a program to increase your data quality. Also find information on how to best assess and manage your data, as well as evaluating whether or not data qua... E-Book
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Tactical data quality: How to improve data quality with a tight budget
In this e-book, you'll learn how to manage data quality efforts during an economic downturn and find out what trends are emerging in this market. You’ll also learn about common mistakes, the financial costs of poor data quality and how tools and stra... E-Book
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Microsoft SQL Server data profiling tool put to work
Andy Hogg demonstrates how to clean up dirty data with the data profiling tool that comes with Microsoft SQL Server Tip
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Hindsight: The case of the faulty data quality process
The discovery of discrepancies in a telecom company's data led to a realization that more robust procedures for ensuring data quality were needed. Feature
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How clean is your data?
Within a transactional system, data rarely appears to be anything other than squeaky clean. Find out why data is dirty, and how to see the dirt Tip
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Data quality essential to master data management
It is possible to have a data quality initiative without master data management, but every MDM project must have a data quality element Column
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Successful data stewardship framework needs solid plan, firm focus
Stewarding data can be a tough nut to crack: lots of effort for a reward that isn't always apparent. To succeed, strong project management is needed. Feature
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Internal data quality standards not just a game for business users
Ensuring that business users follow corporate standards when entering and updating data is vital to the success of data quality improvement efforts. News Analysis
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Data quality training sets stage for business-user involvement
Organizations looking to include end users in data quality improvement efforts need to invest up front in developing effective training programs. News Analysis
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IT fix-it approach not enough for effective data quality strategy
Relying on data warehousing and integration teams to clean up data errors is a common practice. But preventing data quality problems is better than trying to cure systems of them later on. From the Editors
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Top-to-bottom data quality plan starts with selling execs on need
To win approval for involving business users in data quality efforts, IT managers should draw a connection between accurate data and the bottom line. News Analysis
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Data quality process needs all hands on deck
Data accuracy is critical in technology-driven business processes, making it a must to involve business users in data quality efforts, analysts say. News Analysis
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British Army data quality programme tackles ‘whole army’ concept
The British Army’s data quality programme faces a challenge with moves to unify regular and territorial soldiers into one force Case study | 01 May 2013
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West Midlands Police develops golden view of criminals, suspects
West Midlands Police, the second largest force in the UK, seeks to reduce risk to its officers and the public by using DataFlux software that gives a master record for criminals and suspects Case study | 01 Nov 2012
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New Gartner Magic Quadrant finds demand rising for data quality tools
Organizations are using data quality tools to support a growing range of use cases, according to a new Gartner Magic Quadrant report. News | 06 Sep 2012
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Talend: Guerrilla marketing can add momentum to data management plans
Talend product marketing exec and book author Steve Sarsfield offers tips on how to build a convincing business case for data management projects. News | 10 Jul 2012
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Data errors, other missteps can waylay BI data integration strategy
A business intelligence data integration initiative can easily go off track because of problems such as data quality and data loading issues. News | 06 Jun 2012
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Data management strategies on full display at Enterprise Data World
May brings a busy season for data management conferences to a close. The last stop: Enterprise Data World, which offered advice and some laughs -- the latter at the expense of "big data." From the Editors | 31 May 2012
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People, process issues offer path to improve data quality on the cheap
Educating end users and upgrading internal processes can help enterprises fix data quality problems without dropping a ton of cash, analysts say. News | 16 May 2012
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As data demands speed up, information management tools pave road ahead
New research shows data virtualization, data governance and master data management initiatives provide higher levels of satisfaction with data quality and data integration. News | 15 May 2012
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Intel offers up a 'silver bullet' master data management strategy
An IT manager at Intel Corp. sums up the chip maker's approach to managing master data in four words: Standardize, consolidate, optimize and utilize. News | 25 Apr 2012
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IBM users reveal five data governance best practices to remember
IBM customers Nationwide Insurance and Cardinal Health reveal five rules of thumb they learned while implementing highly ambitious data governance programs. News | 28 Mar 2012
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Five 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. Tip
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Time to tame data architecture complexity, but task is tough
Data architecture is close to a misnomer. Complex corporations are striated by applications, beset by politics. Service buses, data governance programmes offer remedies, but don't underestimate task. Analysis
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Data quality management tips and best practices
Get data quality management tips and best practices with expert advice and book excerpts. Learn about academic sources for data quality, key steps and common mistakes of data quality implementations, how much you should spend for data cleansing and m... Tip
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How to improve data quality on a tight budget -- a guide
Many organizations may be tempted to forgo data quality management to save money, but it's important to assess the ROI of high-quality data, according to consultant David Loshin. Tip
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Data governance success: No pain, no gain
Data governance isn't easy, but it is necessary for success in data-centric projects. Rick Sherman commiserates and offers expert advice for getting started with data governance. Tip
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Five steps for weaving data quality management into your enterprise data integration processes
Data quality management is critical for effective enterprise data integration (EDI). Expert Rick Sherman shares five strategic steps (and potential pitfalls) of data quality management. Tip
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Data quality management: Follow the doctor's orders
Get expert recommendations for data quality management in business intelligence and data warehousing projects. Tip
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Why data governance projects fail
Expert Rick Sherman discloses the top reasons why 90% of data governance projects are doomed for failure. Read his advice to make sure that your projects are not! Tip
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XQuery and XML data: DB2 helps manage the era of unstructured data
The arrival of IBM XQuery and XML data is simplifying users' decisions in this era of unstructured data, just as the arrival of the relational database and SQL simplified IT buyers' decisions twenty years ago. Tip
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Data manager best practices: Dear Santa, I've been a good data management manager
T'was the night before Christmas, when all through the database, guest columnist Rick Sherman was asking Santa to make the world of data management a better place... Tip
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The state of enterprise information management technology use in 2011
Get expert insight on whether there likely will be any strategic shifts during 2011 in how companies use enterprise information management technology. Answer
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Common enterprise information management strategy mistakes to avoid
Learn about four common mistakes that companies make when creating an enterprise information management strategy and starting an EIM program. Answer
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Implementing data quality software but ignoring governance policies
Is it OK to implement data quality software but ignore your data governance policies? Learn more about the relationship between data quality software and governance processes. Answer
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Open source data quality software to help with name matching
Learn what to consider before adopting open source data quality software to help with identity resolution and name matching. Answer
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How often should you update your data quality strategy?
Learn how often companies should update their data quality strategy. See how changes in data quality problems create new challenges and how revising your data quality strategy can help. Answer
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Data governance trends for 2011: Scorecards and data governance tools
Learn about emerging data governance trends for 2011, including more use of data governance tools and data quality metrics and scorecards. Find out what’s useful and what’s not. Answer
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Are the data quality issues worse in real-time data integration apps?
Find out if real-time data integration applications has more data quality issues than other approaches. Also, see if real-time or near-real-time integration are really useful. Answer
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How enterprise information management (EIM) differs from ECM
Learn how enterprise information management (EIM) differs from enterprise content management (ECM) and unstructured data management as a data management technology category. Answer
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Key components of enterprise information management programs
Learn which three components of enterprise information management programs are most important for a successful EIM strategy. Answer
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Key steps for developing an enterprise information management strategy
Here you’ll find 12 expert tips on developing an enterprise information management strategy and roadmap for your company. Use the EIM strategy development advice to get started. Answer
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data hygiene
Data hygiene is the collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, a... Definition
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data-driven decision management (DDDM)
Data-driven decision management (DDDM) is an approach to business governance that focuses on gathering data and analyzing it to guide corporate decisions and policies. The data-driven approach is gaining popularity within the enterprise as the amount... Definition
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data-driven disaster
A data-driven disaster is a serious problem caused by one or more ineffective data analysis processes. Definition
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data scrubbing (data cleansing)
Data scrubbing, also called data cleansing, is the process of cleaning up data in a database that is incorrect, incomplete, or duplicated. Definition
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raw data (source data or atomic data)
Raw data (sometimes called source data or atomic data) is data that has not been processed for meaningful use. Definition
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data governance (DG)
Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, a... Definition
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synthetic backup
Synthetic backup is the process of generating a file from a complete copy of a file created at some past time and one or more incremental copies created at later times... (Continued) Definition
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fixed data (permanent data, reference data, archival data, or fixed-content data)
Fixed data (sometimes referred to as permanent data) is data that is not, under normal circumstances, subject to change. Any type of historical record is fixed data. For example, meteorological details for a given location on a specific day in the p... Definition
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data quality
In computing, data quality is the reliability and application efficiency of data, particularly when kept in a data warehouse. Data quality assurance (DQA) is the process of verifying the reliability and efficiency of data. Definition
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data
In computing, data is information that has been translated into a form that is more convenient to move or process. In other contexts, data has somewhat different meanings. Definition
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On the road with master data management strategies and open data
TechTarget reporters share their views on Gartner's recent MDM Summit and an IBM Smarter Cities confab at Boston University in a fast-paced podcast. Podcast
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Top six data management software stories of 2011
From the rise of "big data" to office politics hampering MDM projects to Hewlett-Packard giving Neoview the ax, and more, there were plenty of notable data management stories in 2011. Photo Story
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Common mistakes that can derail a data governance strategy and program
Learn about the common mistakes companies make in creating, implementing and managing a data governance strategy and program. Video
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Tactical data quality projects deliver quick ROI
Tactical data quality projects can show quick ROI and have a definite impact on the bottom line. Get advice for deploying tactical data quality projects from Forrester's Rob Karel. Podcasts
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Uncovering the new data governance trends, with Ventana Research
Learn data governance trends from Ventana Research -- including early results from a new research study. Podcast
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Exec explains IBM's Information On Demand (IOD) initiative
Listen to IBM's Steve Mills discuss Big Blue's Information On Demand initiative and why he thinks it's in a better position to help customers than rival Oracle. Podcast
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Effective data quality program management: Tips and advice
Learn how to structure data quality management programs, how to deal with data quality program challenges and more, from expert Even Levy. Podcast
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Best practices for designing and implementing sustainable, long-term data quality programs
Learn best practices for designing and implementing data quality management programs. Podcast
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Creating successful data stewardship programs, with Jill Dyché
Learn how data stewardship fits into data governance and data quality management programs, from Jill Dyché. Podcast
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Data quality assessment helps identify, fix data quality problems
Data quality problems can't be fixed until they are identified. Learn how a data quality assessment can help pinpoint data quality problems. Podcast
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Five 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. Tip
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British Army data quality programme tackles ‘whole army’ concept
The British Army’s data quality programme faces a challenge with moves to unify regular and territorial soldiers into one force Case study
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data hygiene
Data hygiene is the collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, a... Definition
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Microsoft SQL Server data profiling tool put to work
Andy Hogg demonstrates how to clean up dirty data with the data profiling tool that comes with Microsoft SQL Server Tip
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On the road with master data management strategies and open data
TechTarget reporters share their views on Gartner's recent MDM Summit and an IBM Smarter Cities confab at Boston University in a fast-paced podcast. Podcast
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Hindsight: The case of the faulty data quality process
The discovery of discrepancies in a telecom company's data led to a realization that more robust procedures for ensuring data quality were needed. Feature
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data-driven decision management (DDDM)
Data-driven decision management (DDDM) is an approach to business governance that focuses on gathering data and analyzing it to guide corporate decisions and policies. The data-driven approach is gaining popularity within the enterprise as the amount... Definition
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data-driven disaster
A data-driven disaster is a serious problem caused by one or more ineffective data analysis processes. Definition
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How clean is your data?
Within a transactional system, data rarely appears to be anything other than squeaky clean. Find out why data is dirty, and how to see the dirt Tip
-
Data quality essential to master data management
It is possible to have a data quality initiative without master data management, but every MDM project must have a data quality element Column
- See more All on Data quality techniques and best practices
About Data quality techniques and best practices
Learn the latest data quality management and data governance best practices from analysts, experts and a wide range of case studies. Find data quality techniques and best practices including templates for organizations in all stages of planning -- whether an organization is updating its data quality management processes or just identifying data quality issues and learning the business benefits of data governance and data quality management. Find out how companies use data profiling, reports and scorecards to monitor and measure data quality, or find out what data quality management controls, standards and processes have helped other companies reach their objectives. Finally, be sure to browse many data quality management and data governance case studies with real-world insight across a variety of industries.