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Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence
Data: The Importance of Data Quality in Business Intelligence Originally Published - October 20, 2008 The zeal to get as much business data to the user as soon as possible often prevails over the establishment of processes that control the quality of data. Low data quality standards can lead to bad business decisions and missed opportunities. Even with a data warehouse that is well designed and equipped with the best tools for business intelligence (BI), users will encounter inefficiency and frustration
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Product Data Management (PDM), Engineering Change Order and Technology Transfer, Design Collaboration, Process and Project Management, Product Technology
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Document Management System (DMS)
Document management systems (DMS) assist with the management, creation, workflow, and storage of documents within different departments. A DMS stores documents in a database and associates importan...
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Documents related to
Data Management for Business Intelligence
Along with the increasing sophistication of BI capabilities comes an increase in data volume@yet companies want to improve time-to-information for users. Data
comes an increase in data volume—yet companies want to improve time-to-information for users. Data storage and data retrieval decisions are now often made based on cost instead of business needs—leading to compromises that impact performance. Find out the BI strategies, capabilities, and technologies best-in-class companies are using to address these challenges.
Captured by Data
The benefits case for enterprise asset management (EAM) has been used to justify huge sums in EAM investment. But to understand this reasoning, it is necessary
by Data Enterprise Asset Management Systems and the Aims of Modern Maintenance Since the late 1980s, enterprise asset management (EAM) vendors throughout the world have pitched their products based partly on the ability to capture, manipulate, and analyze historical failure data. Part of the stated benefits case is often the ability to highlight the causes for poor performing assets, provide the volume and quality of information for determining how best to manage the assets, and informing
Big data is currently garnering a tremendous amount of attention, and thought leaders often point to three key attributes: volume, velocity, and variety. The
Three V s of Big Data Big data is currently garnering a tremendous amount of attention, and thought leaders often point to three key attributes: volume, velocity, and variety. The analysis of these three key attributes can be extended into understanding what big data means to the financial planning and analysis function. This article identifies the technology developments converging to create the big data rush and examines how the financial planning function is impacted by volume, velocity, and variety
Data Conversion in an ERP Environment
Converting data in any systems implementation is a high wire act. Converting data in an ERP environment should only be undertaken with a safety net, namely a
means for converting existing data structures, namely: Programming Effort Volume of Data and Frequency of Maintenance. Availability of Resources However, before getting into the discussion of these factors, a word of caution is appropriate. Despite my personal dislike for the overused phrase, Garbage in; garbage out, I cannot think of any better way of describing what could happen if your legacy data is corrupted or unreliable. Take the time to cleanse and prune your data before you convert it. This
Data Masking: Strengthening Data Privacy and Security
Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating
Masking: Strengthening Data Privacy and Security Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.
Data Center Projects: Advantages of Using a Reference Design
It is no longer practical or cost-effective to completely engineer all aspects of a unique data center. Re-use of proven, documented subsystems or complete
aspects of a unique data center. Re-use of proven, documented subsystems or complete designs is a best practice for both new data centers and for upgrades to existing data centers. Adopting a well-conceived reference design can have a positive impact on both the project itself, as well as on the operation of the data center over its lifetime. Reference designs simplify and shorten the planning and implementation process and reduce downtime risks once up and running. In this paper reference designs are
New Data Protection Strategies
One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased
Data Protection Strategies One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets.
Data Migration Best Practices
Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce
Migration Best Practices Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce the risks and costs associated with these projects. This paper offers a handy checklist of issues to consider before, during, and after migration.
Data Quality: A Survival Guide for Marketing
Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge
to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.
Data Management and Analysis
From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it
perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As). For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users to data professionals—must have the most
Data Security Is Less Expensive than Your Next Liability Lawsuit: Best Practices in Application Data Security
Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all
Best Practices in Application Data Security Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all costs. However, this nightmare is preventable: knowledge base-driven data security solutions can be critical tools for enterprises wanting to secure not only their data—but also their status in the marketplace.
Data Quality: Cost or Profit?
Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and
number of articles about data quality. ( Poor Data Quality Means A Waste of Money ; The Hidden Role of Data Quality in E-Commerce Success ; and, Continuous Data Quality Management: The Cornerstone of Zero-Latency Business Analytics .) This time our focus takes us to the specific domain of data quality within the customer relationship management (CRM) arena and how applications such as Interaction from Interface Software can help reduce the negative impact that poor data quality has on a CRM objective.
Data Quality Basics
Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at
Quality Basics Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue.
Data Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics
Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident
and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident decisions and take the right actions, you need more than just software—you also need the right data. This white paper discusses a solution to help ensure that your applications process reliable data so that your spend analyses and supplier risk assessments are
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