Data quality techniques and best practices

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  • NoSQL technologies take on rising tide of big data

    For decades, IT managers, developers and business executives had limited options when shopping for database technologies: Relational databases were universally built on top of the SQL programming language. It was SQL or nothing.

    Now, th... 

  • Tap into enterprise data governance for business value

    Data governance is all about managing data as a business asset and ensuring that data is accurate, consistent, integrated, timely, complete and secure. This report examines the market trends for implementing data governance. Based on data from a rece... 

  • Strategies for kick-starting your data quality program

    In this handbook, readers will find quality advice on balancing manual and automated data quality processes as part of quality improvement efforts, as well as tips on building a business case for purchases of data quality tools. Readers can look forw... 

  • Get a handle on BI data quality management -- before it bites you

    Ensuring that business intelligence data is clean, accurate and consistent is crucial to the success of BI efforts; analyzing bad data will send BI users off track -- quickly. And as data volumes grow and the number of data sources that organizations... 

  • 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... 

  • 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... 

  • 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... 

  • 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... 

  • data-driven decision management (DDDM)

    Data-driven decision management (DDDM) is an approach to business governance that values decisions that can be backed up with data that can be verified. The success of a data-driven approach is reliant upon the quality of the data gathered and the ef... 

  • data-driven disaster

    A data-driven disaster is a serious problem caused by one or more ineffective data analysis processes. 

  • 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. 

  • 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. 

  • 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... 

  • 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) 

  • 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... 

  • 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. 

  • 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. 

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.