Trillium Software - Enterprise Data Quality

Trillium Software - Enterprise Data Quality White Papers

Building a Tangible ROI for Data Quality

This white paper will answer the questions “What data quality metrics should I be tracking?” and “Where do I find an ROI for my data quality efforts?”. While many soft benefits can be attributed to better data quality, organisations with mature data quality initiatives in place have quantified benefits that have been reflected on their organisations’ top and bottom line. Each industry, each project, each organization has different goals, business metrics, and considerations that may impact a resulting return on investment. By quantifying the impact of data quality processing, in a methodical way, you will be able to measure the impact of your efforts and the value you are providing as well as establishing a tangible return on investment.

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Data Intelligence and Governance

Increased transparency of the financial disclosure process requires that all underlying data meet emerging governance, risk and compliance (GRC) requirements. Banks must re-evaluate aspects of their lending strategies and dig into the real data within the various classes of risk. World financial markets are in turmoil. Financial institutions around the globe are struggling to survive the tsunamis of red ticker tape crashing over them. Market giants, such as Bear Stearns and Merrill Lynch, endured orchestrated buy-outs. And the 158-year-old venerable Wall Street icon, Lehman Brothers, failed. Who would ever imagine that Lehman Brothers, a company that endured the Civil War, survived the market crash of 1929 and the subsequent Great Depression, an institution once renowned for its rock-solid business practices and sage investment strategies, a giant of finance… went bankrupt. How could this happen?

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Data Quality Essentials: A Project Manager's Guide to Data Quality

This paper is aimed at project managers and describes the step-by-step process for implementing data quality as part of a project. While technology greatly facilitates and automates data quality management, it should be applied in accordance with a measurable, objective methodology to assure success and a high ROI for the project. Process, people, and business expertise are major components in achieving an improvement in data quality, leaving technology as a way to automate and improve processes.

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Methodology for Enterprise Data Quality and Data Governance

Trillium Software recommends tackling data quality issues that can be directly tied to tangible business benefits and growing this solution out, over time. Many businesses recognise that their data needs improvement, but fail to address the topic at an enterprise level because it is difficult to qualify the actual loss being experienced or the intended gain to be achieved. Because of the impracticality of implementing a corporate-sponsored, enterprise-wide data quality program using a big-bang approach.

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Solving Source Data Problems with Automated Data Profiling

Data profiling process is the best way to accurately plan projects and eliminate the risks associated with data quality problems. It also reduces costs and increases productivity as much as 90% over manual methodsMany project managers and systems integrators have learned know the importance of understanding the data before starting an integration or migration project. The data profiling process is the best way to accurately plan projects and eliminate the risks associated with data quality problems. It also reduces costs and increases productivity as much as 90% over manual methods. Data profiling can automatically identify potential exceptions and anomalies, rather than leaving project managers with having to write scripts based on what they suspect the anomalies to be. Data profiling uncovers such things as missing and duplicate data, misspelled data, broken data rules, invalid data structures, incorrect content, and irreconcilable data. Find out how Ford Financial Europe used a data analysis solution to enable them to able to identify within a few days, data issues across multiple data sources, that would have taken 180 man days of effort to overcome. They were able to improve their monthly reporting, thus increasing support for the many decision making processes across the enterprise.

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