#trillium software

[garymdm]

Poor data quality significantly hinders banks ability to present accurate regulatory reports

Poor #dataquality significantly hinders bank's ability to present accurate regulatory reports

Recent research conducted by Aite Group highlighted the link between poor data quality and regulatory penalties, further creating a case for financial services organisations to improUnderstanding Data Quality Dimension: Consistencyve their data quality. Poor data quality can lead to an adverse conclusion by regulators about a bank’s ability to accurately monitor business risks. Focus on data…

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[garymdm]

Big data quality

Big data quality for Hadoop #DataQuality #BigData

Ventana’s Research recent Big Data Integration benchmark survey supports the growing awareness that data quality and integration are the principal time sinks for big data projects. Their research finds that more than 50% of the time allocated to any big data project is taken in reviewing the data for quality and consistency – not surprising given the largely manual / code-driven approaches that…

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[garymdm]

Avoiding FATF suspension depends on data.

Avoiding FATF suspension depends on data.

South Africa is the only permanent, African member of FATF, the global anti-money laundering watchdog. This is why it came as a shock to hear that the country is close to being greylisted following concerns raised by the global body. Bascially, FATF membership helps to streamline the movement of money across borders by guaranteeing that the parties involved in a transaction are above board. In…

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[garymdm]

Integrating data quality and data governance

Integrating data quality and data governance

For someone that’s been preaching data governance and data quality for more than fifteen years, its been fascinating to see how these two topics have been gaining traction in the last few years.

A few week’s back I touched on the difference between data governance and data quality – governance is about “what” and “who”, data quality is about “how.”

understanding the relationship

This…

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[garymdm]

The 6 dimensions of big data quality

The 6 dimensions of big data quality

Historically, data quality has been measured in terms of dimensions including:

Accuracy – the degree to which data reflects the real world

Completeness – data that is adequately populated

Timeliness – That data is available when expected and needed

Consistency – that data across all systems reflects the same reality

Conformity – that data has consistent standards

Integrity – That relationships…

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[garymdm]

Quick recap of the Syncsort Summer School

Quick recap of the Syncsort Summer School

Over the US summer our partner, Syncsort, published a number of posts reviewing data governance, data quality and big data that made good reading, both for the beginner and for the expert looking to keep up with emerging trends.

I’ll start the ball rolling by linking you to Syncsort;s collection of top 50 big data blogs posts 

if you prefer to listen you may want to review 3Webcasts for Smarter…

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[garymdm]

What is the value wedge for data governance and data quality?

What is the value wedge for data governance and data quality?

A recent post by our partner, Syncsort Trillium Software, introduces the concept of the data wedge.

According to the post, which can be read here, the Data Wedge is the relationship between what data IS, what data DOES and what data MEANS.

The post was inspired by a recent Trillium Software webinar on Gaining Clarity and Confidence in Your Data. Customers were polled to identify which type of…

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[garymdm]

Data profiling is not data quality

Data profiling is not data quality

For years, I have been a proponent of data quality measurement.

Data quality cannot exist without management (and some would argue without governance).

Meaningful data quality metrics play a critical role in managing data quality.

Posts such as Don’t blow up the whale;   Changing data behaviour through KPIs, and  Accuracy, Completeness and Speed of Execution have different takes on this theme.

In…

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