The growing importance of big data quality

Our world is now so awash in data that many organizations have an embarrassment of riches when it comes to available data to support operational, tactical and strategic activities of the enterprise. Such a data-rich environment is highly susceptible to poor-quality data. This is especially true when swimming in data lakes – […]

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The “tarnished record” – Alternatives to gold for fraud analytics

We often talk about full customer data visibility and the need for a “golden record” that provides a 360-degree view of the customer to enhance our customer-facing processes. The rationale is that by accumulating all the data about a customer (or, for that matter, any entity of interest) from multiple sources, you […]

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Why analytical models are better with better data

Most enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on […]

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Operational data governance: Policy vs. procedure for data validation

In my prior posts about operational data governance, I've suggested the need to embed data validation as an integral component of any data integration application. In my last post, we looked at an example of using a data quality audit report to ensure fidelity of the data integration processes for […]

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How do you measure the value of data governance?

Data governance plays an integral role in many enterprise information initiatives, such as data quality, master data management and analytics. It requires coordinating a complex combination of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data stewardship and change management. With so much overhead involved in […]

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Operational data governance: Who owns data quality problems?

Data integration teams often find themselves in the middle of discussions where the quality of their data outputs are called into question. Without proper governance procedures in place, though, it's hard to address these accusations in a reasonable way. Here's why. tags: data governance, data integration, data quality, operational data […]

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How do you define data governance?

Data governance has been the topic of many of the recent posts here on the Data Roundtable. And rightfully so, since data governance plays such an integral role in the success of many enterprise information initiatives – such as data quality, master data management and analytics. These posts can help you prepare for discussing […]

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Why an agile mind-set will lead to better data prep and analytics

.@philsimon on the need to adopt agile methodologies for data prep and analytics.






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Is data quality a component of data preparation? Or vice versa?

Critical business applications depend on the enterprise creating and maintaining high-quality data. So, whenever new data is received – especially from a new source – it’s great when that source can provide data without defects or other data quality issues. The recent rise in self-service data preparation options has definitely improved the quality of […]

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