Is it wise to crowdsource data governance?

@philsimon says that it's downright silly to ignore the benefits of thinking about data-related issues in different and unexpected ways.

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How SAS supports the four pillars of a data quality initiative

Data quality initiatives challenge organizations because the discipline encompasses so many issues, approaches and tools. Across the board, there are four main activity areas – or pillars – that underlie any successful data quality initiative. Let’s look at what each pillar means, then consider the benefits SAS Data Management brings […]

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How can data privacy and protection help drive better analytics?

Balance. This is the challenge facing any organisation wishing to exploit their customer data in the digital age. On one side we have the potential for a massive explosion of customer data. We can collect real-time social media data, machine data, behavioural data and of course our traditional master and […]

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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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Data governance in action

Many people have the perception that data governance is all about policies and mandates, committees and paperwork, without any real "rubber on the road" impact. I want to dispel this viewpoint by sharing a simple example of how one company implemented data governance to enforce something practical that delivered long-term […]

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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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The next data governance challenge: Agility

.@philsimon says that data-governance professionals will need to be more agile than ever.






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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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What's the difference between data governance and data management? (Part 2)

In Part 1 of this series, we defined data governance as a framework – something an organization can implement in small pieces. Data management encompasses the disciplines included in the data governance framework. They include the following: Data quality and data profiling. Metadata (business, technical and operational). Data security. Data movement within the enterprise. […]

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