The Data Roundtable

Data Management

Understand why managing your data is essential for analytics

Data Management

small dog looks at big dog representing growing importance of big data quality

Jim HarrisNovember 21, 2016 0

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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Data Management

Thanksgiving and how it relates to self-service data prep, Hadoop and more

Matthew MagneNovember 16, 2016 3

3 Thanksgiving lessons about data warehouses, Hadoop and self-service data prep

It's that time of year again where almost 50 million Americans travel home for Thanksgiving. We'll share a smorgasbord of turkey, stuffing and vegetables and discuss fun political topics, all to celebrate the ironic friendship between colonists and Native Americans. Being part Italian, my family augments the 20-pound turkey with pasta –

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Data Management

employee practicing self-service data preparation

Phil SimonNovember 14, 2016 0

Data preparation: Empowering business users via self-service

.@philsimon says don't treat data self-service as a binary.

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Data Management

David LoshinNovember 10, 2016 0

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 Management

man focusing on why analytical models are better with better data

Jim HarrisNovember 7, 2016 0

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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Data Management

contemplating security in a big data worls

Phil SimonNovember 1, 2016 0

What does security mean in the world of big data?

.@philsimon says that even seemingly useless information can be useful under the right circumstances.

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Data Management

Dylan JonesOctober 31, 2016 0

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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Data Management

David LoshinOctober 27, 2016 0

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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Data Management

Phil SimonOctober 25, 2016 0

The next data governance challenge: Agility

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

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Data Management

man considers how to measure the value of data governance

Jim HarrisOctober 20, 2016 0

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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Data Management