The Data Roundtable

Data Management

Understand why managing your data is essential for analytics

Data Management

santa uses data quality to wrap up christmas

Matthew MagneDecember 16, 2016 0

How Santa uses data quality to wrap up Christmas

"Two weeks to go," Santa said to himself, with millions of toys stacked up on the shelves. Each year worry hit at the same time – "How do I get the right toy to the right child without losing my mind?" Though Old St. Nick didn't have a computer science degree, deep down

English

Data Management

team meeting to discuss data quality SLA

Dylan JonesDecember 16, 2016 0

Creating analytics? Don't forget a supplier SLA policy.

During a data quality assessment, one of my clients discovered that a large chunk of data that ultimately fed into their business analytics engine was sourced externally. After examining the contracts surrounding this data, I found that 100% of it failed to possess service-level agreements (SLAs) for the quality of

English

Data Management

computer hacker collecting identifiers about individual

David LoshinDecember 14, 2016 0

Identity, identification and the proliferation of identifiers

I was surprised to learn recently that despite the reams of laws and policies directing the protection of personally identifiable information (PII) across industries and government agencies, more than 50 million Medicare beneficiaries were issued cards with a Medicare Beneficiary Number that's based on their Social Security Number (SSN). That's

English

Data Management

woman with tablet, managing data where it lives

Jim HarrisDecember 12, 2016 0

Managing data where it lives

Historically, before data was managed it was moved to a central location. For a long time that central location was the staging area for an enterprise data warehouse (EDW). While EDWs and their staging areas are still in use – especially for structured, transactional and internally generated data – big

English

Data Management

abstract programming and computer screen represent the importance of metadata

Joyce Norris-MontanariDecember 9, 2016 0

Importance of metadata – Bridging the gap (Part 2: transformation and movement)

In the first blog of this four-part series, we discussed traditional data management and how we can apply these principles to our big data platforms. We also discussed how metadata can help bridge the gap of understanding the data as we move to newer technologies. Part 2 will focus on

English

Data Management

group of people represent crowdsourcing data governance

Phil SimonDecember 7, 2016 0

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.

English

Data Management

Helmut PlinkeDecember 5, 2016 0

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

English

Data Management

Joyce Norris-MontanariDecember 2, 2016 0

Importance of metadata – Bridging the gap (Part 1: source system)

Traditional data management includes all the disciplines required to manage data resources. More specifically, data management usually includes: Architectures that encompass data, process and infrastructure. Policies and governance surrounding data privacy, data quality and data usage. Procedures that manage a data life cycle from creation of the data to sunset

English

Data Management

coworkers harmonizing semantics for analytic consistency

David LoshinNovember 28, 2016 0

Harmonizing semantics for consistency in interpreting analytical results

One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source

English

Blogs

Blogs

Data Management