Data Management

Jim HarrisAugust 31, 2015 0

Data quality to "DI" for

There is a time and a place for everything, but the time and place for data quality (DQ) in data integration (DI) efforts always seems like a thing everyone’s not quite sure about. I have previously blogged about the dangers of waiting until the middle of DI to consider, or become forced

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Data Management

Phil SimonAugust 26, 2015 0

Data integration: Comparing traditional sources and big data

While not on the same level of Rush, I do fancy myself a fan of The Who. I'm particularly fond of the band's 1973 epic, Quadrophenia. From the track "5:15": Inside outside, leave me alone Inside outside, nowhere is home Inside outside, where have I been? The inside-outside distinction is rather apropos

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Data Management

David LoshinAugust 24, 2015 0

Data integration considerations for the data lake: Standardization and transformation

In my last post, I noted that the flexibility provided by the concept of the schema-on-read paradigm that is typical of a data lake had to be tempered with the use of a metadata repository so that anyone wanting to use that data could figure out what was really in

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Data Management

Jim HarrisAugust 20, 2015 0

Data governance and analytics

The intersection of data governance and analytics doesn’t seem to get discussed as often as its intersection with data management, where data governance provides the guiding principles and context-specific policies that frame the processes and procedures of data management. The reason for this is not, as some may want to

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Data Management

Georgia MarianiAugust 20, 2015 0

Education meets big data: key steps in preparing for your SLDS

In my first blog post in the Education meets Big Data blog series, I explained the need for Statewide Longitudinal Data Systems(SLDS). In the blog post following, I shared an interview with Armistead Sapp, one of the authors of the book, "Implement, Improve and Expand Your Statewide Longitudinal Data System." In this

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Education

Data Management

Phil SimonAugust 19, 2015 0

Big data integration: The case against an "all-in" approach

I've spent a great deal of time in my consulting career railing against multiple systems of record, data silos and disparate versions of the truth. In the mid-1990s, I realized that Excel could only do so much. To quickly identify and ultimately ameliorate thorny data issues, I had to up

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English

Data Management

Matthew MagneAugust 18, 2015 0

What do metadata bridges, HAWQ and gender analysis have in common?

Now that another summer of 12-hour family road-trips to Maine and Ohio, pricey engineering and basketball camps for the kids, and beating the heat at the beach are over, I've taken a fresh look at what people are focused on with their data – and what SAS is providing in the data management space.

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Data Management | Fraud & Security Intelligence

Stuart RoseAugust 13, 2015 0

Is good…good enough?

“Garbage in, garbage out” is more than a catchphrase – it’s the unfortunate reality in many analytics initiatives. For most analytical applications, the biggest problem lies not in the predictive modeling, but in gathering and preparing data for analysis. When the analytics seems to be underperforming, the problem almost invariably

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English

Data Management

David LoshinAugust 12, 2015 0

Data integration considerations for the data lake: The need for metadata

A few of our clients are exploring the use of a data lake as both a landing pad and a repository for collection of enterprise data sets. However, after probing a little bit about what they expected to do with this data lake, I found that the simple use of

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Data Management

Dylan JonesAugust 10, 2015 0

Justifying the need for a data governance business case

Many people who plan data governance initiatives ignore the need for a business case. "We've already had approval for the project; why do we need a business case when we've got the budget signed off?" The perception is that because they have a strong commitment, there is no need to get

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Data Management

Phil SimonAugust 6, 2015 0

What is big data integration?

.@philsimon offers his definition of the term.

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English

Data Management

Guido OswaldAugust 3, 2015 0

Data quality on Hadoop: The easy way

Bigger doesn’t always mean better. And that’s often the case with big data. Your data quality (DQ) problem – no denial, please – often only magnifies when you get bigger data sets. Having more unstructured data adds another level of complexity. The need for data quality on Hadoop is shown by user

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English

Data Management

David PopeJuly 31, 2015 0

Oil and gas data management overview

In the oil and gas industry, analytics are used to improve both upstream and downstream operations, from optimizing exploration and forecasting production to reducing commodity trading risk and understanding customer's energy needs. If you plan to derive value from the digital oil field, big data, and analytics, one of the first things

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Data Management

Kelly McGuireJuly 23, 2015 0

Just get me my data! (Please): How advances in data management are making it easier for business users to access data

We’ve been talking about data recently at the Analytic Hospitality Executive. I’ve advocated to use whatever data you have, big or small, to get started today on analytic initiatives that will help you avoid big data paralysis. In this blog, I’m going to get a bit more technical than usual

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English

Hospitality

Advanced Analytics | Data Management

Andreas GöddeJuly 20, 2015 0

Three ways to save with a big data lab

I’ve had a lot of discussions with business leaders around the discrepancy between big data investment fears and successful use cases. Most of them say that "the quest for the golden use case" takes too much time and is usually not successful in the end. Ultimately, this quest can lead to

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Data Management

David LoshinJuly 17, 2015 0

Data modeling for data policy management

Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.

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Analytics | Customer Intelligence | Data Management

SAS ColombiaJuly 15, 2015 0

6 maneras de repensar su estrategia de gestión de datos y evitar los peores escenarios

“Aquellos que no conocen su pasado están condenados a repetirlo” La retrospección es un proceso lento. Así como en los seres humanos aún persisten en el tiempo comportamientos que no funcionan, en las organizaciones perduran procesos de información que se rompen y pueden causar grandes crisis ¿cómo evitarlo? En la

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Spanish

Data Management

Joyce Norris-MontanariJuly 14, 2015 0

Common problems and pitfalls when starting a data governance project

Guess what? Data governance can be considered a bottleneck and a bothersome activity at some organizations. So let’s discuss how NOT TO BE the BOTTLENECK. Defining what the data governance initiative will entail is very important here.

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English

Data Management

Phil SimonJuly 9, 2015 0

What Zappos' bold moves teach us about data governance

.@philsimon on whether companies should apply some radical tactics to DG.

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Data Management

Jim HarrisJuly 7, 2015 0

Are data governance and MDM still inseparable?

Yes. But since this post needs to be more than a one-word answer to its title, allow me to elaborate. Data governance (DG) enters into the discussion of all enterprise information initiatives. Whether or not DG should be the opening salvo of these discussions is akin to asking whether the

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Analytics | Data Management

Mark TorrJuly 7, 2015 0

What’s the future of analytics within the enterprise architecture?

What does the future of analytics look like in your organizations enterprise architecture? Does it include thinking about a two speed approach to analytics which includes both: An agile rapidly changing analytics platform for innovation (a lab) seperated from operations and broad enterprise audience usage A slowly moving systematic enterprise analytics platform (a factory)

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English

Data Management

Dylan JonesJuly 2, 2015 0

Struggling with data governance alignment? Look to history.

If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the

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Data Management | Programming Tips | SAS Administrators

David SternJuly 2, 2015 0

Anonymization for data managers

I’ve spent some time over the past couple of months learning more about anonymization. This began with an interest in the technical methods used to protect sensitive personally-identifiable information in a SAS data warehouse and analytics platform we delivered for a customer. But I learned that anonymization has two rather different meanings; one in the

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English

Data Management

David LoshinJune 30, 2015 0

Data policies and data governance

In recent years, we practitioners in the data management world have been pretty quick to conflate “data governance” with “data quality” and “metadata.” Many tools marketed under "data governance" have emerged – yet when you inspect their capabilities, you see that in many ways these tools largely encompass data validation and data standardization. Unfortunately, we

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English

Analytics | Data Management | Data Visualization

Greg WoodJune 25, 2015 0

Are you missing out when it comes to data monetization?

After doing some recent research with IDC®, I got to thinking again about the reasons that organizations of all sizes in all industries are so slow at adopting analytics as part of their ‘business as usual’ operations. While I have no hard statistics on who is and who isn’t adopting

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English

Analytics | Data Management | Data Visualization

Daniel TeacheyJune 22, 2015 0

Build a culture that embraces big data challenges

Big data. Streaming data. Complex data. We’ve all heard the reasons why organizations feel like they’re facing an insurmountable data challenge. Now, it’s time to do something about it. For the past few years, SAS has helped some of the world’s leading companies make sense of an avalanche of data.

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English

Data Management

Phil SimonJune 22, 2015 0

Is effective data governance possible in an era of big data?

.@philsimon on the new challenges of data governance.

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English

Data Management | Internet of Things

Jim HarrisJune 18, 2015 0

ESP can determine if big data is eventful

Jim Harris says event stream processing determines if big data is eventful and relevant enough to process and store.

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English

Data Management | Data Visualization

Leo SadovyJune 16, 2015 0

Is Data an Asset? The importance of the metaphors we use for data

The metaphors we choose to describe our data are important, for they can either open up the potential for understanding and insight, or they can limit our ability to effectively extract all the value our data may hold.  Consisting as it does of nothing but electric potentials, or variations in

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Data Management

Joyce Norris-MontanariJune 15, 2015 0

Event stream processing – Tips 2 and 3: Understand the life cycle of the data, collection and consumption

Determining the life cycle of event stream data requires us to first understand our business and how fast it changes. If event data is analyzed, it makes sense that the results of that analysis would feed another process. For example, a customer relationship management (CRM) system or campaign management system like

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