SAS Blogs

Data Management

Blend, cleanse and prepare data for analytics, reporting or data modernization efforts

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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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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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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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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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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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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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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Data Management | Internet of Things

Man on smartphone considers data privacy concerns

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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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

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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Data Management

Dylan JonesJune 11, 2015 0

Can ESP bridge the data quality gap?

As consumers, the quality of our day is all too often governed by the outcome of computed events. My recent online shopping experience was a great example of how computed events can transpire to make (or break) a relaxing event. We had ordered grocery delivery with a new service provider. Our existing provider

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Data Management

Joyce Norris-MontanariJune 9, 2015 0

Event stream processing – Tip 1: Don’t be overwhelmed

I believe most people become overwhelmed when considering the data that can be created during event processing. Number one, it is A LOT of data – and number two, the data needs real-time analysis. For the past few years, most of us have been analyzing data after we collected it,

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Data Management | Data Visualization

Daniel AunvigJune 9, 2015 0

All customer intelligence must be woven into CRM programs – online and offline

You've probably heard many times about the fantastic untapped potential of combining online and offline customer data. But relax, I’m going to cut out the fluff and address this matter in a way that makes the idea plausible and its objectives achievable. The reality is that while much has been

Data Management

David LoshinJune 4, 2015 0

Embedding event stream analytics

In my last two posts, I introduced some opportunities that arise from integrating event stream processing (ESP) within the nodes of a distributed network. We considered one type of deployment that includes the emergent Internet of Things (IoT) model in which there are numerous end nodes that monitor a set of sensors,

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Data Management

Jim HarrisJune 2, 2015 0

Three things that need to get real – real-time, that is

In my previous post, I discussed the similarities, differences and overlap between event stream processing (ESP) and real-time processing (RTP). In this post, I want to highlight three things that need to get real. In other words, three things that should be enhanced with real-time capabilities, whether it’s ESP, RTP or

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Data Management

Hugo D'UlisseJune 1, 2015 0

Use Hadoop to visualize what wasn’t visible before

You might have lots of data on lots of customers, but imagine if you could suddenly add in a huge dollop of new, highly informative data that you weren’t able to access before. You could then use analytics to extract some really important insights about these customers, allowing you to

Data Management

David LoshinMay 27, 2015 0

Pushing event analytics to the edge

In my last post, we examined the growing importance of event stream processing to predictive and prescriptive analytics. In the example we discussed, we looked at how all the event streams from point-of-sale systems from multiple retail locations are absorbed at a centralized point for analysis. Yet the beneficiaries of those

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Data Management

Considering how to be smarter with the Internet of Things

Patryk ChorośMay 22, 2015 0

Let us be smarter with the Internet of Things

As we enter the era of “everything connected,” we cannot forget that gathering data is not enough. We need to process that data to gain new knowledge and build our competitive advantage. The Internet of Things is not just a consumer thing – it also makes our businesses more intelligent. Whenever

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Data Management

Phil SimonMay 21, 2015 0

Why data visualization matters

.@philsimon says that you shouldn't bring a knife to a gun fight.

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Data Management

Joyce Norris-MontanariMay 20, 2015 0

Data management for analysis – Feeding the analytical monster more than once

(Otherwise known as Truncate – Load – Analyze – Repeat!) After you’ve prepared data for analysis and then analyzed it, how do you complete this process again? And again? And again? Most analytical applications are created to truncate the prior data, load new data for analysis, analyze it and repeat

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Data Management

Dr. Laurie MilesMay 20, 2015 0

There’s no ‘i’ in data science team

Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all

Data Management

Using cellphone and considering event stream processing versus real-time processing

Jim HarrisMay 20, 2015 0

Event stream processing and/or real-time processing

Event stream processing (ESP) and real-time processing (RTP) so often come up in the same conversation that it begs the question if they are one and the same. The short answer is yes and/or no. But since I don’t need the other kind of ESP to know that you won’t

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Data Management

Evan GuarnacciaMay 18, 2015 0

Event stream processing: Think "rapid"

What sends a data management product to the top of the “hot” list? In a word – speed. Especially when that speed can gracefully accommodate the huge world of streaming data from the Internet of Things. One of SAS’ hottest (and recently enhanced) products, SAS Event Stream Processing is an

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Data Management

David LoshinMay 12, 2015 0

Holistic analysis and event stream processing

Over the past year and a half, there has been a subtle shift in media attention from big data analytics to what is referred to as the Internet of Things, or IoT for short. The shift in focus is not intended to diminish the value of big data platforms and

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Data Management

David LoshinMay 8, 2015 0

Integration and publication: Data management for analytics

Once you have assessed the types of reporting and analytics projects and activities are to be done by the community of data analysts and consumers and have assessed their business needs and requirements for performance, you can then evaluate – with confidence – how different platforms and tools can be combined to satisfy

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Data Management

Young businessman works on data virtualization

Patryk ChorośMay 8, 2015 0

In data governance’s service: Data virtualization, part 2

Data governance and data virtualization can become powerful allies. The word governance is not be understood here as a law but more as a support and vision for business analytics application. Our governance processes must become agile the same way our business is transforming. Data virtualization, being a very versatile

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