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Data Management

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

Data Management

Charlie ChaseMarch 12, 2015 0

Are you using downstream data to improve forecast accuracy?

Downstream data have been electronically available on a weekly basis since the late 1980s. But most companies have been slow to adopt downstream data for planning and forecasting purposes. Let's look at why that is. Downstream data is data that originates downstream on the demand side of the value chain. Examples

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Data Management

Clark BradleyMarch 12, 2015 0

Provisioning data for advanced analytics in Hadoop

The data lake is a great place to take a swim, but is the water clean? My colleague, Matthew Magne, compared big data to the Fire Swamp from The Princess Bride, and it can seem that foreboding. The questions we need to ask are: How was the data transformed and

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Data Management | Internet of Things

David FredeMarch 10, 2015 0

Internet of Things: The industrial internet

Today in manufacturing there has been a lot of investment in automation, supervisory controls, quality, and execution systems. The amount of data produced and now being captured is staggering. The data captured in industry will re-define what is “big” in big data. Yet, for all this investment: Equipment still fails. Scrap

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Data Management

David LoshinMarch 10, 2015 0

Using Hadoop: Emerging options for improved query performance

In my last two posts, we concluded two things. First, because of the need for broadcasting data across the internal network to enable the complete execution of a JOIN query in Hadoop, there is a potential for performance degradation for JOINs on top of files distributed using HDFS. Second, there are

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Data Management

Mazhar LeghariMarch 9, 2015 0

Showing the ugly face of bad data: Part 2

In my previous post, I talked about how a bank realized that data quality was central to some very basic elements of its initiatives, such as know your customer (KYC), customer on-boarding and others. In this blog, let’s explore what this organization did to foster an environment of data quality

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Data Management

Dylan JonesMarch 5, 2015 0

The impact of data quality reach

One of the common traps I see data quality analysts falling into is measuring data quality in a uniform way across the entire data landscape. For example, you may have a transactional dataset that has hundreds of records with missing values or badly entered formats. In contrast, you may have

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Data Management

Matthew MagneMarch 4, 2015 0

SAS Data Loader for Hadoop helps your data heroes navigate the fire swamp of big data

In The Princess Bride, one of my favorite movies, our hero Westley – in an attempt to save his love, Buttercup – has to navigate the Fire Swamp. There, Westley and Buttercup encounter fire spouts, quicksand and the dreaded rodents of unusual size (RUS's). Each time he has a response to the

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Data Management

David LoshinMarch 3, 2015 0

Using Hadoop: Query optimization

In my last post, I pointed out that an uninformed approach to running queries on top of data stored in Hadoop HDFS may lead to unexpected performance degradation for reporting and analysis. The key issue had to do with JOINs in which all the records in one data set needed

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Data Management

Mazhar LeghariMarch 2, 2015 0

Showing the ugly face of bad data: Part 1

Financial institutions are mired with large pools of historic data across multiple line of businesses and systems. However, much of the recent data is being produced externally and is isolated from the decision making and operational banking processes. The limitations of existing banking systems combined with inward-looking and confined data practices

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Data Management

Phil SimonMarch 2, 2015 0

What skills will be required to make sense of big data?

Small data is akin to algebra; big data is like calculus.

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Data Management

Jim HarrisFebruary 25, 2015 0

Big wishes for data management

In the movie Big, a 12-year-old boy, after being embarrassed in front of an older girl he was trying to impress by being told he was too short for a carnival ride, puts a coin into an antique arcade fortune teller machine called Zoltar Speaks, makes a wish to be big,

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Analytics | Data Management

Craig RubendallFebruary 19, 2015 0

Why SAS joined the Open Data Platform (ODP) initiative

As the point person for SAS joining the new Open Data Platform (ODP) initiative, I want to make it clear why SAS is involved with ODP, and why we think it’s important to our customers, and the Hadoop and big data ecosystem as a whole. SAS is not in it to

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Data Management

David LoshinFebruary 19, 2015 0

Using Hadoop: Impacts of data organization on access latency

Hadoop is increasingly being adopted as the go-to platform for large-scale data analytics. However, it is still not necessarily clear that Hadoop is always the optimal choice for traditional data warehousing for reporting and analysis, especially in its “out of the box” configuration. That is because Hadoop itself is not

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Data Management

Phil SimonFebruary 17, 2015 0

What's the future of data? In a word, more.

When asked what his movement wanted around a century ago, the iconic American labor leader Samuel Gompers famously gave a one-word answer: "More." This annoyed his opponents at the negotiating table and many in the business community. He was not demanding a specific wage increase or fighting for a distinct cause like

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Data Management

Joyce Norris-MontanariFebruary 16, 2015 0

The three myths of ongoing data quality: Financial gains based on data quality

If you are looking for a way to fund your data quality objectives, consider looking in the closets of the organization. For example, look for issues that cost the company money that could have been avoided by better availability of data, better quality of the data or reliability of the

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Data Management

Felix LiaoFebruary 16, 2015 0

The Relevance of Data Management in the Era of Big Data

Data Management has been the foundational building block supporting major business analytics initiatives from day one. Not only is it highly relevant, it is absolutely critical to the success of all business analytics projects. Emerging big data platforms such as Hadoop and in-memory databases are disrupting traditional data architecture in

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Customer Intelligence | Data Management

Natalie OsbornFebruary 13, 2015 0

The 360-degree view: Gaining a complete picture of your guests

For hotel companies, it is challenging to find new ways to differentiate in an ever evolving marketplace. There is a lot of talk in our industry about the increasing numbers of third party channels and distributors to have entered the marketplace, and how that impacts the hotel company’s core business.

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Data Management

Stuart RoseFebruary 12, 2015 0

Welcome to “data-driven decisions”

Business analytics is about dramatically improving the way an organization makes decisions, conducts business and successfully competes in the marketplace. At the heart of business analytics is data. Historically, the philosophy of many insurers has been on collecting data, data and more data. However, even with all this data, many

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Data Management

Jim HarrisFebruary 11, 2015 0

Crowdsourcing data improvement: Part 3

In this blog series, I am exploring if it’s wise to crowdsource data improvement, and if the power of the crowd can enable organizations to incorporate better enterprise data quality practices. In Part 1, I provided a high-level definition of crowdsourcing and explained that while it can be applied to a wide range of projects

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Data Management

Joyce Norris-MontanariFebruary 9, 2015 0

The three myths of data quality: Put it in production and leave it alone

Once in a while, people run into an issue with the data that doesn't really need to be fixed right to ensure success of a specific project. So, the data issues are put into production and forgotten. Everyone always says, “We will go back and correct this later.” But that

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Data Management

Dylan JonesFebruary 6, 2015 0

Compliance finally gives data quality the platform it deserves

Regulatory compliance is a principal driver for data quality and data governance initiatives in many organisations right now, particularly in the banking sector. It is interesting to observe how many financial institutions immediately demand longer timeframes to help get their 'house in order' in preparation for each directive. To the

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Data Management

Jim HarrisFebruary 4, 2015 0

Crowdsourcing data improvement: Part 2

In this blog series, I am exploring if it’s wise to crowdsource data improvement, and if the power of the crowd can enable organizations to incorporate better enterprise data quality practices. In Part 1, I provided a high-level definition of crowdsourcing and explained that while it can be applied to a wide range of projects

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Data Management

Joyce Norris-MontanariFebruary 3, 2015 0

The three myths of on-going data quality: If you don't see the problem, there is no problem

There are companies that have no data quality initiative, and truly do believe that if they see no data problem. In effect, they say that if it does not interfere with day-to-day business, then there is no data quality problem. From what I have seen in my consulting experience, it usually

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Data Management

Kelly McGuireFebruary 2, 2015 0

The Cornell School of Hospitality Administration: What will 2015 bring?

We asked our partners at the Cornell Center for Hospitality Research to poll the research faculty at the Hotel School to understand their guidance about what to expect in 2015. We were also able to get a preview of what the faculty will be working on in terms of research

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Data Management

David LoshinFebruary 2, 2015 0

Implications of coalescing data quality

Over my last two posts, I suggested that our expectations for data quality morph over the duration of business processes, and it is only at a point that the process has completed that we can demand that all statically-applied data quality rules be observed. However, over the duration of the

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Data Management

Dylan JonesJanuary 27, 2015 0

Is your data quality strategy continually evolving?

One of the significant problems data quality leaders face is changing people's perception of data quality. For example, one common misconception is that data quality represents just another data processing activity. If you have a data warehouse, you will almost certainly have some form of data processing in the form

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Analytics | Data Management | Internet of Things

Leo SadovyJanuary 27, 2015 0

Creating value on the IoT – It ain’t about you

The Internet of Things is going to be driven by innovative business models as much as by innovative technology. In order to ground the following discussion, I found it helpful to create this visual depiction of the IoT that defines and distinguishes the key elements that enter into these business models.

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Data Management

David LoshinJanuary 26, 2015 0

Dynamic data and coalescing quality

In my last post, I pointed out that we data quality practitioners want to apply data quality assertions to data instances to validate data in process, but the dynamic nature of data must be contrasted with our assumptions about how quality measures are applied to static records. In practice, the

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Data Management

Helmut PlinkeJanuary 23, 2015 0

Big data quality

Utilizing big data analytics is currently one of the most promising strategies for businesses to gain competitive advantage and ensure future growth. But as we saw with “small data analytics,” the success of “big data analytics” relies heavily on the quality of its source data. In fact, when combining “small” and “big” data

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