Blogs

Blogs

Data Management

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

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 0

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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Analytics | Data Management

Iván LópezNovember 15, 2016 0

5 Beneficios del uso personalizado de Data Management para agilizar el ROI

Una de las preocupaciones constantes de toda compañía es desarrollar un ROI (Retorno de Inversión) positivo, es decir, tener la certeza de que las acciones realizadas como parte de la estrategia de negocio serán rentables. Por ello, diversos organismos de innovación empresarial han decidido implementar herramientas que permiten a las

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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

nurse holds a tarnished record

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

Helmut PlinkeNovember 9, 2016 0

Using suggestion-based matching in SAS Data Quality

We all have challenges in getting an accurate and consistent view of our customers across multiple applications or sources of customer information. Suggestion-based matching is a technique found in SAS Data Quality to improve matching results for data that has arbitrary typos and incorrect spellings in it. The suggestion-based concept

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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

Alan CudneyNovember 2, 2016 0

Four ways to continue the health care transformation

Electronic health records (EHRs) and the overall advancement of information technology have produced a tsunami of data that must be stored, managed and used. Some had naively hoped that EHRs would bring a simpler, more streamlined industry. Instead, we’re finding that the delivery and management of health care is more

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Data Management

Clemens Knobloch

Clemens KnoblochNovember 1, 2016 0

Calling SAS Data Quality jobs from Python

With DataFlux Data Management 2.7, the major component of SAS Data Quality and other SAS Data Management solutions, every job has a REST API automatically created once moved to the Data Management Server. This is a great feature and enables us to easily call Data Management jobs from programming languages

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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

observing data governance in action

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

planning operational data governance

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

data governance

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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Advanced Analytics | Data Management

David PopeOctober 24, 2016 0

Analytics: Four key elements for success

Analytics provides better insights into why something happened, or helps provide decision makers with information about what will happen in the future. That allows organizations to act now to improve outcomes instead of reacting to events after they happen. But it takes more than analytics alone. Achieving this level of

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Analytics | Data for Good | Data Management

doctor looking at clinical data on tablet

David DowningOctober 21, 2016 0

How mature is your decision-making strategy?

In recent healthcare blogs I’ve looked at the need to drive more value from the UK’s National Health Service (NHS) and how this relies upon the ability to make decisions based on robust, data-driven insights. But what value will these decisions have if they're not founded on a mature data

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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 | Programming Tips

Mary Kathryn QueenOctober 19, 2016 0

Using multiple SAS Quality Knowledge Base locales in a SAS Event Stream Processing compute window

In a previous blog post, I demonstrated combining the power of SAS Event Stream Processing (ESP) and the SAS Quality Knowledge Base (QKB), a key component of our SAS Data Quality offerings. In this post, I will expand on the topic and show how you can work with data from

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Data Management | Internet of Things

man contemplating: to stream or not to stream

Felix LiaoOctober 18, 2016 0

To stream or not to stream?

Hadoop may have been the buzzword for the last few years, but streaming seems to be what everyone is talking about these days. Hadoop deals primarily with big data in stationary and batch-based analytics. But modern streaming technologies are aimed at the opposite spectrum, dealing with data in motion and

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Data Management

woman looking for a way to define data governance

Joyce Norris-MontanariOctober 17, 2016 0

What's the difference between data governance and data management? (Part 2)

In Part 1 of this series, we defined data governance as a framework – something an organization can implement in small pieces. Data management encompasses the disciplines included in the data governance framework. They include the following: Data quality and data profiling. Metadata (business, technical and operational). Data security. Data movement within the enterprise.

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Data Management

hotel lobby representing what AirBNB teaches us about traditional data governance

Phil SimonOctober 14, 2016 0

What AirBNB teaches us about traditional data governance

.@philsmion says that even the "best governed" organization today isn't safe from inquiring minds.

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Data Management

Woman questioning who owns data quality problems?

David LoshinOctober 12, 2016 0

Operational data governance: Who owns data quality problems?

Data integration teams often find themselves in the middle of discussions where the quality of their data outputs are called into question. Without proper governance procedures in place, though, it's hard to address these accusations in a reasonable way. Here's why.

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Data Management

celebrating the value of data governance

Dylan JonesOctober 10, 2016 0

How I (reluctantly) learned the value of data governance

Data governance must encompass management of the full life cycle of a data policy – its definition, approval, implementation and the means of ensuring its observance - David Loshin, Data Policies and Data Governance I was checking out my Google stats on Data Quality Pro recently and observed that "How

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Data Management

considering how to define data governance

Jim HarrisOctober 7, 2016 0

How do you define data governance?

Data governance has been the topic of many of the recent posts here on the Data Roundtable. And rightfully so, since data governance plays such an integral role in the success of many enterprise information initiatives – such as data quality, master data management and analytics. These posts can help you prepare for discussing

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Data Management | Machine Learning

Charlie ChaseOctober 7, 2016 0

Machine learning changes the way we forecast in retail and CPG

Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the

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Manufacturing | Retail

Data for Good | Data Management

Dan StevensOctober 6, 2016 0

A playbook for analyzing real world intelligence in a health care setting

Real world data collected in a functioning health care setting instead of a controlled clinical environment can provide opportunities for new and deeper insights across life science and health care organizations. However, managing, analyzing and extracting actionable information from the varied available sources can present unique challenges. The sheer size of these

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Data Management

steel of building represents data governance

Joyce Norris-MontanariOctober 5, 2016 0

What's the difference between data governance and data management? (Part 1)

Lately, the definitions of data governance and data management look very much alike. In this two-part series, we'll define data governance and data management. And we'll see that there's a big difference in the two.

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Data Management

should we separate governance for data and analytics?

Phil SimonOctober 3, 2016 0

Should organizations separate analytics governance and data governance?

.@philsimon asks, Rather than trying to tackle a new form of governance, wouldn't your organization do better to shore up its existing data-governance practices?

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Data Management

Carmen GallagherSeptember 29, 2016 0

Big data allows us to rethink data strategy

Start with the end in mind -- wise words that apply to everything, and in the world of big data it means we have to change the way we look at managing the data we have. There was a time when we managed data quality, and the main goal was

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Data Management

businessman considering data governance skills

Dylan JonesSeptember 29, 2016 0

Data governance: The perfect marriage of soft and hard skills?

We've witnessed a significant rise in data governance adoption in recent years. Careers, technology, education, frameworks, practitioners – there's growth in all aspects of the discipline. Regulatory compliance across many sectors is a typical driver for data governance. But I also believe one of the main reasons is the realisation by

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