Data Management

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

Data Management | Learn SAS | Programming Tips
Leonid Batkhan 0
Modifying variable attributes in all datasets of a SAS library

Using the DATASETS procedure, we can easily modify SAS variable attributes such as name, format, informat and label: proc datasets library=libref; modify table_name; format var_name date9.; informat var_name mmddyy10.; label var_name = 'New label'; rename var_name = var_new_name; quit; We cannot, however, modify fixed variable attributes such as variable type

Data Management
Jim Harris 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 –

Data Management
Jim Harris 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

Data Management
Dylan Jones 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

Data Management
Jim Harris 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

Data Management
Joyce Norris-Montanari 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.

Data Management
Jim Harris 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

Data Management | Machine Learning
Charlie Chase 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

Data for Good | Data Management
Dan Stevens 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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