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 […]
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 […]
The post Operational data governance: Policy vs. procedure for data validation appeared first on The Data Roundtable.
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 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. First, many operational data integration projects are not configured […]
The post Operational data governance: Who owns data quality problems? appeared first on The Data Roundtable.
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 […]
.@philsimon on the need to adopt agile methodologies for data prep and analytics.
The post Why an agile mind-set will lead to better data prep and analytics appeared first on The Data Roundtable.
Lately I've been binge-watching a lot of police procedural television shows. The standard format for almost every episode is the same. It starts with the commission or discovery of a crime, followed by forensic investigation of the crime scene, analysis of the collected evidence, and interviews or interrogations with potential suspects. It ends […]
The post The importance of a business glossary for data preparation appeared first on The Data Roundtable.
Critical business applications depend on the enterprise creating and maintaining high-quality data. So, whenever new data is received – especially from a new source – it’s great when that source can provide data without defects or other data quality issues. The recent rise in self-service data preparation options has definitely improved the quality of […]
The post Is data quality a component of data preparation? Or vice versa? appeared first on The Data Roundtable.
Hadoop has driven an enormous amount of data analytics activity lately. And this poses a problem for many practitioners coming from the traditional relational database management system (RDBMS) world. Hadoop is well known for having lots of variety in the structure of data it stores and processes. But it's fair to […]
The post Data preparation strengthens Hadoop information chain appeared first on The Data Roundtable.
In my last post, I talked about how data still needs to be cleaned up – and data strategy still needs to be re-evaluated – as we start to work with nontraditional databases and other new technologies. There are lots of ways to use these new platforms (like Hadoop). For example, many […]