Every organization needs a set of roles that are responsible for managing the quality of the data and metadata that are used in the enterprise’s operations and analytics. These roles are the “data quality heroes.” Introduction Data management entails the proper management of data as well as the corresponding data definitions or metadata. It aims at ensuring that data and
The concept of enterprise information management includes all the disciplines that allow an organization to manage data and information as assets for a variety of purposes. Introduction Enterprise information management is one of the hottest topics in information technology (IT) today. Many metadata management efforts have gone well beyond the scope of data warehousing / business intelligence and instead are
A shift in technologies will expand traditional methods of data generation, data storage, and data analysis capabilities that will reduce friction and costs, while streamlining processes in many data-intensive industries like insurance. Introduction Part One of this series presented topics about the great volume of new data types, such as data generated by the Internet of Things (IoT), and how
Every organization needs a data strategy to develop and use the value of its data and information assets. Developing a data strategy requires executive commitment and perseverance to an enterprise view of data’s capabilities. Introduction If you speak with many CEOs, most will tell you they firmly believe data is a valuable corporate asset, which, if used properly, can deliver
The Business Analyst meets with stakeholders, but does the Business Analyst manage stakeholders? The successful BA should engage in stakeholder management to ensure the correct user provides requirements. Introduction A stakeholder is an individual who has an interest in your application and provides requirements, processes, or other information. The Business Analyst (BA) has the sometimes difficult task of eliciting the
Data manipulation, analysis and data management combine with scientific methods, processes, and systems to extract knowledge or insights from large amounts of data Introduction The phrase “data science” is akin to the proverbial elephant and the eight blind men – each one imagining what an “elephant” is by what he feels in the small area to which he has access.
Integrating the components of enterprise information management into a project centric organization can be challenging, but it is essential for successfully using data as an asset. Introduction One the challenges with implementing Enterprise Information Management (EIM) is integrating and gaining traction in a project centric organization. For companies with a more formalized Project Management organization, the organization views corporate work
Data generated by The Internet of Things is having a major effect on insurance companies’ approaches to enterprise data management Introduction Even before the use of computers, data was at the heart of the insurance business. Unlike other enterprises that sell a tangible product, insurers sell a promise. In exchange for your premium dollar today, the insurer promises to pay
Introduction Today’s digital world and fast paced business environment demands pervasive, timely analytics. Organizations must deliver speed, agility and ease of use for data access and analytics. In response, business intelligence (BI) and analytics teams are shifting to a more agile, self-service model that enables greater user access to data and the ability to create and perform individualized reports and
Samuel B. Holcman An enterprise architecture (EA) is a series of conceptual blueprints that defines the structure and operation of an organization to enable strategic progress. Enterprise Architecture (EA) explicitly describes an organization through a set of independent, non-redundant artifacts, defines how these artifacts relate with each other, and develops a set of prioritized, aligned initiatives and roadmaps to understand
