Data Management University Blog

Agile Program Framework for Data and Analytics

March 6, 2019

It is important to connect program-level agile frameworks with data and analytics delivery and the variety of application programs that will benefit from agile, flexible development Introduction As many organizations move beyond agile for individual projects, they make a transition to program-level agile frameworks. Although these frameworks provide some guidance on how to support agile programs with shared services provided by enterprise

Scope Creep Challenge Stakeholders Want More

February 22, 2019

The Business Analyst is confronted with the stakeholder’s desire to modify features, functions and requirements “just because” resulting in scope creep. Why are the stakeholders asking for changes and what can a Business Analyst do about it? Introduction Project scope defines the extent of the project; “scope creep” is the tendency to add features, functions, business rules or other requirements which expands

The Power of Mentorship for Data Management Professionals

January 31, 2019

Data scientists and other data professionals need a combination of education, practical experience, and soft skills to succeed and have a positive impact Introduction Gone are the days of the Wild, Wild West for data scientists. In those heady days, the job search process for entry-level data scientists and beyond was relatively painless. No more. As the profession has matured

Cyber-bullying and Online Harassment

January 13, 2019

The pervasive and anonymous use of social media has increased the rate and frequency of online harassment and cyber-bullying, endangering the data, safety, reputation, and even lives of many people Introduction It is no secret that people are bullied, harassed, and threatened over the internet, to the point where, if someone disagrees with another person, death threats often are the

The Value of Context to Textual Data Analysis

January 9, 2019

Context is essential to the meaning of any text. The value of context in textual data analysis cannot be overlooked. We take context for granted. Context is free and natural and just comes with data. Right? When we look into this proposition we find out that it is anything but true. Context is necessary but it does not come free

Requirements for Agile Projects

December 14, 2018

There is no difference between requirements for agile projects and those for traditional projects. Therefore, the preferred phrase should be “requirements for agile projects” Agile development approaches are used currently in most software organizations at least some of the time. With the rise in popularity of agile methods, business analysts and product owners often use the term “agile requirements” to

21st Century Data Management Professions

November 27, 2018

Data-oriented positions and professions may be less vulnerable to disruption by automation and new technologies, due to data management’s reliance on critical thinking, analysis, and human interaction Introduction No matter what job anyone has, the person has to know how to do that job. That is, after all, the reason the candidate was hired. Simple enough, right? Not really. The

Data Analytics and Big Data Management

October 10, 2018

Data and business analytics, blockchain, data science, big data management, and other trends in data management and technology will enable efficiencies in risk management and insurance for many years. Introduction In Part One of this series, we spoke of how the great volume of new data types, such as data generated by the Internet of Things, will be a catalyst

Waterfall versus Agile Project Management

October 1, 2018

Waterfall and agile project management styles have strengths and weaknesses. Both are suitable in specific situations. Know the strengths and weaknesses, and when to use one style over another for successful project management. Introduction Many project management professionals inquire about “agile project management”. Since Agile is a bit difficult to describe by itself, it is better to outline the more

Data Models Ensure Successful Migrations

September 21, 2018

When migrating data across platforms, it is critically important to have three full data models to support the effort, with the relevant metadata. Having these models will reduce time, costs, and effort needed to perform the migration successfully. Introduction Migrating data from one platform (e.g., legacy applications) to another (e.g., DW, ERP, MDM) is a difficult endeavor requiring understanding of

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Agile Program Framework for Data and Analytics

Scope Creep Challenge Stakeholders Want More

The Power of Mentorship for Data Management Professionals

Cyber-bullying and Online Harassment

The Value of Context to Textual Data Analysis

Requirements for Agile Projects

21st Century Data Management Professions

Data Analytics and Big Data Management

Waterfall versus Agile Project Management

Data Models Ensure Successful Migrations

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