The Explosion of “Manifestos” in Data and Analytics
Data professionals continue to experiment with new ways to approach analytics that improve outcomes for enterprises. Some propose dramatic changes while others are evolutionary. Here’s our list of the top data and analytics manifestos that industry leaders have proposed (in no particular order). Please tweet us if we missed any.
Manifesto for Radical Analytics
Some people heard me say “Radical Analytics” and were a bit disconcerted by the term — but I assure you, “radical” is really what I mean, as you will see in my upcoming articles.
Agile Analytics Manifesto — ZEPL
In today’s data driven, big data world, analytics velocity — and I don’t mean computing speed — is paramount. Monolithic, non-collaborative, opaque processes are enemies of velocity. How can we change this?Much like the Agile Software Development movement, I propose that we also need an Agile Analytics Manifesto.
Agile Analytics Manifesto — IIA
The ideas behind agile were originally conceived on a ski trip in 2001 and ultimately published as the Manifesto for Agile Software Development. The Agile Manifesto outlines areas of emphasis and a series of 12 principles. I’ve adjusted these principles below to articulate an Agile Analytics Manifesto where an analytics team’s “customers” are its internal business partners and stakeholders.
Manifesto for Agile Data Science
The Agile Data Science Manifesto focuses on how to think, rather than on what to do. The specifics of Kanban or Scrum work for data science, so long as the team thinks dynamically in response to opportunities that emerge from exploring data. The key is that you approach data science in an active and dynamic way.
Data-First Manifesto
We propose a strategy for conducting digital humanities teaching and research that prioritizes publishing data above all other project activities. Drawing on our experience working with faculty, librarians, and graduate students on a critical edition in TEI of Charles Baudelaire’s Les Fleurs du Mal, we have created a manifesto to support a data-first strategy in the digital humanities.
Data Manifesto
We need a Data Revolution that sets a new political agenda, that puts existing data to work, that improves the way data is gathered and ensures that information can be used. To deliver this vision, we need the following steps. 12 Steps to a Data Revolution.
Professional Data Science Manifesto
The latest technology advancements have made data processing accessible, cheap and fast for everyone. We believe combining engineering practices with the scientific method will extract the most utility from these advancements. So this manifesto proposes a principled methodology for unifying science and technology.
Data-Centric Manifesto
We have uncovered a root cause of the messy state of Information Architecture in large institutions today. It is the prevailing application-centric mindset that gives applications priority over data. The remedy is to flip this on its head. Data is the center of the universe; applications are ephemeral. These are the key principles of the data centric manifesto.
Data Practice Values and Principles
We believe these values and principles, taken together, describe the most effective, ethical, and modern approach to data teamwork. Data Practice Values and Principles
Leader’s Data Manifesto
The Leader’s Data Manifesto: Your organization’s best opportunities for organic growth lie in data. Data offers enormous untapped potential to create competitive advantage, new wealth and jobs; improve health care; keep us all safer; and otherwise improve the human condition. Organizations are far from being data-driven…
DataOps Manifesto
DataOps is an analytic development method that emphasizes communication, collaboration, integration, automation, measurement and cooperation between data scientists, analysts, data/ETL (extract, transform, load) engineers, information technology (IT), and quality assurance/governance. The method acknowledges the interdependence of the entire end-to-end analytic process. It aims to help organizations rapidly produce insight, turn that insight into operational tools, and continuously improve analytic operations and performance. It enables the whole analytic team involved in the analytic process to follow the values laid out in the DataOps Manifesto.
