Dashboarding - matthewfincher/blacksky GitHub Wiki
Executives (High Level) Data visualization
Executives want access to user-driven, visual, interactive dashboards with data from a broad range of sources that speed time to insight.
Over the next ten years, data complexity and volume will continue to grow as modern organizations add new ways to store and consume data. What’s required is a drag-and-drop agile solution that can talk or connect to any source of data quickly, regardless of what that source is or where it exists; whether it’s a new database, a new data model, or a cloud API.
Software Developers (Low Level) Cloud9Charts, Kibana
Data visualization implies a tool set that allows a non-coder to build data visualizations from building blocks, as apposed to wiring up hard-coded charts. Because they write code, Software Developers are primarily focused on using charts to power embedded reporting and dashboards. Data visualization is a secondary concern.
However, a subset of Software Developers are focused on solving the really hard part of data visualization: the analytics problem. These Developers want to do analytics on any type of data in its native environment, without relocation or transformation.
Data Power Users (Mid Level) Apache Zepplin
Data Power Users, like data scientists and data analysts, need tools to help them explore data; sculpt it; better discover and understand it. Visualization for Data Power Users is often just a means to end.
For Developers, charts are great. Charts make dashboards and reporting possible. But for Data Power Users, it’s all about tinkering with data and building data analytic workflows; they are very focused on understanding and wrangling the data until it’s suitable for use downstream. This sort of ad hoc and exploratory use case is becoming more common, and we need to devote time and resources to make sure we get the right tools for Data Power Users.
Butler Analytics
2015 - Integrated Enterprise Analytics
Business Intelligence Cheat Sheet 2017
2015 - Qlik Sense and Tableau Positioning
Gartner
Gartner 2016 - Market Guide for Enterprise-Reporting-Based Platforms
Business Intelligence and Analytics
Gartner 2016 - Critical Capabilities for Business Intelligence and Analytics Platforms
Gartner 2016 - Magic Quadrant for Business Intelligence and Analytics Platforms
Gartner 2016 - Reviews for BI and Analytics Platforms
The business intelligence (BI) market has reached an inflection point. Organizations want agile workflow and self-service analytics. Gartner's 2016 Magic Quadrant for Business Intelligence and Analytics Platforms stated the report represents a fundamental change in how it evaluates vendors.
Gartner redefines what BI and analytics platforms should look like in an era defined by the business need for fast-moving intelligence that is unconstrained by central services bottlenecks. In this scenario, BI and analytics tools become an underpinning to the entire infrastructure and all its tools, easily accessed by users who need the intelligence, rather than a discrete tool provisioned and allocated by central IT.
"easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models up front as a prerequisite to analysis"
Gartner said that this change aligns with the idea of bimodal IT, where Mode 1 represents traditional IT delivery and Mode 2 represents the type of agile delivery usually enjoyed by digital native companies.
BI In 2018 Gartner based this year's report on three strategic planning assumptions expected by 2018:
- Most business users will have access to self-service tools to prepare data for analysis.
- Most standalone self-service data preparation offerings will either have expanded into end-to-end analytic platforms or have been integrated as features into existing analytics platforms.
- Smart, governed, Hadoop-based, search-based, and visual-based data discovery will converge in a single form of next-generation data discovery that will include self-service data preparation and natural-language generation. Gartner recommended that IT organizations take a measured approach to evolving their BI and analytics strategies. Many existing enterprise reporting systems are "integral to day-to-day business processes, and these processes would be exposed to unnecessary risk if disrupted by an attempt to recreate them in a modern platform."
However, "organizations should initiate new BI and analytics projects using a modern platform that supports a Mode 2 delivery model, in order to take advantage of market innovation and to foster collaboration between IT and the business through an agile and iterative approach to solution development."
Advanced Analytics Platforms
Gartner 2016 - Critical Capabilities for Advanced Analytics Platforms
Gartner 2016 - Magic Quadrant for Advanced Analytics Platforms