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Emission factor mapping and other capabilities As part of Oracle Fusion Cloud Sustainability, enterprises would get access to features such as automated transaction records, contextualizeddata, pre-built dashboards, emission factor mapping, and audit capabilities.
But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality. Consider how it looks to nontechnical executives when every digital transformation initiative has customized dashboards, different KPIs, and metrics with underlying data quality issues.
All these metrics will help the reader understand the metrics around how users interact with their trial software. The dashboard creator also provided an important metric (Total Hours on Site) in the top-left corner and filled the dashboard with charts that give more context to that number.
Introducing generative AI-powered data descriptions With AI-generated descriptions in Amazon DataZone, data consumers have these recommended descriptions to identify data tables and columns for analysis, which enhances data discoverability and cuts down on back-and-forth communications with data producers.
It also handy explanations of the metrics, with key context where necessary. They don't have an ability to analyze the data, should anything pique their interest, and neither will they ever want access to the contextualdata to do a… oh, wait, why did x happen , or I wonder if z is the reason Average Order Value is $356.
With IBM Instana and IBM Turbonomic, you can take integrated, granular metrics and dynamic topology from Instana to fuel action recommendations from Turbonomic. Contextualdata: Context is king, but it’s rarely achieved given our current IT systems.
Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextualdata points to uncover new insights and adjust tactics and decisions on the fly. So how is the data extracted?
How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.
Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextualdata points to uncover new insights and adjust tactics and decisions on the fly.
Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextualdata points to uncover new insights and adjust tactics and decisions on the fly.
Knowledge assembly in action To better understand why organizations fall short when assembling knowledge, we must first understand how knowledge assembly unfolds, starting with some basic concepts: Data are raw, unorganized facts, such as numbers, text, and images, that lack context and meaning on their own.
It’s a truism that data is the most important asset in the 21 st century economy. But, today too many enterprises exist in a data fog, with poorly contextualizeddata scattered across millions of tables. Dispelling this data fog is one of the key challenges for the next generation enterprise.
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