This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
I'm excited about the power of a well created dashboard. Dashboards are every where, we will look at a lot of them in this post and they are all digital. Here's a great dashboard, for the Museum of Art… take a minute to ponder it… Isn't it pretty awesome? They are data pukes. Still a data puke.
Better dashboards, better decisions. A well-constructed and organized dashboard empowers users to make better data-driven decisions. But how can you recognize readability issues in your dashboards while you build them to avoid wasting time and endlessly redoing your work? Pitfalls of a disorganized dashboard.
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.
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. So how is the data extracted?
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?
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content