erwin

JANUARY 24, 2020

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose. Conceptual Data Models: High-level, static business structures and concepts. Logical Data Models: Entity types, data attributes and relationships between entities.

Modeling 143

Modeling Business Driver Sales Data-driven 143

CIO Business Intelligence

AUGUST 2, 2023

So Thermo Fisher Scientific CIO Ryan Snyder and his colleagues have built a data layer cake based on a cascading series of discussions that allow IT and business partners to act as one team. Martha Heller: What are the business drivers behind the data architecture ecosystem you’re building at Thermo Fisher Scientific?

Manufacturing 79

Manufacturing Data Architecture Strategy Data Strategy 79

Sisense

JULY 15, 2020

What is the business plan going forward? Some teams are being furloughed, some are being released, and talent leaders need to know how they build a capacity model to keep a flexible, yet data-driven capacity to keep the recruitment engine going.” Nurit Shiber, Chief People Officer, Sisens e. Nurit Shiber, Chief People Officer, Sisense.

Data-driven 84

Data-driven Metrics Business Driver Visualization 84

BizAcuity

MARCH 31, 2022

Combined, it has come to a point where data analytics is your safety net first, and business driver second. For accurate predictions, companies now use various data models, machine and deep learning techniques to continuously improve and refine the quality of the outcome. Intense competition at every level. AI Services.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Sisense

AUGUST 15, 2019

AI Exploration automatically generates visualizations and deeper insights that anticipate the next question from a business user without the involvement of a business analyst. Release Q3 2019 Summary.

Slice and Dice 60

Slice and Dice Analytics Dashboards Business Driver 60

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. Today, free visualizations seem to be everywhere. It’s all about context. End users expect more from analytics too.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52