article thumbnail

Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

What is a data architect? Skills, salaries, and how to become a data framework master

CIO Business Intelligence

Data architect Armando Vázquez identifies eight common types of data architects: Enterprise data architect: These data architects oversee an organization’s overall data architecture, defining data architecture strategy and designing and implementing architectures.

article thumbnail

5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

article thumbnail

The New Normal for FP&A: Data Analytics

Jedox

In addition to using data to inform your future decisions, you can also use current data to make immediate decisions. Some of the technologies that make modern data analytics so much more powerful than they used t be include data management, data mining, predictive analytics, machine learning and artificial intelligence.

article thumbnail

Quantitative and Qualitative Data: A Vital Combination

Sisense

Traditional methods of gathering and organizing data can’t organize, filter, and analyze this kind of data effectively. What seem at first to be very random, disparate forms of qualitative data require the capacity of data warehouses , data lakes , and NoSQL databases to store and manage them.

article thumbnail

The rise of the data lakehouse: A new era of data value

CIO Business Intelligence

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. Lakehouses redeem the failures of some data lakes.

Data Lake 139