IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

Data science 60

Data science Big data Analytics Mining 60

Collibra

FEBRUARY 3, 2022

And bad data can rear its ugly head twice, both in the historical data used to train the predictive model and in the new data used by that model to make future decisions. Why data lakes suffer from quality issues. A data lake acts as a central repository of data.

Metadata 52

Metadata Analytics Unstructured data Government 52

Everteam

APRIL 16, 2019

One of the most important elements of an information governance program is the proper classification of your data. A central, formal classification scheme is critical, especially when much of the information – structured and unstructured – is used in multiple departments or teams across the organization.

ROT 40

ROT Information governance Metadata Government 40

Collibra

SEPTEMBER 9, 2022

Data-driven organizations are depending on modern technologies and AI to get the most out of their data assets. But they struggle with data quality issues all the time. Incomplete or inaccurate data, security problems, hidden data – the list is endless. The most common data quality problem statements.

Analytics 92

Analytics Customer Experience Marketing Unstructured data 92

Collibra

JUNE 25, 2021

Data-driven organizations are depending on modern technologies and AI to get the most out of their data assets. But they struggle with data quality issues all the time. Incomplete or inaccurate data, security problems, hidden data – the list is endless. The most common data quality problem statements.

Analytics 59

Analytics Customer Experience Unstructured data Metadata 59

IBM Big Data Hub

JUNE 21, 2023

The solution is built from a foundation model developed using watsonx, IBM’s enterprise-grade AI platform designed to manage the entire lifecycle of AI models, from curating trusted data sources to governing responsible, trusted AI. For AI Commentary, the team drew source material from nearly 130 million documents.

Unstructured data 78

Unstructured data Digital transformation Analytics Government 78

Collibra

JULY 27, 2022

Sometimes companies are unable to derive deeper, actionable insights from the data because the data is inaccurate, not accessible, or secure. . Forward-looking companies use data intelligence to remedy these issues. Data intelligence combines factors from data quality and governance to metadata management.

Metadata 52

Metadata Artificial Intelligence Analytics Unstructured data 52

Reltio

JANUARY 28, 2021

To illustrate, some companies were able to pivot faster to curbside pick up and virtual experiences that were traditionally run live in person, such as auctions, without creating a disconnected customer experience because they had a single source of truth for their enterprise data. 2 Make data governance an integral part.

MDM 98

MDM Customer Experience Marketing Government 98

IBM Big Data Hub

AUGUST 4, 2023

Employees and leaders need to trust the data is accurate, know how to access it, as well as how it could be applied to business problems. In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used.

Analytics 65

Analytics Artificial Intelligence Digital transformation Access 65

Brandeis Records Manager

SEPTEMBER 19, 2016

Like most people, I prefer to have a reasonable sense of job security, as long as my interest is engaged. When we consider that fixed, controlled records following recordkeeping principles and information governance are typical objectives in our programs, it’s not unreasonable to get a little scared by this.

Blockchain 62

Blockchain Records Management Unstructured data Artificial Intelligence 62