CIO Business Intelligence

DECEMBER 10, 2024

Instead, CIOs must partner with CMOs and other business leaders to help quantify where gen AI can drive other strategic impacts especially those directly connected to the bottom line. Improving data quality and integrating new data sources to enrich customer and prospect data are vital for applying AI in marketing and sales.

Sales 143

Sales Metrics Data-driven Measurement 143

CIO Business Intelligence

MARCH 19, 2025

Similarly, Deloittes 2024 CxO Survey highlights that while CDOs prioritize AI and business efficiency, sustainability remains a secondary focus. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

IT 59

IT Data Governance Data-driven Metrics 59

Alation

MARCH 14, 2023

Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence. What is a data strategy? Why is a data strategy important?

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Cloudera

JULY 29, 2022

Similarly, data should be treated as a corporate asset with a dedicated long-term strategy that lets the organization store, manage, and utilize its data effectively. In an industry that is subject to stringent regulatory requirements, it is critical to use data to accurately scale up risk management.

Data Architecture 89

Data Architecture Data Strategy Risk Management Strategy 89

CIO Business Intelligence

JULY 5, 2024

Here are five best practices to get the most business benefit from gen AI. Set your holistic gen AI strategy Defining a gen AI strategy should connect into a broader approach to AI, automation, and data management. Define which strategic themes relate to your business model, processes, products, and services.

ROI 125

ROI Experimentation Risk Cost-Benefit 125

Cloudera

NOVEMBER 25, 2021

So, what are the common user cases we are seeing for enterprise data clouds? Protect: security needs including risk management, fraud detection and cybersecurity initiatives through risk modelling and analysis, regulatory compliance, and financial crime prevention. . About the author: .

Machine Learning 90

Machine Learning IoT Risk Business Objectives 90

Ontotext

SEPTEMBER 1, 2023

Otherwise, they are like a black box, where very little is known as to how they arrive at answers and responses and organizations can lose control of private data, GenAI pipelines can get compromised, or applications can be attacked in subtle ways by hackers.

Risk 52

Risk Modeling Data Quality Data Governance 52