FineReport

APRIL 9, 2024

From advanced analytics to predictive modeling, the evolving landscape of business intelligence is revolutionizing how data is processed and leveraged for actionable insights. Comparing leading BI tools provides valuable insights into the diverse capabilities and features available for data analysis and reporting.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8]. Currency amounts reported in Taiwan dollars.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Ontotext

JULY 6, 2023

Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, Connected Inventory Ontotext’s Connected Inventory integrates data from various sources, which enables efficient reporting.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Sisense

SEPTEMBER 3, 2020

Besides strong technical skills (for instance, use of Hadoop, programming in R and Python , math, statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. Data Analyst. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

O'Reilly on Data

JULY 15, 2019

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. However, because most institutions lack a modern data architecture , they struggle to manage, integrate and analyze financial data at pace.

Data Architecture 84

Data Architecture Risk Management Risk Management 84

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. So what’s holding organizations back from fully using their data to make better, smarter business decisions? Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Cloudera

SEPTEMBER 7, 2023

This puts the onus on institutions to implement robust data encryption standards, process sensitive data locally, automate auditing, and negotiate clear ownership clauses in their service agreements. But these measures alone may not be sufficient to protect proprietary information. AI-ify risk management.

Insurance 81

Insurance Risk Data-driven Finance 81

Octopai

JULY 12, 2020

An enormous amount of time was being wasted performing manual searches, as the BI team needed to frequently comb through the enterprise data warehouse’s fields to determine how each was calculated or to find their sources. We now get the benefit of proliferated data quality.” – Andrew Stewardson, Data Risk Manager, FCSA.

Data Warehouse 58

Data Warehouse Data Quality Metadata Risk Management 58

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

FEBRUARY 22, 2022

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data. It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

datapine

AUGUST 29, 2022

to describe how companies can combine traditional analytics with a big data approach. He recognizes big online companies like Google or Facebook as the originators of the top big data tools and technologies, as well as data-driven management reporting and best practices.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

AWS Big Data

AUGUST 14, 2023

When are data products deprecated, and who is accountable for the consequences to their consumers? How do we define “risk” and “value” in the context of data products, and how can we measure this? Whose responsibility is it to justify the existence of a given data product?

Management 79

Management Risk Measurement Optimization 79

Andrew White

JANUARY 11, 2021

What is unique about the D&A Leadership Vision is that it crossed over into business since for many organizations, the CDO reports into the CEO or COO (as examples). The fill report is here: Leadership Vision for 2021: Data and Analytics. Value Management or monetization. Product Management. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52