CIO Business Intelligence

SEPTEMBER 2, 2022

The average salary for a financial software engineer is $116,670 per year, with a reported salary range of $85,000 to $177,000 per year, according to data from Glassdoor. You’ll also be expected to stay on top of latest tech trends, work closely with product managers, and assist in building cloud-based solutions for financial clients.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

The average salary for a financial software engineer is $116,670 per year, with a reported salary range of $85,000 to $177,000 per year, according to data from Glassdoor. You’ll also be expected to stay on top of latest tech trends, work closely with product managers, and assist in building cloud-based solutions for financial clients.

Finance 98

Finance IT Software Reporting 98

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

Ontotext

APRIL 21, 2021

Ontotext clients have used enterprise knowledge graphs to ensure inventory control is done in an intelligent and automated way. Inventory here refers to the vast amounts of information and data that financial institutions need on a daily basis, which are core assets to the business. Knowledge Graphs Make the Impossible, Simple.

Metadata 59

Metadata Finance Cost-Benefit Data Quality 59

erwin

SEPTEMBER 17, 2020

Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. The importance of end-to-end data lineage is widely understood and ignoring it is risky business. Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Octopai

JANUARY 19, 2020

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks.

Metadata 61

Metadata Risk Management Business Intelligence Data Governance 61

CIO Business Intelligence

APRIL 29, 2022

The data-first transformation journey can appear to be a lengthy one, but it’s possible to break it down into steps that are easier to digest and can help speed you along the pathway to achieving a modern, data-first organization. Key features of data-first leaders. 5x more likely to be highly resilient in terms of data loss.

Digital Transformation 116

Digital Transformation Strategy Data-driven Data Strategy 116

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 37

Machine Learning Management Enterprise Risk Management 37

Ontotext

JULY 6, 2023

In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. Transaction and pricing data (e.g., Let’s consider an example about risk and opportunity event detection. Signals from unstructured content (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

FineReport

MARCH 24, 2024

The primary objective of a data analyst is to transform raw data into meaningful insights that drive organizational improvements. By conducting extensive research and analysis, they generate reports that inform strategic decisions, identify areas for enhancement, and guide the implementation of new initiatives.

Statistics 52

Statistics Data mining Visualization Reporting 52

Alation

AUGUST 17, 2022

And while IT should never be the data owner for business data, research from Digital Trailblazer author and former CIO Isaac Sacolick shows that 40 percent of business leaders “just want IT to fix the data and deliver reports.” It comes down to the question: What is the value of your data? Data management.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

Sisense

SEPTEMBER 3, 2020

As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. They are the link between the data resources available to an enterprise and executives looking for opportunities to make the business better, faster, and stronger. See an example: Explore Dashboard. Database Specialist.

Statistics 97

Statistics Metrics Visualization Finance 97

Alation

JUNE 14, 2021

Do we know the business outcomes tied to data risk management? Once you have data classification then you can talk about whether you need to tokenize and why, or anonymize and why, or encrypt and why, etc.” Intelligent systems powered by machine learning are necessary for overcoming the challenges of data management.

Data Governance 97

Data Governance Recreation/Entertainment Data Lake Digital Transformation 97

Smart Data Collective

SEPTEMBER 25, 2019

However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely. If you trust the data, it’s easier to use confidently to make business decisions. Only 13% said they did not have a strategy at all.

Technology 84

Technology Statistics Strategy Insurance 84

erwin

FEBRUARY 4, 2021

While acknowledging that data governance is about more than risk management and regulatory compliance may indicate that companies are more confident in their data, the data governance practice is nonetheless growing in complexity because of more: Data to handle, much of it unstructured. Sources, like IoT.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

IBM Big Data Hub

DECEMBER 4, 2023

By implementing Oracle , one of the world’s leading enterprise resource planning (ERP) tools, organizations can transform their business processes and significantly increase operational efficiency. Companies large and small are increasingly digitizing and managing vast troves of data.

Testing 73

Testing Consulting Digital Transformation Cost-Benefit 73

Cloudera

SEPTEMBER 7, 2023

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

Insurance 84

Insurance Risk Data-driven Finance 84

Octopai

JULY 12, 2020

Like many BI teams, FCSA’s struggles began with the massive amount of data that existed in their enterprise data warehouse. Perhaps most frustrating for FCSA was the major data-quality issues that arose due to manual, error-prone data processing. Think of the wonders it could work for your enterprise.

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

AUGUST 29, 2022

However, once you start working with larger enterprise-level data sets with millions of rows and hundreds of columns of information, Excel may not be capable of handling such volumes. 9) Big Data For Dummies by Judith Hurwitz, Alan Nugent, Dr. The author also introduces the concept of “analytics 3.0”

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Find out what is working, as you don’t want to totally scrap an already essential report or process. What data analysis questions are you unable to currently answer? Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

AWS Big Data

AUGUST 14, 2023

DPPM does not exist in a vacuum—by its nature, DPPM is closely related to and interdependent with enterprise architecture practices like business capability management and project portfolio management. DPPM itself may therefore also be considered, in part, an enterprise architecture practice.

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. What is your vision for D&A for small and medium enterprises? Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52