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 128

Digital Transformation Strategy Data-driven Data Strategy 128

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. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Data augmentation.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

SEPTEMBER 2, 2022

In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Director of software engineering.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Director of software engineering.

Finance 98

Finance IT Software Reporting 98

Octopai

JUNE 19, 2023

For business users Data Catalogs offer a number of benefits such as better decision-making; data catalogs provide business users with quick and easy access to high-quality data. This availability of accurate and timely data enables business users to make informed decisions, improving overall business strategies.

Metadata 88

Metadata Data Governance Measurement Risk Management 88

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

Alation

AUGUST 17, 2022

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? Enterprise risk management.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. However, different types of data need to be treated differently.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

FineReport

MARCH 24, 2024

They analyze, interpret, and manipulate complex data, track key performance indicators, and present insights to management through reports and visualizations. Data analysts interpret data using statistical techniques, develop databases and data collection systems, and identify process improvement opportunities.

Statistics 52

Statistics Data mining Visualization Reporting 52

Ontotext

JULY 6, 2023

By promptly identifying and addressing risks, it enhances operational resiliency and enables proactive risk management. The solution also reduces incident response times, optimizes processes and streamlines asset management. First of all, it helps bridge the gap between business abstracts and technical realities.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

IBM Big Data Hub

SEPTEMBER 15, 2023

Key challenges in meeting Sustainability disclosure requirements For most companies today, meeting this wide range of requirements will force accelerating the maturity level of ESG data quality, data management, and governance controls, as well as streamlining the ESG reporting processes to ensure auditability.

Risk 75

Risk Reporting Data-driven Publishing 75

Smart Data Collective

SEPTEMBER 25, 2019

Indeed, encryption alone does not guarantee this, but it’s something you can and should use as part of an overall strategy. If you trust the data, it’s easier to use confidently to make business decisions. Slightly less (42%) mentioned having a limited encryption strategy used for certain applications or types of data.

Technology 85

Technology Statistics Strategy Insurance 85

O'Reilly on Data

JULY 15, 2019

A look at how guidelines from regulated industries can help shape your ML strategy. 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.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

DataRobot Blog

MAY 26, 2022

In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Figure 6: Model lift chart showing model predictions against actual outcomes, sorted by increasing predicted value. Conclusion.

Risk 52

Risk Modeling Metrics Business Objectives 52

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 78

Insurance Risk Data-driven Finance 78

CIO Business Intelligence

AUGUST 7, 2023

According to Rocket Software’s survey: Only 33% of respondents are extremely confident that they have the right technology/software in place to execute an effective approach to IT risk management. Only 28% of respondents are extremely confident they have the right people in place to execute an effective approach to IT risk management.

Risk 97

Risk IT Management Risk Management 97

TDAN

JUNE 2, 2020

As I am writing this, countries around the world are fighting a global pandemic. This is the first global pandemic of the modern information age. There have been a number of epidemics in recent years, such as Ebola, but they (thankfully) have been regional in nature. This time around, we have the power of AI, […].

Risk Management 85

Risk Management Data Quality Risk Data Strategy 85

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. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. Identify a consumption strategy. Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

erwin

OCTOBER 24, 2019

Addressing the Complexities of Metadata Management. The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires. With erwin, organizations can: 1.

Metadata 104

Metadata Management Data-driven Data Architecture 104

datapine

FEBRUARY 22, 2022

1) What Is A Business Intelligence Strategy? 2) BI Strategy Benefits. 4) How To Create A Business Intelligence Strategy. Over the past 5 years, big data and BI became more than just data science buzzwords. Your Chance: Want to build a successful BI strategy today? What Is A Business Intelligence Strategy?

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

IBM Big Data Hub

DECEMBER 4, 2023

With careful planning and solid organizational strategy, businesses of any size can take advantage of Oracle’s unique cloud solutions , reducing costs and streamlining operations. Clearly define the objective of the implementation project and determine its scope, timeline and budget as well as create a risk management plan.

Testing 75

Testing Consulting Digital Transformation Cost-Benefit 75

DataRobot Blog

DECEMBER 20, 2022

For example, just to name the business of asset managers is generally dependent on these (but not limited to) four fundamentals : Accurately estimating the current asset’s price and rent. Automating and optimizing their investment strategy. You can understand the data and model’s behavior at any time. Conclusions.

Management 59

Management Machine Learning Optimization Modeling 59

datapine

AUGUST 29, 2022

At 156 pages on Kindle, this is a book you could finish in one (long) sitting if you were so inclined, and that you can also use as an inspiration when you work on your business intelligence strategy. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Alation

NOVEMBER 23, 2021

Organizations don’t need to spend a lot of money to get data governance. They can improve data quality, security and risk management without the need for an expensive big-bang project. What organizations actually govern is data-consumer behavior, and not the data itself. Creates Shared Processes.

Data Governance 52

Data Governance Data-driven Data Processing Consulting 52

AWS Big Data

AUGUST 14, 2023

At the same time, unstructured approaches to data mesh management that don’t have a vision for what types of products should exist and how to ensure they are developed are at high risk of creating the same effect through simple neglect. Examples may include associated revenue, savings, or reductions in operational losses.

Management 81

Management Risk Measurement Optimization 81

Andrew White

JANUARY 11, 2021

Do you recommend a consulting approach strategy rather than a CDO strategy? How do you think Technology Business Management plays into this strategy? Where does the Data Architect role fits in the Operational Model ? Assuming a data architect helps model and guide and assist D&A then they play a key role.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

MAY 9, 2024

ETL pipelines are commonly used in data warehousing and business intelligence environments, where data from multiple sources needs to be integrated, transformed, and stored for analysis and reporting. For example, retail companies can monitor sales transactions as they occur to optimize inventory management and pricing strategies.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52