CIO Business Intelligence

MARCH 18, 2024

We are proceeding cautiously because the rise of LLMs [large language models] presents a new level of data security risk,” he says. “We We have been developing our own internal AI capability over the last few years using open-source models. A key focus of the bank’s AI team is likewise data quality.

Risk 130

Risk Cost-Benefit Data Processing Testing 130

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. The reality of LLMs and other “narrow” AI technologies is that none of them is turn-key.

Insurance 81

Insurance Risk Data-driven Finance 81

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

DataRobot Blog

JULY 21, 2022

Document assumptions and risks to develop a risk management strategy. Machine learning models created in silos are rarely implemented. Evaluate the computing resources and development environment that the data science team will need. Models could also be deployed into multiple environments at once.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Cloudera

JULY 15, 2021

For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Now, risk management has become exponentially complicated in multiple dimensions. . Attendees included senior risk managers and analytics experts from financial institutions and insurance companies.

Risk 99

Risk Optimization Management Insurance 99

FineReport

APRIL 9, 2024

Evolving BI Tools in 2024 Significance of Business Intelligence In 2024, the role of business intelligence software tools is more crucial than ever, with businesses increasingly relying on data analysis for informed decision-making. This resulted in increased profitability and strengthened competitive positioning within the industry.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

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

erwin

OCTOBER 24, 2019

. • Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards. Analyzing metadata – Understand how data relates to the business and what attributes it has. Addressing the Complexities of Metadata Management.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Ontotext

SEPTEMBER 1, 2023

Terms related to GenAI such as hallucinations and Large Language Models (LLMs) have become lingua-franca for any and every business conversation. Generative AI refers to computational models that are trained on massive amounts of text data and output in the form of text, images, video, audio, new data, and even code.

Risk 52

Risk Modeling Data Quality Data Governance 52

DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. They all serve to answer the question, “How well can my model make predictions based on data?” So, we ask, what recommendations and assessments can you use to verify the origin and quality of the data used? How large is the data set?

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

IBM Big Data Hub

AUGUST 15, 2022

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. Model risk management.

Data mining 60

Data mining Risk Modeling Risk Management 60

Alation

MARCH 14, 2023

Govern Data governance models should be flexible and dynamic while proactively addressing risk management and compliance with local and global regulations. It offers numerous features and benefits which will allow organisations to readily and effectively implement a future-proof data strategy.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

erwin

APRIL 4, 2019

Some other common data governance obstacles include: Questions about where to begin and how to prioritize which data streams to govern first. Issues regarding data quality and ownership. Concerns about data lineage. Competing project and resources (time, people and funding).

Data Governance 75

Data Governance Data Quality Risk Management Data-driven 75

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

Analytics 90

Analytics Insurance Big Data Uncertainty 90

Octopai

JUNE 19, 2023

Everyone has access to the same data and the same understanding of what the data represents, reducing miscommunications and discrepancies. Catalogs also allow for better Risk Management; data catalogs help businesses maintain regulatory compliance by providing a clear record of what data is stored and how it’s used.

Metadata 88

Metadata Data Governance Measurement Risk Management 88

Ontotext

APRIL 21, 2021

The problem was the left hand had no way of knowing the systemic issues around data governance, risk management and compliance framework. If a policy can be understood, it can be modelled in RDF , the language used by Ontotext GraphDB. This is literally a case of the left hand not knowing what the right hand was doing.

Metadata 59

Metadata Finance Cost-Benefit Data Quality 59

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

CIO Business Intelligence

SEPTEMBER 2, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

APRIL 29, 2022

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.

Digital Transformation 121

Digital Transformation Strategy Data-driven Data Strategy 121

DataRobot Blog

DECEMBER 20, 2022

Yet, in the digital transformation era, the pricing and assessment of real estate assets is more difficult than described by brokers’ presentations, valuation reports, and traditional analytical approaches like hedonic models. Datarobot enables users to easily combine multiple datasets into a single training dataset for AI modeling.

Management 59

Management Machine Learning Optimization Modeling 59

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Image by Ben Lorica.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

FineReport

MARCH 24, 2024

They also develop and support reporting processes, ensure data quality, and collaborate with internal and external stakeholders to understand data content and gather business requirements. Python: Known for its text data handling capabilities and compatibility with various platforms and databases. JPMorgan Chase & Co.:

Statistics 52

Statistics Data mining Visualization Reporting 52

AWS Big Data

AUGUST 14, 2023

Taken together, the posts in this series lay out some possible operating models for data mesh within an organization. In the same way, organizations seeking to implement successful data mesh strategies must respect the nature and structure (legal, political, commercial, technology) of their organizations in their implementation.

Management 81

Management Risk Measurement Optimization 81

datapine

AUGUST 29, 2022

Data Smart’ contains enough practical knowledge to actually start performing analyses by using good old Microsoft Excel. The author then proceeds to highlight the most important steps of the process model, such as sampling, treatment of missing values, and variable selection.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Cloudera

JANUARY 22, 2019

Cloudera Enterprise is a one-stop shop for running analytics models and algorithms against multiple data sources across on-premises and cloud, and sometimes real-time data sources. Shared Data Catalog to preserve context and enforce Governance.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

Domino Data Lab

FEBRUARY 4, 2019

Lately there’s been lots of dialog about bias in machine learning models, though considering the noise part of the equation is an important corollary. We’d love to hear your insights and perspectives about running data teams. The ability to measure results (risk-reducing evidence). Lack of data, or data quality issues (silos).

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Andrew White

JANUARY 11, 2021

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. Value Management or monetization. Product Management. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52