CIO Business Intelligence

JULY 10, 2024

We examine the risks of rapid GenAI implementation and explain how to manage it. These examples underscore the severe risks of data spills, brand damage, and legal issues that arise from the “move fast and break things” mentality. Effective partnering requires transparency and clear documentation from vendors.

Risk 111

Risk Strategy Consulting Cost-Benefit 111

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. And there are tools for archiving and indexing prompts for reuse, vector databases for retrieving documents that an AI can use to answer a question, and much more. Only 4% pointed to lower head counts.

Enterprise 339

Enterprise Testing Modeling Reporting 339

datapine

APRIL 15, 2020

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. But before production, you need to develop documentation, test driven design (TDD), and implement these important steps: Actively involve key stakeholders once again.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

O'Reilly on Data

MARCH 24, 2020

The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

CIO Business Intelligence

DECEMBER 11, 2024

The field of data observability has experienced substantial growth recently, offering numerous commercial tools on the market or the option to build a DIY solution using open-source components. Data governance needs to follow a similar path, transitioning from policy documents and confluence pages to data policy as code.

Management 111

Management Data Governance Data Science Reporting 111

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 82

Data Quality Data-driven Data Lake Metrics 82

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

CIO Business Intelligence

JANUARY 30, 2025

These changes can expose businesses to risks and vulnerabilities such as security breaches, data privacy issues and harm to the companys reputation. It also includes managing the risks, quality and accountability of AI systems and their outcomes. Organizations need to have a data governance policy in place.

Testing 93

Testing Metrics Cost-Benefit Modeling 93

Rocket-Powered Data Science

FEBRUARY 15, 2023

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens). In short, you must be willing and able to answer the seven WWWWWH questions (Who?

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

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

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Data-related decisions, processes, and controls subject to data governance must be auditable.

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

APRIL 16, 2024

With the emergence of GenAI capabilities, fast-tracking digital transformation deployments are likely to change manufacturing as we know it, creating an expanding chasm of leaders versus followers, the latter of which will risk obsolescence. However, despite the benefits of GenAI, there are some areas of risk. Data quality dependence.

Manufacturing 142

Manufacturing Digital Transformation Risk Dashboards 142

erwin

APRIL 29, 2020

In the event of a change in data expectations, data lineage provides a way to determine which downstream applications and processes are affected by the change and helps in planning for application updates. Business terms and data policies should be implemented through standardized and documented business rules.

Metadata 111

Metadata Key Performance Indicator Data Governance Data Quality 111

CIO Business Intelligence

MAY 2, 2023

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. The risk of derailments increases as I hear inconsistent answers or too many conflicting priorities.

Digital Transformation 141

Digital Transformation IT Data-driven Data Quality 141

erwin

FEBRUARY 13, 2020

The numerous data types and data sources that exist today weren’t designed to work together, and data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. Finding metadata, “the data about the data,” isn’t easy.

Metadata 135

Metadata Data Governance Management Cost-Benefit 135

CIO Business Intelligence

SEPTEMBER 17, 2024

All this while CIOs are under increased pressure to deliver more competitive capabilities, reduce security risks, connect AI with enterprise data, and automate more workflows — all areas where architecture disciplines have a direct role in influencing outcomes.

Enterprise 145

Enterprise Digital Transformation Cost-Benefit Risk 145

erwin

SEPTEMBER 17, 2020

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with auto generated and meaningful documentation of the mappings, is a powerful way to support overall data governance. Data quality is crucial to every organization.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

erwin

JANUARY 30, 2020

The results of our new research show that organizations are still trying to master data governance, including adjusting their strategies to address changing priorities and overcoming challenges related to data discovery, preparation, quality and traceability. Top Five: Benefits of An Automation Framework for Data Governance.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

erwin

MARCH 25, 2021

Risk Management and Regulatory Compliance. Risk management, specifically around regulatory compliance, is an important use case to demonstrate the true value of data governance. According to Pörschmann, risk management asks two main questions. Strengthen data security. How likely is a specific event to happen? “You

Data Governance 109

Data Governance Cost-Benefit IT Risk 109

erwin

JULY 12, 2019

Modern, strategic data governance , which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. Strengthen data security.

Data Governance 110

Data Governance Management Metadata Risk Management 110

erwin

FEBRUARY 19, 2021

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage). Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

CIO Business Intelligence

DECEMBER 19, 2023

Create these six generative AI workstreams CIOs should document their AI strategy for delivering short-term productivity improvements while planning visionary impacts. These workstreams require documenting a vision, assigning leaders, and empowering teams to experiment.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. They can govern the implementation with a documented business case and be responsible for changes in scope. On the flip side, document everything that isn’t working. Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

CIO Business Intelligence

SEPTEMBER 12, 2024

At Gartner’s London Data and Analytics Summit earlier this year, Senior Principal Analyst Wilco Van Ginkel predicted that at least 30% of genAI projects would be abandoned after proof of concept through 2025, with poor data quality listed as one of the primary reasons.

Unstructured Data 111

Unstructured Data Data Quality Enterprise Data Governance 111

CIO Business Intelligence

AUGUST 15, 2024

Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. CIOs should first launch internal projects with low public-facing exposure , which can mitigate risk and provide a controlled environment for experimentation.

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

CIO Business Intelligence

MAY 15, 2024

“If you have a data center that happens to have capacity, why pay someone else?” According to Synopsys’ open source security and risk analysis released in February, 96% of all commercial code bases contained open source components. It also focuses largely on risk and governance issues. But that might not always be the case.

Modeling 137

Modeling Risk Software Enterprise 137

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 89

Metadata Risk Management Business Intelligence Data Governance 89

CIO Business Intelligence

JANUARY 23, 2024

Quality metrics can be used to measure the improvements that come from reducing defects, lowering the impacts of human errors, improving data quality, and other program outcomes that illustrate how increasing quality connects to business impact.

Measurement 141

Measurement Digital Transformation KPI Metrics 141

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. For a credit risk model, the target could be defined as “fully repays loan” or “payments in first 2 years are current” or or “collateral is repossessed.”. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

CIO Business Intelligence

JANUARY 9, 2024

He and his team have created information decks, documents, and presentations that describe the various types of AI and how they can be used and explain how and where AI and machine learning may be useful — and why it’s not the solution to all the problems they have. What is our appetite for risk and how do we address it?

IT 140

IT Risk Cost-Benefit Machine Learning 140

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Smart Data Collective

JULY 15, 2021

It helps you locate and discover data that fit your search criteria. With data catalogs, you won’t have to waste time looking for information you think you have. What Does a Data Catalog Do? Advanced data catalogs can update metadata based on the data’s origins. How Does a Data Catalog Impact Employees?

Metadata 133

Metadata IT Data-driven Data Quality 133

erwin

OCTOBER 24, 2019

Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration. With erwin, organizations can: 1.

Metadata 104

Metadata Management Data-driven Data Architecture 104

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. It uncovered a number of obstacles that organizations have to overcome to improve their data operations. Overcoming Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

AWS Big Data

FEBRUARY 29, 2024

Data governance is a critical building block across all these approaches, and we see two emerging areas of focus. First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses.

Data Governance 109

Data Governance Unstructured Data Metadata Data Lake 109

Alation

SEPTEMBER 7, 2021

In this blog we will discuss how Alation helps minimize risk with active data governance. Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59