CIO Business Intelligence

APRIL 4, 2024

In total, it took the CIO’s team and agency a little over two years to convert 160 million documents into a transformed, revamped, and people-centric system, built on the Salesforce CRM, that tells their stories and focuses on people outcomes, not case outcomes.

Digital Transformation 110

Digital Transformation Data Lake ROI Modeling 110

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 98

Data Governance Unstructured Data Metadata Data Lake 98

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

Smart Data Collective

AUGUST 11, 2022

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence. Conclusion.

Data Quality 125

Data Quality Machine Learning Digital Transformation Big Data 125

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. This calls for additional planning, documentation, and testing.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

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. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality.

Digital Transformation 134

Digital Transformation IT Data-driven Metrics 134

Ontotext

MARCH 5, 2024

The complete documentation of this feature is available on the JavaScript functions page of the GraphDB documentation, and I wanted to demonstrate some fun that I had trying this out. When used in a CONSTRUCT query or an INSERT update request, the terms returned by your JavaScript functions can be used to enhance data quality.

Data Quality 52

Data Quality Testing Visualization Management 52

CIO Business Intelligence

APRIL 1, 2024

Capturing the “as-is” state of your environment, you’ll develop topology diagrams and document information on your technical systems. Ensure that data is cleansed, consistent, and centrally stored, ideally in a data lake. Data preparation, including anonymizing, labeling, and normalizing data across sources, is key.

Data Lake 123

Data Lake Uncertainty Consulting Risk 123

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.

Key Performance Indicator 111

Key Performance Indicator Data Governance Metadata Data Quality 111

Grooper

APRIL 9, 2020

The heart of digital transformation is data, and organizations without a clear strategy for data curation and enrichment from documents are at risk of failed transformation and missing out on massive revenue opportunities. The way we’ve thought about curating data from documents over the years is fundamentally flawed.

Digital Transformation 65

Digital Transformation Data Quality Enterprise Risk 65

DataRobot Blog

MAY 10, 2022

The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. The first step would be to make sure that the data used at the beginning of the model development process is thoroughly vetted, so that it is appropriate for the use case at hand.

Risk 64

Risk Modeling Machine Learning Data Quality 64

AWS Big Data

NOVEMBER 9, 2023

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. This popular open-source tool for data warehouse transformations won out over other ETL tools for several reasons. The tool also offered desirable out-of-the-box features like data lineage, documentation, and unit testing.

Data Warehouse 107

Data Warehouse Testing Data Quality Reporting 107

IBM Big Data Hub

NOVEMBER 24, 2023

Collectively, discovery and design is where significant time is spent during modernization, whereas development is much easier once the team has decoded the legacy application functionality, integration aspects, logic and data complexity. Ingest BIAN details and understand BIAN Service Landscape.

Cost-Benefit 101

Cost-Benefit Testing Data-driven Enterprise 101

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Data Quality Metadata Data Governance 52

CIO Business Intelligence

MARCH 14, 2024

AI has already found its way into many government departments, with the most common use of AI identified by the NAO being to support operational decision-making through automated document analysis, digital assistant functionality, and image recognition.

Testing 103

Testing Risk Digital Transformation Statistics 103

CIO Business Intelligence

APRIL 8, 2024

Digital Athlete draws data from players’ radio frequency identification (RFID) tags, 38 5K optical tracking cameras placed around the field capturing 60 frames per second, and other data such as weather, equipment, and play type. million video frames and documents about 100 million locations and positions of players on the field.

Analytics 97

Analytics Broadcasting Predictive Analytics Metrics 97

Octopai

MAY 15, 2023

Data lineage helps identify redundant or inefficient data integration steps, allowing organizations to streamline their processes and reduce development, maintenance, and operational costs associated with ETL. Data quality costs: Poor data quality can result in significant costs for organizations.

Data Quality 52

Data Quality Data Governance Data Integration Risk 52

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

Rocket-Powered Data Science

FEBRUARY 15, 2023

Since ChatGPT is built from large language models that are trained against massive data sets (mostly business documents, internal text repositories, and similar resources) within your organization, consequently attention must be given to the stability, accessibility, and reliability of those resources.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Implementing a data catalog first will make MDM more successful.

Management 52

Management Data Quality Metadata Testing 52

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 140

Digital Transformation Unstructured Data Machine Learning Risk Management 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

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 77

Data Quality Data-driven Data Lake Metrics 77

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

IBM Big Data Hub

FEBRUARY 9, 2023

Once you have a clear understanding of business objectives and data needs, set data governance goals and priorities. An effective and efficient data governance program will support your organization’s growth and competitive advantage. So where are you in your data governance journey?

Data Governance 83

Data Governance Business Objectives Strategy Data Quality 83

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

CDW Research Hub

JANUARY 27, 2020

Document the existing solution. List databases and data objects that need to be migrated. Define known data quality issues to be addressed. List processes and tools that populate and pull data from the existing solution. Document the existing solution into an “as-is” architecture diagram.

Data Warehouse 52

Data Warehouse Testing Data Quality IT 52

TDAN

AUGUST 3, 2021

Database standards are common practices and procedures that are documented and […]. Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard.

Data Quality 116

Data Quality Data Architecture Modeling Management 116

Alation

FEBRUARY 14, 2023

What % of data is or isn’t assigned to a domain? What % of columns have data quality rules implemented? Does the top 5% of data used in the organization have data owners, stewards, titles, definitions, and classifications? How many business processes are documented? What % of tables have a definition?

ROI 52

ROI Data Governance Metrics Cost-Benefit 52

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

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 Digital Transformation Cost-Benefit 145

Timo Elliott

DECEMBER 14, 2023

Large language models are good at figuring out what we meant, and the principle applies to many real-world data problems. For example, machine learning is already used to extract information from documents such as invoices: the date, amount, supplier ID etc.

Machine Learning 64

Machine Learning IoT Statistics Modeling 64

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? Risk, however, is a different challenge.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

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 123

Metadata IT Data-driven Data Quality 123

CIO Business Intelligence

MARCH 24, 2023

Overcoming data challenges Despite their growing commitment to ESG, financial firms have learned the path to sustainability and prosperity can be rocky. “ESG ESG data quality is the biggest challenge. revenue growth from businesses showing a lower commitment to ESG.

Risk Management 52

Risk Management Metrics Consulting Data Quality 52

Alation

OCTOBER 5, 2021

It’s on Data Governance Leaders to identify the issues with the business process that causes users to act in these ways. Inconsistencies in expectations can create enormous negative issues regarding data quality and governance. Establish a data governance program that drives business value by aligning team roles to KPIs.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96