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

erwin

OCTOBER 8, 2020

And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. Companies everywhere are building innovative business applications to support their customers, partners and employees and are increasingly migrating from legacy to cloud environments.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

erwin

JANUARY 24, 2019

Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use. Governing metadata. Data lineage, impact analysis and business rules repositories.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

CIO Business Intelligence

DECEMBER 13, 2023

and applying and enriching metadata helps organizations take a big step toward innovating with generative AI. Balancing reward and risk Once in the shadows, unstructured data is pivotal in helping generative AI enable human creativity and problem-solving.

Unstructured Data 97

Unstructured Data IoT Metadata Manufacturing 97

erwin

APRIL 29, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (API)s, file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data. Who are the data owners?

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

erwin

NOVEMBER 6, 2020

To better explain our vision for automating data governance, let’s look at some of the different aspects of how the erwin Data Intelligence Suite (erwin DI) incorporates automation. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

CIO Business Intelligence

MAY 10, 2023

Access to artificial intelligence ( AI ) and the drive for adoption by organizations is more prevalent now than it’s ever been, yet many companies are struggling with how to manage data and the overall process. The first step in this process is to ensure the right technical and business metadata is in place.

Metadata 98

Metadata Data Governance Modeling Technology 98

Alation

SEPTEMBER 7, 2021

In this blog we will discuss how Alation helps minimize risk with active data governance. Governance influences how an organization’s objectives are set and achieved, how risk is monitored and addressed, and how performance is optimized. Organizations that run afoul of such laws risk damaging their reputation.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.) This allows for a high degree of transparency and auditability.

Cost-Benefit 79

Cost-Benefit Metadata Data Governance Modeling 79

erwin

JANUARY 17, 2020

Data models provide visualization, create additional metadata and standardize data design across the enterprise. Thanks to organizations like Amazon, Netflix and Uber, businesses have changed how they leverage their data and are transforming their business models to innovate – or risk becoming obsolete. SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

datapine

SEPTEMBER 29, 2022

Therefore, there are several roles that need to be filled, including: DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. These processes could include reports, campaigns, or financial documentation.

Data Quality 241

Data Quality Metrics Data-driven Management 241

erwin

SEPTEMBER 25, 2020

Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types. Data modeling is a critical component of metadata management , data governance and data intelligence. Reduce risks and costs. It has many benefits. Foster successful cloud adoption.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

Alation

DECEMBER 15, 2021

This uncovers actionable intelligence, maintains compliance with regulations, and mitigates risks. It creates the standards, processes, and documentation structures for how the organization will collect and manage data. This could include operational efficiency, minimising expenditure, risk mitigation, or revenue increase.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Octopai

APRIL 8, 2020

While there are many factors that led to this event, one critical dynamic was the inadequacy of the data architectures supporting banks and their risk management systems. Let’s examine how these processes failed, what regulations were put in place as a result, and what this means for business intelligence teams today.

Finance 52

Finance Cost-Benefit Metadata Data Architecture 52

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). End users access this data using third-party SQL clients and business intelligence tools.

Data Lake 102

Data Lake Data Warehouse Management Risk 102

Octopai

DECEMBER 10, 2019

Well, scoot over Templeton, because it’s also going to be the year of the Automated Business Glossary for business intelligence and data governance teams everywhere. One of the best ways to solve this issue is to create a business glossary. Automated Business Glossary Tools Streamline Processes.

Metadata 53

Metadata Data Warehouse Reporting Business Intelligence 53

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Smart Data Collective

JULY 27, 2021

Over the years, Snapshot has grown into the industry’s most comprehensive toolset for org management, relational data migration, documentation, technical debt removal, and more. As such, when it starts generating piles of technical debt, customers are at risk of system failure. What about SAP and Oracle? Visualize the problem.

Big Data 131

Big Data Snapshot IT Dashboards 131

Octopai

JUNE 1, 2020

Data mapping is the cornerstone of many important business intelligence processes: Data Integration – Even if systems are compatible, combining two disparate data repositories still requires meticulous data mapping. Octopai’s automated BI intelligence platform makes this process easy! Value of Data Mapping.

Metadata 52

Metadata Data Warehouse Data Integration Finance 52

erwin

APRIL 27, 2022

Read more about erwin® Data Intelligence by Quest. Extensive automation and collaboration functionality speed development cycles and minimize the risks of bugs and performance issues. Make sure your database developers have the data operations solutions they need to deliver applications that support business requirements.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

Jet Global

FEBRUARY 23, 2024

Because ETL tools allow the creation of reusable transformation logic, they’re able to automate the data transformation process, reducing the need for manual intervention and minimizing the risk of human error. Business applications use metadata and semantic rules to ensure seamless data transfer without loss.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

CIO Business Intelligence

DECEMBER 4, 2023

With constant advances in intelligent document processing, compute power, DevOps workflows, and AI, the content, context, and value of unstructured data is rapidly increasing. A modern ILM approach helps CIOs and their teams align processes to business objectives and regulatory requirements. Iron Mountain Capture/Enrich.

Unstructured Data 52

Unstructured Data Data Lake Metadata Business Objectives 52

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. All rights reserved.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

CIO Business Intelligence

JANUARY 30, 2024

“Always the gatekeepers of much of the data necessary for ESG reporting, CIOs are finding that companies are even more dependent on them,” says Nancy Mentesana, ESG executive director at Labrador US, a global communications firm focused on corporate disclosure documents. The complexity is at a much higher level.”

Reporting 107

Reporting Data Quality Strategy Data-driven 107

Alation

MARCH 21, 2023

What’s more, many companies struggle with rigid legacy technologies that increase the risk of a data breach. Otherwise, they risk a data privacy violation. So much is automatic — the metadata extraction, curation, labeling, query log ingestion, and building out the lineage — it’s a big help,” says Leonard Kowk, senior data analyst.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

CIO Business Intelligence

APRIL 7, 2022

KPMG calculates a score for an employee’s risk of attrition, tries to identify a reason for that, and then suggests a remediation. By analyzing messaging metadata, “not the messages themselves,” he says, “we can now statistically prove that certain types of communication behavior directly correlate to business performance.”.

Consulting 143

Consulting Management Interactive Metadata 143

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. How do we define “risk” and “value” in the context of data products, and how can we measure this?

Management 81

Management Risk Measurement Optimization 81

CIO Business Intelligence

APRIL 27, 2022

Rajgopal adds that all customer data, metadata, and escalation data are kept on Indian soil at all times in an ironclad environment. Perhaps most importantly, there is a rising need for enterprises and CIOs to explore whether a sovereign approach to the cloud is warranted and if so, to address it expediently to mitigate risk. “We

Digital Transformation 109

Digital Transformation Metadata Risk Management 109

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Cloudera

JANUARY 21, 2021

Of course you don’t want to re-create the risks and costs of data silos your organization has spent the last decade trying to eliminate. You also do not want to risk your company-wide cloud consumption costs snowballing out of control. But – you need those mission critical analytics services, and you need them now!

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

AWS Big Data

JANUARY 22, 2024

The majority of data produced by these accounts is used downstream for business intelligence (BI) purposes and in Amazon Athena , by hundreds of business users every day. Data as a product Treating data as a product entails three key components: the data itself, the metadata, and the associated code and infrastructure.

Data-driven 87

Data-driven Advertising Metadata Data Architecture 87

Jet Global

SEPTEMBER 22, 2023

In a rapidly evolving business environment, timely insights from data and the ability to react quickly to change are critical. Business intelligence is a key tool, empowering companies to get the most out of their data by providing tools to analyze information, streamline operations, track performance, and inform decision-making.

Visualization 52

Visualization Reporting Data Warehouse OLAP 52

Andrew White

MARCH 22, 2019

to weave together the governance and management of master data, application data, and less-widely shared data, and just enough enterprise metadata management. Don’t You Need to Understand Your Business Information Architecture? MDM is far from dead – but we need a modem approach to MDM (so we need a new name?)

Analytics 68

Analytics Metadata Data Governance Management 68

Grooper

OCTOBER 23, 2019

Articulating strategy in terms of business outcome is key. For example, business growth, profits, cost and performance optimization, efficiency, compliance, and risk management. The metrics we track are mainly related to tracking project efficiency; they don’t directly support overall business strategy. Governance.

Metrics 59

Metrics Measurement Strategy Optimization 59

Domino Data Lab

APRIL 3, 2019

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Allows metadata repositories to share and exchange.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Jet Global

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., Present your business case.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52