CIO Business Intelligence

MARCH 24, 2023

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.” Such a framework provides your organization with a holistic approach to collecting, managing, securing, and storing data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 95

Data Governance Unstructured Data Metadata Data Lake 95

CIO Business Intelligence

APRIL 8, 2024

Working with partner Amazon Web Services (AWS), the NFL has developed Digital Athlete, a platform that uses computer vision and ML to predict which players are at the highest risk of injury based on plays and their body positions. million video frames and documents about 100 million locations and positions of players on the field.

Analytics 96

Analytics Broadcasting Predictive Analytics Metrics 96

CIO Business Intelligence

APRIL 4, 2024

This involves rigorous evaluation of potential benefits, risks, and costs associated with each AI initiative to ensure investments are prudent and aligned with our risk-return profile.” Webster Bank is following a similar strategy. Data is the lynchpin to AI success,” says Nafde. Diasio agrees. asks Srivastava.

Enterprise 128

Enterprise Cost-Benefit Forecasting Strategy 128

Octopai

MAY 15, 2023

Here are some common cost areas where data lineage can be beneficial: Infrastructure and storage costs: Data lineage allows organizations to understand data usage patterns, access frequencies, and data dependencies. Data quality costs: Poor data quality can result in significant costs for organizations.

Data Quality 52

Data Quality Data Governance Data Integration Risk 52

IBM Big Data Hub

NOVEMBER 24, 2023

Application modernization starts with assessment of current legacy applications, data and infrastructure and applying the right modernization strategy (rehost, re-platform, refactor or rebuild) to achieve the desired result. Subsequent phases are build and test and deploy to production.

Cost-Benefit 99

Cost-Benefit Testing Data-driven Enterprise 99

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 121

IT Risk Cost-Benefit Enterprise 121

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. In all enterprise organizations, we've seen a real broadening of data ecosystems.

Digital Transformation 65

Digital Transformation Data Quality Enterprise Risk 65

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

Alation

AUGUST 17, 2022

We’ve seen the emergence of new, less invasive approaches, yet the question remains: What do CIOs think is most required to deliver a successful data governance program? The Risks of Early Data Governance Programs. The biggest risk was a lack of ownership. If your definitions are bad, so is your governance/risk/security.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

Alation

JULY 15, 2022

I recently led an online session, Data Monetisation and Governance , looking at the evolution of data governance , defining data ethics (from the Turing Institute ), and touching on the balancing act between using data to monetise (by increasing revenue, decreasing spend, or mitigating risk) and meeting ethical obligations.

Cost-Benefit 89

Cost-Benefit Data Governance ROI Risk 89

erwin

JANUARY 30, 2020

Constructing a Digital Transformation Strategy: How Data Drives Digital. I’m encouraged by these results as it tells us that enterprises are really beginning to embrace the power of data to shape their organizations. And close to 50 percent have deployed data catalogs and business glossaries.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

erwin

JULY 12, 2019

So it’s important to understand how to use strategic data governance to manage the complexity of regulatory compliance and other business objectives … Designing and Operationalizing Regulatory Compliance Strategy. Strengthen data security. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

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

CIO Business Intelligence

MARCH 7, 2024

In my recent column, I delved into the challenges enterprises face in integrating AI into the workplace and outlined strategies for CISOs to monitor or control the use of AI effectively. The benefits of AI for some industries will drive major strategy changes, and the impact will be vast. There is also the concern about using data.

Digital Transformation 72

Digital Transformation Machine Learning Risk Testing 72

datapine

JANUARY 24, 2021

It is not just important to gather all the existing information, but to consider the preparation of data and utilize it in the proper way, has become an indispensable value in developing a successful business strategy. That being said, it seems like we’re in the midst of a data analysis crisis.

IT 317

IT Statistics KPI Data-driven 317

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 Metadata Data Quality Data Governance 52

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 remediation strategies come in. Data augmentation.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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

O'Reilly on Data

OCTOBER 22, 2020

More recently, at least one company has earned itself significant PR and legal controversy for collecting training data sources from social media platforms under circumstances that were at least arguably a violation of both the platform’s terms of service and platform users’ legal rights.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

O'Reilly on Data

JULY 28, 2020

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. One mid-sized digital media company we interviewed reported that their Marketing, Advertising, Strategy, and Product teams once wanted to build an AI-driven user traffic forecast tool.

Marketing 362

Marketing Experimentation Metrics Testing 362

Jet Global

MAY 1, 2020

In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Risk to the business.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 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 52

Metadata Data Governance Digital Transformation Data Quality 52

Ontotext

MAY 2, 2024

They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance. Evolution of data approaches The data strategies we’ve had so far have led to a lot of challenges and pain points.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

erwin

SEPTEMBER 19, 2019

However, once an organization understands that IT and the business are both responsible for data, it needs to develop a comprehensive, holistic strategy for data governance that is capable of four things: Reaching every stakeholder in the process. Providing a platform for understanding and governing trusted data assets.

Data Governance 108

Data Governance Cost-Benefit Enterprise Modeling 108

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

Octopai

JUNE 24, 2019

“Data governance” is data management policy that ensures the integrity, availability, and efficiency of data within a company. This policy includes specialists, processes, and technology used to manage data. Document classification and lifecycle management will help you deal with oversight of unstructured data.

Data Governance 42

Data Governance Metadata Unstructured Data Software 42

Andrew White

NOVEMBER 14, 2022

How is it that trying to be aware of all your data is the first step to working out what data (or analytic) actually needs to be governed first? Catalog are useful in a small number of data governance programs only. If not, a data catalog won’t be needed for at least 9 months. By documenting an organizations data issues.

Data Integration 52

Data Integration Technology Data Governance Analytics 52

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 271

Enterprise Modeling Testing Reporting 271

CIO Business Intelligence

JANUARY 23, 2024

These objectives overlap and are interdependent, but separating them in this way highlights the three steps CIOs should take to ensure their KPI strategies align with their three objectives. The art is asking the right questions and connecting experience metrics to the digital strategy and prioritized initiatives.

Measurement 127

Measurement Digital Transformation KPI Metrics 127

datapine

APRIL 15, 2020

When it comes to implementing and managing a successful BI strategy we have always proclaimed: start small, use the right BI tools , and involve your team. Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

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. Reshape and aggregate data as necessary. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

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.

Reporting 100

Reporting Data Quality Strategy Data-driven 100

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 79

Insurance Risk Data-driven Finance 79

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).

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

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