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. Data preparation, including anonymizing, labeling, and normalizing data across sources, is key. Right-size your model(s). Assess your readiness. Choose a workload location.

Data Lake 142

Data Lake Consulting Uncertainty Risk 142

CIO Business Intelligence

APRIL 16, 2024

While warp speed is a fictional concept, it’s an apt way to describe what generative AI (GenAI) and large language models (LLMs) are doing to exponentially accelerate Industry 4.0. By leveraging a GenAI-fueled edge with small language models (SLMs), this lengthy work will be streamlined and simplified. Data quality dependence.

Manufacturing 142

Manufacturing Digital Transformation Risk Dashboards 142

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities. So, if you have 1 trillion data points (g.,

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Alation

JUNE 2, 2022

Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches. These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Governance is frequently administered at the tool, modeler, or departmental level. from cognilytica.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

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

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. One recent study shows that only 50% follow a product-centric operating model focusing on customer centricity and delivering delightful customer experiences.

Digital Transformation 141

Digital Transformation IT Data-driven Metrics 141

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. Generative AI-assisted API mapping called out in this paper is a mini exemplar of this.

Cost-Benefit 103

Cost-Benefit Testing Data-driven Enterprise 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 118

Analytics Broadcasting Predictive Analytics Metrics 118

CIO Business Intelligence

DECEMBER 19, 2023

Many technology investments are merely transitionary, taking something done today and upgrading it to a better capability without necessarily transforming the business or operating model. 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

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

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

Timo Elliott

DECEMBER 14, 2023

Now new Generative AI technologies are providing another huge step forward in dealing with imperfect data. Large language models are very good at dealing with some types of messy data. Large language models are good at figuring out what we meant, and the principle applies to many real-world data problems.

Machine Learning 64

Machine Learning IoT Statistics Modeling 64

CIO Business Intelligence

APRIL 4, 2024

Some prospective projects require custom development using large language models (LLMs), but others simply require flipping a switch to turn on new AI capabilities in enterprise software. “AI A human reviews it to make sure it makes sense, and if it does, the AI incorporates that into the learning model,” she says.

Enterprise 134

Enterprise Cost-Benefit Forecasting Strategy 134

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

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 100

Data Warehouse Testing Data Quality Reporting 100

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

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. The Marketing team built the first model, but because it was from marketing, the model optimized for CTR and lead conversion. Nonetheless, building a superior feature pipeline or model architecture will always be worthwhile.

Marketing 362

Marketing Experimentation Metrics Testing 362

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. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Ontotext

MAY 2, 2024

Every new dataset and new user adds a little more friction that hits the core metric of the velocity of data and brings it down to zero. Another challenge is the data models that represent how insights and analytics teams see the world. The other half of our value proposition is Ontotext GraphDB.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

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?

Data Governance 52

Data Governance ROI Metrics Cost-Benefit 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

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 86

Data Governance Business Objectives Strategy Data Quality 86

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. Organizations still depend too much on manual data management.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

Alation

MARCH 14, 2023

Creating a clear process with documented steps will help. Govern Data governance models should be flexible and dynamic while proactively addressing risk management and compliance with local and global regulations.

Data Strategy 52

Data Strategy Strategy Metadata 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

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

erwin

MARCH 8, 2019

Following are two examples that illustrate the data governance stock check, including the Any 2 approach in action, based on real consulting engagements. Then the metadata is classified against a semantic model held in a business glossary. This, among other safeguards, ensures data quality.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

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

CIO Business Intelligence

MARCH 7, 2024

Public LLMs in the sandbox: Safely test publicly available Large Language Models (LLMs) in a sandbox environment, separate from the production setting, to assess their impact without risking operational disruptions. There is also the concern about using data. This means there could be a lot of work to do here.

Digital Transformation 72

Digital Transformation Machine Learning Risk Testing 72

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. Before you put a model into production, you may first need to clear compliance hurdles. Typically, these areas require the most attention: model development, implementation, and use.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

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. Can the current state of our data operations deliver the results we seek?

IT 140

IT Risk Cost-Benefit Enterprise 140

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

Juice Analytics

APRIL 6, 2021

Habit 2: Create a shared vocabulary for your data What is an “active user”? These are terms that need to be carefully defined and documented so we can move on to how we are going to improve them. Val Logan of The Data Lodge is one of the premier thinkers on how organizations can build shared skills in using data.

Metrics 119

Metrics Dashboards Data-driven Data Quality 119

O'Reilly on Data

OCTOBER 22, 2020

Taking the time to work this out is like building a mathematical model: if you understand what a company truly does, you don’t just get a better understanding of the present, but you can also predict the future. Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Cloudera

NOVEMBER 15, 2021

Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . The challenges of data. Data curation.

Unstructured Data 85

Unstructured Data Recreation/Entertainment Structured Data Reporting 85

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