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 107

Analytics Broadcasting Predictive Analytics Metrics 107

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

FEBRUARY 14, 2023

From rebranding data governance in your organization to demonstrating real business impacts, there’s a lot you can do to bring everyone in your business on board. The role of monitoring, measuring, and metrics So, you’ve got the first step done; you’ve implemented data governance and everyone in your organization is on board.

Data Governance 52

Data Governance ROI Metrics Cost-Benefit 52

Alation

FEBRUARY 3, 2022

In order for data governance to continuously improve , practitioners need to think of data governance as a cyclical process. This process embeds continuous improvement into the system through steps that monitor and measure performance to (1) glean insights and (2) integrate those lessons into the governance system.

Data Governance 98

Data Governance Measurement Metadata Testing 98

CIO Business Intelligence

JANUARY 23, 2024

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). He suggests, “Choose what you measure carefully to achieve the desired results.

Measurement 136

Measurement Digital Transformation KPI Metrics 136

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

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

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. 3) Where will your data come from?

IT 317

IT Statistics KPI Data-driven 317

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

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Data Wrangling and Feature Engineering.

Marketing 362

Marketing Experimentation Metrics Testing 362

Alation

OCTOBER 5, 2021

What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Why is your data governance strategy failing?

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

Ontotext

JANUARY 10, 2024

As a start, such a platform would need to support two major design patterns: semantic knowledge hub and semantic data fabric. We call knowledge hub the pattern of using knowledge graphs to better manage documents and unstructured content. This helps improve the way relevant documents are found, recommended, and so on.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Ontotext

MAY 22, 2023

At the heart of such tools is the extraction of fields from forms or specific attributes from documents. Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

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 Consulting Metrics Data Quality 52

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

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

erwin

MARCH 25, 2021

And with many competing projects and activities vying for people’s time, it must be clear to people why choosing data governance activities will have a direct benefit to them. Usually we talk about benefits which are rather qualitative measures, but what we need for decision-making processes are values,” Pörschmann says. “We

Data Governance 109

Data Governance IT Cost-Benefit Risk 109

AWS Big Data

MARCH 7, 2023

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. Unlike ingestion processes, data can be transformed as per business rules before loading.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

Jet Global

AUGUST 23, 2021

Used primarily in a strategic context by corporate Finance Divisions and Boards of Directors, financial statements are key documents that must be prepared and produced with care. Manage data from diverse systems. Data quality is a central point for producing quality reports that can be used effectively in decision-making.

Finance 52

Finance Reporting Data Collection Data Quality 52

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Some business processes may need reviewing to include data analysis — even going as far as requiring specific data to make a business decision.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Cloudera

NOVEMBER 15, 2021

Examples of structured data include the Iris Flower data set where each datum (corresponding to a sample flower) has the same, predefined structure, namely the flower type, and four numerical features: height and width of the petal and sepal. Data curation. months since 2012.

Unstructured Data 87

Unstructured Data Recreation/Entertainment Structured Data Reporting 87

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

CIO Business Intelligence

MARCH 7, 2024

Firewall capability for AI security: Enhance security measures by providing firewall capabilities to safeguard against potential AI-related vulnerabilities. Documenting these plans and use cases will be critical to minimize the future workload if regulators come knocking (and they will). There is also the concern about using data.

Digital Transformation 73

Digital Transformation Machine Learning Risk Testing 73

Alation

FEBRUARY 7, 2023

The business analysts were dealing with a problem that may sound familiar to folks in the data management space. They sought documentation to help them locate the source of the data from the warehouse. This is why it is critical to have active metadata that signals when crucial data assets have issues impacting business users.

Metadata 52

Metadata Data Warehouse Visualization Data Quality 52

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

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. Protecting Sensitive Data. A more disruptive but sometimes necessary measure is to tie errors to alerts that require attention or intervention from a human operator.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

erwin

APRIL 15, 2020

Rationalize external data dictionaries to our internal data dictionaries and understand where differences occur and how we will overcome those differences. Ingest to a quarantined area where it can be profiled and measured for quality, completeness, and correctness, and where necessary, cleansed.

Data Governance 134

Data Governance Metadata Testing Reporting 134

AWS Big Data

AUGUST 14, 2023

When are data products deprecated, and who is accountable for the consequences to their consumers? How do we define “risk” and “value” in the context of data products, and how can we measure this? Whose responsibility is it to justify the existence of a given data product?

Management 75

Management Risk Measurement Optimization 75

Alation

MARCH 11, 2022

Tip 2: Let the data catalog do the heavy lifting. What data is useful? A data catalog with a Behavioral Analysis Engine will measure human behavior around data to locate your most valuable and actionable data. This data about data, AKA metadata , is an essential layer of your new meshy fabric.

Metadata 52

Metadata Data Governance ROI Data Quality 52

FineReport

APRIL 26, 2023

Here are some key factors to keep in mind: Understanding business objectives : It is important to identify and understand the business objectives before selecting a big data tool. These objectives should be broken down into measurable analytical goals, and the chosen tool should be able to meet those goals. Top 10 Big Data Tools 1.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. Be data driven?" " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love!

KPI 124

KPI Metrics Measurement ROI 124

O'Reilly on Data

JULY 15, 2019

Regulators behind SR 11-7 also emphasize the importance of data—specifically data quality , relevance , and documentation. While models garner the most press coverage, the reality is that data remains the main bottleneck in most ML projects.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

BI-Survey

DECEMBER 22, 2020

Providing an easily accessible description of individual data sources and their dependencies and processing flows is an important step. Data catalog platforms are ideal for this purpose. The usability of data documentation for business users stands and falls with an overarching business glossary.

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52

Octopai

AUGUST 3, 2022

It’s also a critical trait for the data assets of your dreams. What is data with integrity? Data integrity is the extent to which you can rely on a given set of data for use in decision-making. Increased data quality, accessibility, alignment across systems, and context all contribute to increased data integrity.

Data Integration 93

Data Integration Manufacturing Data Quality Data Governance 93

BI-Survey

FEBRUARY 23, 2023

BARC’s model for systematizing the starting points for changing a data culture – the BARC Data Culture Framework – has become well established. Track your progress In each of the six pillars of the BARC Data Culture Framework, it is worthwhile to regularly measure progress.

Metadata 52

Metadata Data-driven Data Strategy Strategy 52

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52