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 141

Measurement Digital Transformation KPI Metrics 141

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. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric.

Marketing 364

Marketing Experimentation Metrics Testing 364

CIO Business Intelligence

DECEMBER 20, 2024

Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. Optimize data flows for agility. AI and machine learning models. Curate the data. Application programming interfaces.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

CIO Business Intelligence

NOVEMBER 20, 2023

Measuring developer productivity has long been a Holy Grail of business. In addition, system, team, and individual productivity all need to be measured. Using tools such as Jira, which measures backlog management, it is possible to spot trends that are damaging to optimization. So, it’s complicated. Contribution analysis.

Measurement 126

Measurement Metrics Testing Software 126

CIO Business Intelligence

JANUARY 21, 2025

Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts. Should CIOs bring AI to the data or bring data to the AI?

Cost-Benefit 142

Cost-Benefit Data-driven Strategy Marketing 142

Smart Data Collective

FEBRUARY 9, 2022

Luckily, there are a few analytics optimization strategies you can use to make life easy on your end. Helps you to determine areas of abnormal losses and profits to optimize your trading algorithm. Enables animation and object modeling of 3D charts for better analysis and testing.

Visualization 145

Visualization Optimization Analytics Testing 145

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning).

Advertising 357

Advertising Cost-Benefit ROI Machine Learning 357

Smart Data Collective

JANUARY 19, 2023

Reasons for Cost Optimization Cost optimization is an important part of any organization’s DevOps strategy. By optimizing costs, organizations can maximize their profits and keep up with the ever-changing business landscape. But what are some of the reasons why DevOps teams should consider cost optimization?

Optimization 124

Optimization Cost-Benefit Software Measurement 124

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

Machine Learning 270

Machine Learning Risk Management Statistics 270

CIO Business Intelligence

JANUARY 21, 2025

Excessive infrastructure costs: About 21% of IT executives point to the high cost of training models or running GenAI apps as a major concern. These concerns emphasize the need to carefully balance the costs of GenAI against its potential benefits, a challenge closely tied to measuring ROI. million in 2025 to $7.45

Cost-Benefit 98

Cost-Benefit ROI Measurement Strategy 98

CIO Business Intelligence

MARCH 25, 2025

Using the companys data in LLMs, AI agents, or other generative AI models creates more risk. Build up: Databases that have grown in size, complexity, and usage build up the need to rearchitect the model and architecture to support that growth over time. Playing catch-up with AI models may not be that easy.

Risk 123

Risk Cost-Benefit Data-driven Digital Transformation 123

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 363

IT Testing Experimentation Software 363

CIO Business Intelligence

OCTOBER 9, 2024

Depending on your needs, large language models (LLMs) may not be necessary for your operations, since they are trained on massive amounts of text and are largely for general use. As a result, they may not be the most cost-efficient AI model to adopt, as they can be extremely compute-intensive.

Strategy 143

Strategy Modeling Management Data Lake 143

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 364

Management Experimentation B2B Machine Learning 364

CIO Business Intelligence

NOVEMBER 13, 2024

The company has already rolled out a gen AI assistant and is also looking to use AI and LLMs to optimize every process. One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. We’re doing two things,” he says.

IT 141

IT Consulting ROI Cost-Benefit 141

CIO Business Intelligence

DECEMBER 18, 2024

Our history is rooted in a traditional distribution model of marketing, selling, and shipping vendor products to our resellers. What were the technical considerations moving from a distribution model to a platform? As a platform company, measurement is crucial to success. This is crucial in a value-driven development model.

Digital Transformation 116

Digital Transformation Metrics Measurement ROI 116

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

datapine

OCTOBER 7, 2020

What gets measured gets done.” – Peter Drucker. By setting operational performance measures, you will know what is happening at every stage of your business. Since every business is different, it is essential to establish specific metrics and KPIs to measure, follow, calculate, and evaluate. Who will measure it?

KPI 269

KPI Metrics Sales Visualization 269

O'Reilly on Data

AUGUST 4, 2022

We can find many more examples across many more decades that reflect naiveté and optimism and–if we are honest–no small amount of ignorance and hubris. This kind of humility is likely to deliver more meaningful progress and a more measured understanding of such progress. We typically underappreciate how complex such systems are.

Machine Learning 339

Machine Learning Modeling Statistics Software 339

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. We need to decide on all of these parameterizations of the clustering model before the cold start interations on the cluster means can begin.

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk. Model risk management. Image by Ben Lorica.

Machine Learning 48

Machine Learning Management Enterprise Risk Management 48

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Insights from ML models can be channeled through Amazon DataZone to inform internal key decision makers internally and external partners.

IoT 111

IoT Machine Learning Metadata Data-driven 111

CIO Business Intelligence

NOVEMBER 4, 2024

However, enterprise cloud computing still faces similar challenges in achieving efficiency and simplicity, particularly in managing diverse cloud resources and optimizing data management. AI models rely on vast datasets across various locations, demanding AI-ready infrastructure that’s easy to implement across core and edge.

Finance 89

Finance Sales Reporting Optimization 89

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . DataOps requires that teams measure their analytic processes in order to see how they are improving over time.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

MARCH 4, 2025

From AI models that boost sales to robots that slash production costs, advanced technologies are transforming both top-line growth and bottom-line efficiency. Operational efficiency: Logistics firms employ AI route optimization, cutting fuel costs and improving delivery times.

Technology 118

Technology ROI Cost-Benefit Manufacturing 118

KDnuggets

SEPTEMBER 20, 2022

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

Metrics 160

Metrics Measurement Optimization Modeling 160

datapine

MAY 2, 2023

You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. According to studies, 92% of data leaders say their businesses saw measurable value from their data and analytics investments.

Big Data 275

Big Data Internet of Things Cost-Benefit Optimization 275

CIO Business Intelligence

JULY 23, 2023

Optimization also rose to the top of IT leaders’ lists: 67% measure success within their IT organization by better optimizing resources. Optimizing IT resources—namely infrastructure, processes, and people—fuels digital transformation and modernization, which drives businesses to keep pace in today’s tough economy.

Optimization 95

Optimization IT Digital Transformation Uncertainty 95

O'Reilly on Data

MARCH 23, 2023

Many of these go slightly (but not very far) beyond your initial expectations: you can ask it to generate a list of terms for search engine optimization, you can ask it to generate a reading list on topics that you’re interested in. It’s important to understand that ChatGPT is not actually a language model. with specialized training.

IT 344

IT Modeling Testing Risk 344

CIO Business Intelligence

MARCH 19, 2025

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

IT 59

IT Data Governance Data-driven Metrics 59

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 279

Machine Learning Data Quality Statistics Modeling 279

Smart Data Collective

MARCH 14, 2022

Data analytics technology is becoming a more important aspect of business models in all industries. Data Analytics is an Invaluable Part of SaaS Revenue Optimization. In this article, we will cover what SaaS sales is, the SaaS cycle, choosing strategies and models, and how to measure the success of SaaS sales.

Modeling 124

Modeling Analytics Key Performance Indicator Sales 124

AWS Big Data

NOVEMBER 7, 2024

Custom context enhances the AI model’s understanding of your specific data model, business logic, and query patterns, allowing it to generate more relevant and accurate SQL recommendations. Your queries, data and database schemas are not used to train a generative AI foundational model (FM).

Metadata 105

Metadata Sales Data Warehouse Optimization 105

AWS Big Data

DECEMBER 5, 2024

As the use of Hydro grows within REA, it’s crucial to perform capacity planning to meet user demands while maintaining optimal performance and cost-efficiency. To perform the tests within a specific time frame and budget, we focused on the test scenarios that could efficiently measure the cluster’s capacity.

Metrics 95

Metrics Dashboards Testing Optimization 95

DataKitchen

SEPTEMBER 20, 2021

The hours formerly wasted on unplanned work can be put to more productive use – creating innovative analytics for the enterprise and improving productivity further by investing in DataOps process optimizations. For example, data cleansing, ETL, running a model, or even provisioning cloud infrastructure. Measurement DataOps.

Testing 246

Testing Metrics Measurement Dashboards 246

IBM Big Data Hub

NOVEMBER 6, 2023

AI and API security Among existing API security measures, AI has emerged as a new — and potentially powerful — tool for fortifying APIs. AI technologies can also enable automated threat modeling. Using historical API data, AI can build threat models to predict vulnerabilities and threats before bad actors can exploit them.

Optimization 113

Optimization Testing Software Risk 113

AWS Big Data

FEBRUARY 21, 2025

Youll learn about how AI-powered search systems employ foundation models (FMs) to capture and search context and meaning across text, images, audio, and video, delivering more accurate results to users. However, generative AI models can produce hallucinationsoutputs that appear convincing but contain factual errors.

Dashboards 79

Dashboards Modeling Measurement Interactive 79