CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. Business intelligence analyst job requirements BI analysts typically handle analysis and data modeling design using data collected in a centralized data warehouse or multiple databases throughout the organization.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

CIO Business Intelligence

AUGUST 25, 2023

Hot Melt Optimization employs a proprietary data collection method using proprietary sensors on the assembly line, which, when combined with Microsoft’s predictive analytics and Azure cloud for manufacturing, enables P&G to produce perfect diapers by reducing loss due to damage during the manufacturing process.

Predictive Analytics 98

Predictive Analytics IoT Manufacturing Analytics 98

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 135

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 135

Smart Data Collective

APRIL 20, 2022

Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon. One of the best ways to create a profitable business model with Amazon involves using data analytics to optimize your PPC marketing strategy.

Data Quality 116

Data Quality Analytics Testing Sales 116

Smart Data Collective

OCTOBER 30, 2021

Last year, one expert reported that Netflix used big data to grow to become a $100 billion company. This shouldn’t surprise anybody, because big data has been instrumental in their business model since the day the company was launched. But what data does it actually process? Is it only watch history?

Big Data 115

Big Data Data Collection Testing Modeling 115

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Smart Data Collective

OCTOBER 8, 2023

The Power of Data Analytics: An Overview Data analytics, in its simplest form, is the process of inspecting, cleansing, transforming, and modeling data to unearth useful information, draw conclusions, and support decision-making. This involves data collection , data cleaning, data analysis, and data interpretation.

Data Analytics 95

Data Analytics Analytics Statistics Data Collection 95

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

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

CIO Business Intelligence

MARCH 30, 2022

Even when customers have tuned their rules for years with fraud analysts who know the space, we come in with machine learning models and beat them,” says Baumhof. But six years ago, when LexisNexis began to invest in machine learning as a replacement for rules-based systems, the company started with a linear regression model.

Machine Learning 144

Machine Learning Testing Risk Data-driven 144

DataKitchen

MARCH 16, 2023

Errors can originate from various sources, including data collection, integration, models, visualization, governance, and security. Organizations will make better-informed, data-driven decisions by minimizing errors and avoiding the negative consequences of relying on inaccurate information. Where do you find errors?

Metrics 130

Metrics Data Analytics Analytics Measurement 130

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

datapine

JANUARY 6, 2022

Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Rocket-Powered Data Science

JUNE 17, 2022

Here is a list of my top moments, learnings, and musings from this year’s Splunk.conf : Observability for Unified Security with AI (Artificial Intelligence) and Machine Learning on the Splunk platform empowers enterprises to operationalize data for use-case-specific functionality across shared datasets. is here, now!

Machine Learning 237

Machine Learning Recreation/Entertainment Risk Business Objectives 237

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 86

Data Science Data Analytics Prescriptive Analytics Analytics 86

Rocket-Powered Data Science

JULY 21, 2023

” Model-Assisted Threat Hunts , also known as Splunk M-ATH , is Splunk’s brand name for machine learning-assisted threat hunting and mitigation. testing for hypothesized threats, behaviors, and activities), (2) Baseline (i.e., automation of the first two type of hunts, using AI and machine learning).

Machine Learning 189

Machine Learning Metrics Reporting Internet of Things 189

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications. How the models are stored.

Data Science 60

Data Science Machine Learning Cost-Benefit Deep Learning 60

datapine

MAY 2, 2023

Your Chance: Want to test a professional logistics analytics software? 10 Essential Big Data Use Cases in Logistics Now that you’re up to speed on the perks of investing in analytics, let’s look at some practical examples that highlight the growing importance of data in logistics, based on different business scenarios.

Big Data 275

Big Data Cost-Benefit Internet of Things Optimization 275

CIO Business Intelligence

MAY 16, 2023

AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data. This helps to ensure accuracy and neutrality.

Advertising 102

Advertising Recreation/Entertainment Experimentation Enterprise 102

Smart Data Collective

AUGUST 2, 2022

Automotive OEMs and top automotive software companies can work together to build resilient software development processes with sophisticated AI algorithms that allow them to innovate, meet growing customer needs for infotainment systems, and monetize new business models.

Software 127

Software Machine Learning B2B Manufacturing 127

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 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

SEPTEMBER 8, 2022

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

Cost-Benefit 121

Cost-Benefit Internet of Things Data-driven Data Lake 121

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Decision Management Solutions

FEBRUARY 8, 2021

in partnership with IBM and other organizations is in the process of conducting performance tests on the world’s first autonomous ship and is due to set sail for its first transatlantic voyage this spring. I think you’ll see the parallels with Decision Management as the story unfolds. Marine AI—based in Plymouth, U.K.—in

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. To comply with licensing considerations, we cannot provide a sample of the ETF constituents data.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. It empowers businesses to explore and gain insights from large volumes of data quickly. Choose the Test tab.

Dashboards 62

Dashboards Testing Metrics Optimization 62

Smart Data Collective

MARCH 28, 2022

It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. Prescriptive data analytics: It is used to predict outcomes and necessary subsequent actions by combining the features of big data and AI. They can be again classified as random testing and optimization.

Data Analytics 85

Data Analytics Software Analytics Statistics 85

Smart Data Collective

MARCH 8, 2021

All in all, the concept of big data is all about predictive analytics. Such data is great for introducing revamped maintenance practices. Thanks to the availability of comparative data for practically every type/model of a vehicle, fleet managers can plan maintenance activities based on certain knowledge.

Big Data 143

Big Data Management Predictive Analytics Statistics 143

CIO Business Intelligence

JUNE 5, 2023

By integrating remote working capabilities, the CIO plays a hand in an organization’s shift to an increasingly remote or hybrid workforce model—a move that can significantly reduce a company’s carbon footprint. This opportunity to collaborate with sustainability in mind extends to the influence CIOs hold over where and how employees work.

Software 105

Software Measurement Visualization Business Objectives 105

IBM Big Data Hub

FEBRUARY 23, 2024

DL models can improve over time through further training and exposure to more data. When a user sends a message, the system uses NLP to parse and understand the input, often by using DL models to grasp the nuances and intent. Marketing and sales: Conversational AI has become an invaluable tool for data collection.

Enterprise 108

Enterprise Interactive Data-driven Key Performance Indicator 108

AWS Big Data

FEBRUARY 1, 2024

Large language models (LLMs) are becoming increasing popular, with new use cases constantly being explored. This is where model fine-tuning can help. Before you can fine-tune a model, you need to find a task-specific dataset. Next, we use Amazon SageMaker JumpStart to fine-tune the Llama 2 model with the preprocessed dataset.

Metadata 96

Metadata Modeling Data Processing Unstructured Data 96

IBM Big Data Hub

OCTOBER 16, 2023

Examples of AI bias in the real world show us that when discriminatory data and algorithms are baked into AI models, the models deploy biases at scale and amplify the resulting negative effects. Enable secure access to users, models and data. Safeguard AI models, data and infrastructure from adversarial attacks.

Advertising 78

Advertising Machine Learning Data Architecture Cost-Benefit 78

FineReport

MARCH 24, 2024

Data Analyst Job Description: Major Tasks and Duties Data analysts collaborate with management to prioritize information needs, collect and interpret business-critical data, and report findings. Jupyter Notebooks: Simplifies code testing and collaboration for data analysis tasks.

Statistics 52

Statistics Data mining Visualization Reporting 52

Ontotext

FEBRUARY 12, 2024

This calls for additional planning, documentation, and testing. A data mesh will likely require more engineers to get started, so a critical mass is needed for successful adoption. Each team should be accountable for providing their prepared data sets to downstream systems.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Ontotext

FEBRUARY 14, 2024

CEEL is an end-to-end transformer-based model that includes mention detection and uses entity types and entity descriptions to perform entity linking. Out of the box, the model links to Wikidata IDs. That’s why such models must use context in order to differentiate between entities, just as humans do.

Cost-Benefit 52

Cost-Benefit Modeling Metadata Optimization 52

Data Science and Beyond

APRIL 5, 2021

The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Instead, many companies have switched to a locally-remote model, hiring remotely within the same country or timezone region. Only time will tell. Sustainability.

Experimentation 61

Experimentation Testing Publishing Measurement 61