erwin

JULY 30, 2020

ArchiMate is an enterprise architecture (EA) modeling language from The Open Group and used to communicate an organization’s enterprise architecture. Pronounced “ AR-ki-mayt” , the modeling language’s name comes from a compounding of “ archi tecture” and “ani mate.” was released in 2017. How Does ArchiMate Work?

Enterprise 138

Enterprise Visualization Modeling Testing 138

Smart Data Collective

APRIL 5, 2022

Although SageMaker has become a popular hardware accelerator since it was launched in 2017, there are plenty of other overlooked hardware accelerators on the market. This feature helps automate many parts of the data preparation and data model development process. A data visualization interface known as SPSS Modeler.

Cost-Benefit 122

Cost-Benefit Machine Learning Data Science Unstructured Data 122

CIO Business Intelligence

SEPTEMBER 7, 2022

Dubbed Cropin Cloud, the suite comes with the ability to ingest and process data, run machine learning models for quick analysis and decision making, and several applications specific to the industry’s needs. The company says it has partnered with more than 250 B2B customers.

B2B 105

B2B Contextual Data IoT Deep Learning 105

AWS Big Data

AUGUST 7, 2023

Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Amazon Redshift. Simply use SQL statements to create and train SageMaker ML models using your Redshift data and then use these models to make predictions.

Forecasting 80

Forecasting Data Warehouse Machine Learning Modeling 80

Cloudera

NOVEMBER 1, 2022

A quick trip in the congressional time machine to revisit 2017’s Modernizing Government Technology Act surfaces some of the most salient points regarding agencies’ challenges: The federal government spends nearly 75% of its annual information technology funding on operating and maintaining existing legacy information technology systems.

Cost-Benefit 83

Cost-Benefit Data Architecture Risk IoT 83

BizAcuity

NOVEMBER 26, 2019

It is widely used for modeling and structuring of unshaped data. It helps in transforming enterprise data into rich visuals. However, migration from SSRS 2017 has some issues with the PBI RS server. It also offers a more visual and interactive UI for the reports. It can process a large amount of data.

Reporting 84

Reporting Recreation/Entertainment Interactive Data Processing 84

Sisense

JULY 24, 2019

NLQ serves those users who are in a rush, or who lack the skills or permissions to model their data using visualization tools or code editors. Last, and still a very painful challenge for most users, is the familiarity with the underlying data and data model. Machine Intent vs. User Intent.

Analytics 89

Analytics Experimentation Metadata Big Data 89

Andrew White

MAY 28, 2020

and their assumptions would be laid out in front of me: Get the best tool, the cleanest data, the best analytic, the right visualization, and the right decision will be taken. It was the failure of the model that had assumptions about how phones were used, made by people. This is not what being data-driven means.

Data-driven 69

Data-driven Big Data Structured Data Visualization 69

BizAcuity

NOVEMBER 27, 2019

It is widely used for modeling and structuring of unshaped data. It helps in transforming enterprise data into rich visuals. However, migration from SSRS 2017 has some issues with the PBI RS server. It also offers a more visual and interactive UI for the reports. It can process a large amount of data.

Reporting 52

Reporting Recreation/Entertainment Interactive Data Processing 52

BizAcuity

NOVEMBER 27, 2019

It is widely used for modeling and structuring of unshaped data. It helps in transforming enterprise data into rich visuals. However, migration from SSRS 2017 has some issues with the PBI RS server. It also offers a more visual and interactive UI for the reports. It can process a large amount of data.

Reporting 52

Reporting Recreation/Entertainment Interactive Data Processing 52

datapine

MAY 11, 2022

“By visualizing information, we turn it into a landscape that you can explore with your eyes. 90% of the information transmitted to the brain is visual. Data visualization methods refer to the creation of graphical representations of information. That’s where data visualization comes in. A sort of information map.

Visualization 252

Visualization Dashboards Key Performance Indicator Sales 252

IBM Big Data Hub

MARCH 8, 2024

The ecosystem has become more complex as business models advance and new ecommerce trends appear. In recent years, for instance, ecommerce companies based on a subscription model—like Blue Apron and BarkBox— have grown over 1,000%. The ecommerce market has grown exponentially over the last decade.

Consulting 90

Consulting Marketing Sales Data Processing 90

Data Science 101

MAY 6, 2019

I think in physics one of the things that attracted me most to the field that I studied, which was particle physics, was the ability to leverage computer science mathematical modeling and data visualization to solve big questions. And what kind of transformation do you need to apply to the data set to enable modeling?

Data Science 98

Data Science Modeling Machine Learning Testing 98

Smarten

JANUARY 3, 2019

Citizen Analysts create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, Citizen Analysts are not data scientists, or professional analysts or IT staff. ’ Clearly, Citizen Analysts are here to stay!

Business Intelligence 40

Business Intelligence Statistics Interactive Reporting 40

Insight

OCTOBER 25, 2019

My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30). Three views for Object 12: CAD model as visualized by Microsoft’s Print 3D software (left). The first one serves as a baseline for performance.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Jet Global

SEPTEMBER 4, 2020

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data entities are more secure and arguably easier to master than the relational database model, but one downside is there are lots of them!

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

Smarten

NOVEMBER 22, 2017

Self-Serve Advanced Analytics provides a roadmap to guide you through a maze of organizational data with tools and models that will bring together data from disparate sources and enable sophisticated yet simple analytics. These tools allow the enterprise to establish and monitor key metrics and to objectively assess results.

Analytics 40

Analytics IT Business Intelligence Visualization 40

O'Reilly on Data

FEBRUARY 18, 2020

Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage. Not necessarily: Java-related searches increased by 5% between 2017 and 2018. Coincidence?

Data-driven 335

Data-driven Software Statistics Marketing 335

Cloudera

DECEMBER 19, 2018

Despite being a nascent technology, natural language analysis has increased in priority by 23% from 2017 to 2018, according to Dresner’s survey. When dealing with big, fast, and complex data, you need to be able to visualize your analytics and BI in real-time. A modern data warehouse plays a foundational role in analytics and BI.

Data Warehouse 52

Data Warehouse Analytics Big Data Data Architecture 52

Rita Sallam

AUGUST 17, 2016

The platform then automatically finds, visualizes and narrates important findings or the story in the data such as correlations, exceptions, clusters, links and predictions that are relevant to them without requiring them to build models, or write algorithms. Why BeyondCore is Disruptive.

Scorecard 40

Scorecard Visualization Sales Marketing 40

Domino Data Lab

MARCH 16, 2021

We are at an interesting time in our industry when it comes to validating models – a crossroads of sorts when you think about it. There is an opportunity for practitioners and leaders to make a real difference by championing proper model validation. Three models were created. On to Concept Extraction and Building.

Modeling 122

Modeling Deep Learning Testing Optimization 122

Domino Data Lab

JANUARY 6, 2019

Also, clearly there’s no “one size fits all” educational model for data science. Laura Noren, who runs the Data Science Community Newsletter , presented her NYU postdoc research at JuptyerCon 2018, comparing infrastructure models for data science in research and education. The Berkeley model addresses large university needs in the US.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

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. Adrian Weller (2017-07-29). “

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

datapine

NOVEMBER 19, 2019

Exciting and futuristic, the concept of computer vision is based on computing devices or programs gaining the ability to extract detailed information from visual images. Visual analytics: Around three million images are uploaded to social media every single day. billion in 2017 to $190.61 Artificial Intelligence (AI).

Technology 279

Technology Internet of Things IT IoT 279

Domino Data Lab

SEPTEMBER 18, 2019

Models are at the heart of data science. Data exploration is vital to model development and is particularly important at the start of any data science project. From 2010 to 2017, the median price of a single-family home in San Francisco has gone from approximately $775,000 to $1.5 Interactive Data Visualization in Python.

Visualization 99

Visualization Interactive Machine Learning Data Science 99

DataRobot Blog

MAY 25, 2022

In 2017, Hurricane Harvey struck the U.S. DataRobot enables the user to easily combine multiple datasets into a single training dataset for AI modeling. DataRobot combines these datasets and data types into one training dataset used to build models for predicting whether a building will be damaged in the hurricane. The Datasets.

Risk 52

Risk Predictive Modeling Insurance Predictive Analytics 52

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Insight

DECEMBER 30, 2019

In Figure 1, you can see the results of the Harris corner detector applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals: Figure 1?—?Harris Harris corner detection applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals. Original image is on the left.

Visualization 72

Visualization Modeling Machine Learning IT 72

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Sisense

DECEMBER 3, 2019

Data is the New Oil” was coined by The Economist in May 2017 and became a mantra for organizations to drive new wealth from data. With a single click, an ensemble of univariate forecast models run against your data. An AI algorithm selects the best model based on the type of data analyzed. AI (and more) on the horizon.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. We are all familiar with linear and logistic regression models.

IT 68

IT Visualization Modeling Uncertainty 68

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

Forecasting Statistics Modeling Software 64

Smarten

JANUARY 7, 2020

Because traditional BI solutions were not designed to support use by team members within the line of business or business users in general, the enterprise could not capitalize on the unique perspective, knowledge or skill of these users to advance business results, plan for future results or solve problems.

IT 52

IT Business Intelligence ROI Reporting 52

Ontotext

MAY 18, 2020

Such problems and the complexities related to such computationally-intensive tasks are essential in the fields of weather forecasting, molecular modeling, airplane and spacecraft aerodynamics, personalized medicine, self-driving cars. And just when we might have thought FLOPS had hit their limit, here’s another peak achieved at the U.S.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72