datapine

JANUARY 24, 2021

In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. Your Chance: Want to perform advanced data analysis with a few clicks? Try our professional data analysis software for 14 days, completely free! Data Is Only As Good As The Questions You Ask.

IT 317

IT Statistics KPI Data-driven 317

Domino Data Lab

FEBRUARY 4, 2021

The original container use case for data science focused on what I call, “environment management”. Getting models into production is a critical stage in the MLOps life cycle. What role does container technology play in getting ML/AI models into production? Container technology has changed the way data science gets done.

Modeling 130

Modeling Data Science Data Processing Software 130

datapine

JANUARY 31, 2022

Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was. What Are The Benefits of Business Intelligence? In order to do this, they first defined what data was the most relevant for the company. The power of knowledge.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. What is Data in Use? One of the primary sources of tension?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

AWS Big Data

NOVEMBER 29, 2024

This framework acts in a provider-subscriber model to enable data transfers between SAP systems and non-SAP data targets. This blog post details how you can extract data from SAP and implement incremental data transfer from your SAP source using the SAP ODP OData framework with source delta tokens.

Visualization 105

Visualization Data Processing Data-driven Cost-Benefit 105

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. What is RAPIDS. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? What Is Data Quality Management (DQM)? But first, let’s define what data quality actually is. What is the definition of data quality? Table of Contents. 2) Why Do You Need DQM? 3) The 5 Pillars of DQM. 4) Data Quality Best Practices. 5) How Do You Measure Data Quality?

Data Quality 241

Data Quality Metrics Data-driven Management 241

DataKitchen

MAY 10, 2024

This blog explores the third of five critical use cases for Data Observability and Quality Validation—data Production—highlighting how DataKitchen’s Open-Source Data Observability solutions empower organizations to manage this critical stage effectively. Are production models accurate, and do dashboards display correct data?

Metrics 124

Metrics Testing Data Quality Dashboards 124

DataKitchen

JANUARY 3, 2022

Business analytic teams have ongoing deliverables – a dashboard, a PowerPoint, or a model that they refresh and renew. A business analyst team receiving deliverables from an IT organization like that doesn’t trust what they receive and ends up having to QA it thoroughly, which also involves a lot of effort. Analytics Hub and Spoke.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

erwin

OCTOBER 8, 2020

And as you make this transition, you need to understand what data you have, know where it is located, and govern it along the way. But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy. Automated Cloud Migration.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

DataKitchen

NOVEMBER 20, 2021

What makes an effective DataOps Engineer? You might ask what that means. A DataOps Engineer shepherds process flows across complex corporate structures. Organizations have changed significantly over the last number of years and even more dramatically over the previous 12 months, with the sharp increase in remote work.

Testing 246

Testing Machine Learning Data Warehouse Analytics 246

DataKitchen

MAY 10, 2021

If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. As machine learning advances globally, we can only expect the focus on model risk to continue to increase.

Risk 111

Risk Modeling IT Risk Management 111

DataKitchen

MAY 10, 2024

This blog post delves into the third critical use case for Data Observation and Data Quality Validation: development and Deployment. What Code Is In What Environment? What Code Is In What Environment? How Many Models Dashboards Were Deployed? What Is The Average Number Of Tests Per Pipeline?

Testing 124

Testing Cost-Benefit Data Quality Dashboards 124

DataKitchen

JULY 12, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. While people can do what they want with language (and many often do), syntax helps ordinary language users understand how to organize words to make the most sense. Do you know as a data engineer?

Data Quality 130

Data Quality Testing Manufacturing Finance 130

Domino Data Lab

JULY 10, 2019

This blog post considers Ludwig, offering a brief overview of the package and providing tips for practitioners such as when to use Ludwig’s command-line syntax and when to use its Python API. This blog also provides code examples with a Jupyter notebook that you can download or run via hosting provided by Domino.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

Domino Data Lab

OCTOBER 7, 2020

In this blog we show what the changes in behavior of data are in high dimensions. In our next blog we discuss how we try to avoid these problems in applied data analysis of high dimensional data. Statistics developed in the last century are based on probability models (distributions). Danger of Big Data.

Statistics 145

Statistics Testing Predictive Modeling Big Data 145

erwin

DECEMBER 9, 2024

For instance, Large Language Models (LLMs) are known to ultimately perform better when data is structured. But how can delivering an intelligent data foundation specifically increase your successful outcomes of AI models? One reason would be to counteract our inherent bias as we work to train the data that feeds AI models.

Metadata 52

Metadata Data Quality Sales Modeling 52

datapine

SEPTEMBER 1, 2020

What Is A Market Research Report? However, today’s business world still lacks a way to present market-based research results in an efficient manner – the static, antiquated nature of PowerPoint makes it a bad choice in the matter, yet it is still widely used to present results. Your Chance: Want to test a market research reporting software?

Reporting 247

Reporting Marketing KPI Dashboards 247

DataKitchen

SEPTEMBER 18, 2024

These leaders are expected to influence organizational behavior without direct authority, leading to what DataKitchen CEO Christopher Bergh described as “data nags”—individuals who know what’s wrong but struggle to get others to act. The challenge is not simply a technical one. How the change should be communicated and implemented.

Scorecard 177

Scorecard Data Quality Measurement Testing 177

Occam's Razor

FEBRUARY 25, 2013

This blog is centered around creating incredible digital experiences powered by qualitative and quantitative data insights. But we've never stopped to consider this question: What is the return on investment (ROI) of digital analytics? We'll have a lot of detail in the model. Analysts: Put up or shut up time!

Analytics 143

Analytics ROI Marketing Modeling 143

Rocket-Powered Data Science

JULY 19, 2023

The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage. What could be faster and easier than on-prem enterprise data sources? A similarly high percentage of tabular data usage among data scientists was mentioned here.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Sisense

AUGUST 6, 2020

So what are the high-level steps to incorporate AI and machine learning into new and existing products? Improving customer experience and reducing cost in a single step sounds impossible, but this is exactly what correctly implemented AI can achieve. What is the process to improve recommender engines?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

DataRobot Blog

JULY 22, 2022

Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.

Modeling 105

Modeling Risk Management Testing Risk 105

AWS Big Data

JULY 11, 2024

In this blog post, we propose a solution based on Amazon OpenSearch Service for similarity search and the pretrained model ProtT5-XL-UniRef50 , which we will use to generate embeddings. ProtT5-XL-UniRef50 is based on the t5-3b model and was pretrained on a large corpus of protein sequences in a self-supervised fashion.

Machine Learning 115

Machine Learning Modeling Data Processing Testing 115

DataRobot Blog

JULY 5, 2022

Cleaned and enriched geospatial data combined with geostatistical feature engineering provides substantial positive impact on a housing price prediction model’s accuracy. The question we’ll be looking at is: What is the predicted sale price for a home sale listing? Utah Spatial Modeling Process. Price Prediction Example.

Recreation/Entertainment 103

Recreation/Entertainment Machine Learning Predictive Modeling Sales 103

DataKitchen

JUNE 24, 2021

Agility is absolutely the cornerstone of what DataOps presents in the build and in the run aspects of our data products.”. That’s when organizations start to get a quick and powerful feel for what the opportunity is. Jim Tyo, Chief Data Officer, Invesco. Chris Bergh, CEO & Head Chef, DataKitchen. DataOps is a complementary process.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

Ontotext

DECEMBER 9, 2020

Perhaps, you have already built one, which is inconsistent, fragile, difficult to integrate with modern front end stacks, does not support the basics such as role-based access control, data shape validation, caching or denial of service limits. Generate a semantic object model from existing RDF schemas.

Modeling 130

Modeling Management Optimization Machine Learning 130

Smart Data Collective

FEBRUARY 21, 2021

A growing number of marketers are using data analytics technology to optimize their lead generation models. But what lead generation strategies can you use in conjunction with your data analytics tools. Combining Data Analytics with Your Lead Generation Model. Data analytics is very important to the future of marketing.

Data Analytics 120

Data Analytics Strategy Analytics Data-driven 120

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. But what do those adjectives actually mean? What actions earn you these labels? I'd like to share the contents of that document in this blog post.

Metrics 107

Metrics Measurement Statistics Data Collection 107

CIO Business Intelligence

JUNE 12, 2024

To help alleviate the complexity and extract insights, the foundation, using different AI models, is building an analytics layer on top of this database, having partnered with DataBricks and DataRobot. Some of the models are traditional machine learning (ML), and some, LaRovere says, are gen AI, including the new multi-modal advances.

Modeling 140

Modeling Testing Cost-Benefit Metadata 140

DataRobot

MARCH 10, 2021

Although AI is powerful and generates trillions of dollars of economic value across the world, what you see in science fiction movies remains pure fiction. Deploy the machine learning model into production. Beware the hype about AI systems. Contrast the dictionary definition with how the word is used. Is AI Autonomous?

Machine Learning 145

Machine Learning Experimentation Key Performance Indicator Reporting 145

Cloudera

DECEMBER 21, 2020

In this blog we will take you through a persona-based data adventure, with short demos attached, to show you the A-Z data worker workflow expedited and made easier through self-service, seamless integration, and cloud-native technologies. Shaun plans to clone the exemplified model linked from the report to his local environment.

Dashboards 97

Dashboards Visualization Data Warehouse Data Lake 97

Octopai

APRIL 19, 2021

Without organized metadata management, the validity of a company’s data is compromised and they won’t achieve adequate compliance, data governance, or generate correct insights. Manual Data Lineage is a Thing of the Past Learn what Automated Data Lineage Can Do For Your BI & Analytics Team Download the eBook. IRM UK Connects.

Metadata 59

Metadata Management Business Intelligence Data Governance 59

CIO Business Intelligence

OCTOBER 24, 2023

The capabilities of these new generative AI tools, most of which are powered by large language models (LLM), forced every company and employee to rethink how they work. First, there’s the internal demand to understand how your organization is going to adopt these new tools and what you need to do to avoid falling behind your competitors.

Cost-Benefit 126

Cost-Benefit Modeling Data Quality Statistics 126

CIO Business Intelligence

JUNE 10, 2022

But in helming IT at IBM, she is also tasked with identifying what technologies make the most sense not only for IBM but also its CIO clients. The company had already been operating in a largely hybrid model, with flexible workplace best practices in place. Back in around the 1950s is when the first CIO roles really emerged.

Technology 140

Technology Enterprise Management Finance 140

DataKitchen

OCTOBER 5, 2022

Observability also validates that your data transformations, models, and reports are performing as expected. Part 1: Defining the Problems. This is the first post in DataKitchen’s four-part series on DataOps Observability. DataOps Industry Challenges. This solution sits on top of your existing infrastructure?without

Testing 169

Testing Risk Data Processing Statistics 169