Analytics Vidhya

AUGUST 1, 2022

What is equally important here is the ability to communicate the data and insights from your predictive models through reports and dashboards. Introduction In this article, we will explore one of Microsoft’s proprietary products, “PowerBI”, in-depth. PowerBI is used for Business intelligence. And […].

Reporting 389

Reporting Predictive Modeling Dashboards Business Intelligence 389

O'Reilly on Data

APRIL 2, 2019

Model packaging: companies are using MLflow to incorporate custom logic and dependencies as part of a model’s package abstraction before deploying it to their production environment (example: a recommendation system might be programmed to not display certain images to minors). Model governance. there aren’t enough of them.

Machine Learning 260

Machine Learning Modeling Data Science Software 260

datapine

JULY 26, 2022

1) What Is A Small Business Dashboard? 3) Why Do You Need Small Business Dashboards. 4) Small Business Dashboard & KPIs Examples. Enter small business dashboards and metrics. BI dashboard tools provide smaller businesses with valuable insights regarding their operations thanks to real-time access to data.

Dashboards 151

Dashboards KPI Metrics Sales 151

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

Bigeye’s monitoring capabilities start with automated dependency mapping to identify the source of data used in analytic dashboards and data products, as well as a lineage graph of the data pipeline. The ability to monitor and measure improvements in data quality relies on instrumentation.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

Cloudera

JUNE 26, 2024

In today’s data-driven world, businesses rely heavily on their dashboards to make informed decisions. However, traditional dashboards often lack the intuitive interface needed to truly harness the power of data. Say goodbye to static dashboards and hello to a whole new level of engagement.

Dashboards 81

Dashboards Modeling Visualization Business Intelligence 81

Analytics Vidhya

JUNE 9, 2022

Power BI interviews will provide insights from a variety of data by modelling data and telling stories from data visualizations using reports and dashboards. Introduction Power BI is one of the most popular data visualization and analytics software product developed by Microsoft. Source: [link] […].

Visualization 291

Visualization Dashboards Data Science Publishing 291

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The predictive models, in practice, use mathematical models to predict future happenings, in other words, forecast engines. Data exploded and became big. We all gained access to the cloud.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

CIO Business Intelligence

FEBRUARY 6, 2025

The hype around large language models (LLMs) is undeniable. Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Tableau, Qlik and Power BI can handle interactive dashboards and visualizations. They leverage around 15 different models. Theyre impressive, no doubt.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

CIO Business Intelligence

NOVEMBER 4, 2024

The Cybersecurity Maturity Model Certification (CMMC) serves a vital purpose in that it protects the Department of Defense’s data. Myrddin is now part of our CMMC dashboard tool that assists users in conducting gap assessments and interpreting cybersecurity compliance guidelines,” says Birmingham. “It

Dashboards 115

Dashboards Risk Risk Management Data-driven 115

AWS Big Data

JANUARY 15, 2025

In the following section, two use cases demonstrate how the data mesh is established with Amazon DataZone to better facilitate machine learning for an IoT-based digital twin and BI dashboards and reporting using Tableau. The applications are hosted in dedicated AWS accounts and require a BI dashboard and reporting services based on Tableau.

IoT 101

IoT Machine Learning Metadata Data-driven 101

CIO Business Intelligence

NOVEMBER 4, 2024

“This process involves connecting AI models with observable actions, leveraging data subsequently fed back into the system to complete the feedback loop,” Schumacher said. Most AI hype has focused on large language models (LLMs).

Strategy 128

Strategy Data-driven Predictive Analytics Dashboards 128

datapine

SEPTEMBER 1, 2020

Today, there are online data visualization tools that make it easy and fast to build powerful market-centric research dashboards. How To Present Your Results: 3 Market Research Example Dashboards. When it comes to sharing the most important information, research dashboards are invaluable. Let’s take a closer look.

Reporting 247

Reporting Marketing KPI Dashboards 247

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

O'Reilly on Data

OCTOBER 19, 2021

If you ask an engineer to show how they operate the application in production, they will likely show containers and operational dashboards—not unlike any other software service. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 363

IT Testing Experimentation Software 363

datapine

MAY 11, 2022

Data visualizations put together in intuitive dashboards can make the analysis process more dynamic and understandable while keeping the audience engaged. The principles of color theory will have a notable impact on the overall success of your visualization model. Gauge charts can be effectively used with a single value or data point.

Visualization 252

Visualization Dashboards Key Performance Indicator Sales 252

David Menninger's Analyst Perspectives

MARCH 17, 2022

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance.

Business Intelligence 207

Business Intelligence Data Warehouse Data Quality Dashboards 207

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. If you’re basing business decisions on dashboards or the results of online experiments, you need to have the right data.

Machine Learning 363

Machine Learning Statistics Data Quality Data Collection 363

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. Asking the right business intelligence questions will lead you to better analytics.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

CIO Business Intelligence

JANUARY 9, 2025

Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. The commodity effect of LLMs over specialized ML models One of the most notable transformations generative AI has brought to IT is the democratization of AI capabilities.

Software 128

Software Enterprise Key Performance Indicator Machine Learning 128

AWS Big Data

DECEMBER 5, 2024

However, it wouldn’t be wise to display an excessive number of metrics on our monitoring dashboards because that could lead to less clarity and slower insights on the cluster. Capacity monitoring dashboards As part of our platform management process, we conduct monthly operational reviews to maintain optimal performance.

Metrics 79

Metrics Dashboards Testing Optimization 79

datapine

APRIL 15, 2020

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. In the traditional model communication between developers and business users is not a priority. Data changes.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

DataKitchen

APRIL 26, 2021

Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Similarly, Workiva was driven to DataOps due to an increased need for analytics agility to meet a range of organizational needs, such as real-time dashboard updates or ML model training and monitoring.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

datapine

NOVEMBER 11, 2021

c) Dashboard Features. Business intelligence tools provide you with interactive BI dashboards that serve as powerful communication tools to keep teams engaged and connected. 3) Dashboards. Here you will find some of the main BI tool features related to dashboard management: a) Built-in dashboard templates.

Business Intelligence 213

Business Intelligence Dashboards Interactive Visualization 213

CIO Business Intelligence

JANUARY 18, 2024

Recognizing this need, we have created a cutting-edge VSM maturity model. Drawing upon our extensive experience facilitating successful VSM initiatives within large-scale enterprises, this model serves as a compass, offering insights into their current VSM maturity and providing practical direction to advance and reap the additional benefits.

Digital Transformation 119

Digital Transformation Modeling Management Data-driven 119

O'Reilly on Data

AUGUST 18, 2020

However, while Cloudera, Hortonworks, and MapR worked well for a set of common data engineering workloads, they didn’t generalize well to workloads that didn’t fit the MapReduce paradigm, including deep learning and new natural language models. Let’s dive into these reasons in more depth. Data Science and Machine Learning Require Flexibility.

Data Science 364

Data Science Machine Learning Data Warehouse Deep Learning 364

CIO Business Intelligence

JANUARY 14, 2025

They achieve this through models, patterns, and peer review taking complex challenges and breaking them down into understandable components that stakeholders can grasp and discuss. This comprehensive model helps architects become true enablers of organizational success. Most importantly, architects make difficult problems manageable.

Enterprise 119

Enterprise Technology Metrics Measurement 119

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

DataKitchen

SEPTEMBER 20, 2021

The pipelines and workflows that ingest data, process it and output charts, dashboards, or other analytics resemble a production pipeline. When analytics and dashboards are inaccurate, business leaders may not be able to solve problems and pursue opportunities. Figure 1: The four phases of Lean DataOps. Production DataOps. Data Errors.

Testing 246

Testing Metrics Measurement Dashboards 246

datapine

OCTOBER 7, 2020

Turning these datasets into a business dashboard can effectively track the right values and offer a comprehensive application to the entire business system. The CPC (cost-per-click) overview of campaigns is an operational metric that expounds on the standard pricing model in online advertising. Let’s see this through an example.

KPI 269

KPI Metrics Sales Visualization 269

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

FEBRUARY 4, 2020

That said, we will go into details of a marketing campaign report, provide a digital marketing report template, and look into marketing reporting and analysis with the help of carefully created dashboards. It shows how targets are performing in a monthly view, but the user can easily set this marketing dashboard to a yearly time frame.

Reporting 280

Reporting Marketing Advertising Metrics 280

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 49

Machine Learning Management Enterprise Risk Management 49

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. Naveego — A simple, cloud-based platform that allows you to deliver accurate dashboards by taking a bottom-up approach to data quality and exception management.

Testing 300

Testing Machine Learning Consulting Data Science 300

Cloudera

DECEMBER 4, 2024

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. The Cloudera AI Inference service is a highly scalable, secure, and high-performance deployment environment for serving production AI models and related applications.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

DataKitchen

JANUARY 3, 2022

Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups. They see the data errors , the production errors, the broken reports and the inaccurate dashboards. The work product could be a chart, graph, model or dashboard.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

datapine

DECEMBER 11, 2019

SaaS is a software distribution model that offers a lot of agility and cost-effectiveness for companies, which is why it’s such a reliable option for numerous business models and industries. Flexible payment options: Businesses don’t have to go through the expense of purchasing software and hardware. 6) Micro-SaaS.

Software 314

Software Cost-Benefit Data-driven Data Processing 314

CIO Business Intelligence

APRIL 8, 2025

These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When customer records are duplicated or incomplete, personalization fails.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102