Juice Analytics

MARCH 30, 2021

The foundation of getting value from data depends on creating a data fluent culture in your organization. There are many benefits of having a data fluent culture , but what does it take to get there? Here’s the framework we first outlined in our book Data Fluency : Data fluency is a web of connected elements.

Cost-Benefit 131

Cost-Benefit Statistics Metrics Dashboards 131

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 297

Consulting Risk Cost-Benefit Modeling 297

BizAcuity

JULY 7, 2020

Each fragment of the business model needs to be closely observed to ensure that it is equipped with adequate depth in human resources. Earlier, marketing campaigns were highly dependent on data. Book a free demo to explore how our gaming analytics accelerator can help your business to drive more profit.

Revenue Optimization 98

Revenue Optimization Measurement Interactive Marketing 98

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. The time saved can be used for other productive activities.

Data Analytics 84

Data Analytics Software Analytics Statistics 84

datapine

AUGUST 29, 2022

With that in mind, we have prepared a list of the top 19 definitive data analytics and big data books, along with magazines and authentic readers’ reviews upvoted by the Goodreads community. Essential Big Data And Data Analytics Insights. Discover The Best Data Analytics And Big Data Books Of All Time.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

APRIL 7, 2023

So what are ways to store, structure, analyze, and disseminate data collected and centralized to develop digital solutions that promote this? We really use a behavior change model to help our clients deliver on the promise they made to themselves to digitize their records and improve their business.”

Management 94

Management Digital Transformation B2B Marketing 94

Sisense

APRIL 10, 2020

NLP can be used on written text or speech data. For our example, we will use written text for our comparison of R vs Python for data science. We are surrounded by written text every day: emails, SMS messages, webpages, books, and much more. R vs Python for data science: Digging into the differences.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Jet Global

AUGUST 11, 2022

There are numerous challenges facing tax professionals as the emerging BEPS framework becomes clearer and they prepare for adoption: Inconsistent rules – Model rules were released December 2021; however, the market is still lacking enough detail to be able to effectively apply them. Strained resources. We know BEPS 2.0 adoption.

Finance 52

Finance Reporting Consulting Modeling 52

Smart Data Collective

JUNE 15, 2022

Accurate client data collection and analysis are critical to maximizing all of these activities. Given digital marketing’s capacity to accurately target certain groups of individuals, having a good grasp of your model client persona is critical for improving your marketing initiatives. That’s all for this article.

Advertising 97

Advertising Cost-Benefit ROI Sales 97

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Data Collection. Data Mining Models. Classification.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. You can use a regression model. A complementary Domino project is available. . Introduction.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

FineReport

MARCH 29, 2023

This is precisely what is often needed in many enterprise scenarios, such as warehouse management, store management, and employee data management. FineReport supports the design of web and mobile data entry forms, which can collect data into databases and establish business processes during the data collection process.

Reporting 52

Reporting Visualization Dashboards Enterprise 52

CIO Business Intelligence

MARCH 29, 2022

Fortunately, learning-based projects typically use data collected for other purposes. . It’s also important to go back to raw data to ask new questions and train new models, particularly as the world is constantly changing. That, in turn, makes it more likely to find the ones that pay off.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

FineReport

MAY 31, 2023

Dupont Analysis Dashboard FineReport simplifies the implementation of the Dupont analysis model, enabling an in-depth examination of an enterprise’s financial condition through Return on Investment (ROI). The Dupont analysis model comprises three key components: profit margin, asset turnover, and equity multiplier.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Finally, the time series model may give more accurate forecasts than an explanatory or mixed model.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

FineReport

FEBRUARY 9, 2023

We will be more clear about your needs and see how FineReport can help you and your organization to transform data into value. Book a Free Demo 3/Utilize the Backend to Automatically Summarize Numbers In the past, data details had to be exported from SAP and manually split and totaled.

Reporting 40

Reporting IT Finance Enterprise 40

Occam's Razor

OCTOBER 14, 2014

See step four in the process for creating your Digital Marketing and Measurement Model.]. But it is often a million times simpler to create your first set of benchmarks using your own data/performance. If you've read my first book Web Analytics: An Hour A Day, you know that I've advocated this strategy since 2008!

Key Performance Indicator 133

Key Performance Indicator Metrics Reporting Marketing 133

Occam's Razor

MARCH 30, 2017

Most Deep Learning methods involve artificial neural networks, modeling how our bran works. There are some who believe that anything modeled on the human brain, like Deep Learning, will be limited in it’s intelligence, that it will inherit the limits and flaws that our intelligence possesses. 60% increase in bookings in targeted areas.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Occam's Razor

APRIL 15, 2019

Their most common reaction to negative data, if it makes it through, is to try to discredit it by asking analytically-nonsensical questions: What are the p-values of your multi-channel attribution model applied to performance of my strategy? You know my Care-Do-Impact model for analysis and storytelling. Alan Mulally.

Measurement 79

Measurement Strategy Data Processing Uncertainty 79

Occam's Razor

OCTOBER 15, 2012

Please visit the about page to learn more about the data collection methodology, sample sizes, and the Enumeration study to ensure results are representative, and to download the detailed questionnaires used for each study. Let's analyze the multichannel research and purchase behavior of book buyers in the US.

Advertising 136

Advertising Marketing Strategy Insurance 136

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

IBM Big Data Hub

FEBRUARY 8, 2023

Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. This necessitates the detection of bias during data acquisition, building, training, deploying and monitoring models. billion by 2025.

Risk Management 52

Risk Management Risk Modeling Metadata 52

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

O'Reilly on Data

JULY 20, 2021

When we’re building shared devices with a user model, that model quickly runs into limitations. That model doesn’t fit reality: the identity of a communal device isn’t a single person, but everyone who can interact with it. With enough data, models can be created to “read between the lines” in both helpful and dangerous ways.

Risk 303

Risk Software Interactive Modeling 303

Cloudera

MARCH 18, 2021

The Fundamental Review of the Trading Book (FRTB), introduced by the Basel Committee on Banking Supervision (BCBS), will transform how banks measure risk. A revised boundary between the trading book and banking book. A revised internal models approach (IMA) combined with an overhaul of the standardized approach (SA).

Risk 56

Risk Machine Learning Risk Management Measurement 56

erwin

APRIL 21, 2020

The easiest way to understand a data catalog is to look at how libraries catalog books and manuals in a hierarchical structure, making it easy for anyone to find exactly what they need. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

Data is critical to these decisions, such as how to ramp up and support remote employees, re-engineer processes, change entire business models, and adjust supply chains. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance. What Is a Data Catalog?

Measurement 96

Measurement Sales Data Governance Metadata 96

Domino Data Lab

FEBRUARY 4, 2019

Lately there’s been lots of dialog about bias in machine learning models, though considering the noise part of the equation is an important corollary. We’d love to hear your insights and perspectives about running data teams. Of course, some questions in business cannot be answered with historical data.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

IBM Big Data Hub

NOVEMBER 30, 2022

They had a small IT team and needed a powerful tool that could handle a variety of uses, like tax provisioning in treasury, reporting and modeling for finance and still scale. With IT and line of business integrated, the final piece of the puzzle was making sure everyone was looking at and using the right data.

Reporting 52

Reporting Enterprise Finance Business Analytics 52

Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. There are many tactics to employ that might help – including data literacy; storytelling; use of our value pyramid/business value model; use of our decision intelligence model.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68

Jet Global

MAY 4, 2023

Integrate a reliable, transparent solution that leaves a rock solid paper trail and highlights data errors before they have impact. Data Collection Cloud-based operational transfer pricing software allows your tax team to easily gather your company’s worldwide data. Book a free demo.

Management 52

Management Cost-Benefit Reporting Software 52

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. Digital Marketing & Measurement Model. All that is great. That's it. Problematic, right? Management.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

APRIL 21, 2014

We wonder why data people are not loved. :). Strategy two… Book two hours with the senior most company leaders who will talk to you, and create the Digital Marketing and Measurement Model. Why is it so incredibly difficult to make people understand that last click attribution model is just idiot? Joseph Boisseaux.

Marketing Analytics 131

Marketing Analytics Marketing Analytics Measurement 131

datapine

NOVEMBER 19, 2019

This feature hierarchy and the filters that model significance in the data, make it possible for the layers to learn from experience. Thus, deep nets can crunch unstructured data that was previously not available for unsupervised analysis.

Technology 279

Technology Internet of Things IT IoT 279