O'Reilly on Data

OCTOBER 22, 2020

“ “ You can interrogate anyone, no matter what their state of being. A recent discussion led us down the path of our favorite questions: what they are, why they’re useful, and when they don’t work so well. They will get to what happened, along with when and how and why. What is the most important?

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Smarten

MAY 6, 2022

Key Influencer Analytics: What Is It and Why is It Important? No matter the size of your business or in what industry your market is involved, the concept of analytics is more important today than ever before. So, what is Key Influencer Analytics and why is it so important to your business? Key Influencer Analytics: Defined.

Analytics 52

Analytics IT Metrics Machine Learning 52

Ontotext

JANUARY 10, 2024

So, why do we recommend knowledge graphs, which are perceived to be complex, to our customers? No matter what machine learning or graph algorithms are used, they cannot uncover dependencies if the corresponding “signals” are missing. Unless you already have ET riding with you.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Data Science 101

OCTOBER 15, 2023

Those vendors are safe due to established revenue elsewhere, but they are under pressure to publish, and you can set your watch to press releases (and generally well meaning if muddled media interpretation) at various cycles relating to public company reporting. And all of this gives context to why open source is the greatest path into QC.

Risk 107

Risk Marketing Consulting Technology 107

Smart Data Collective

AUGUST 10, 2021

Here’s more on why data is so important for companies and the top 5 analytics tools they’re using this year to stay ahead of the curve and their competition. Unlike print or television ads, which are the same no matter who sees them, it’s possible to create a more personalized advertising experience with digital mediums.

Analytics 129

Analytics Data-driven Advertising Data Science 129

IBM Big Data Hub

SEPTEMBER 12, 2023

Why is this a myth? The variety of technology in use also means you won’t always have a subject matter expert (SME) on hand to assist in the setup and configuration of new applications. Observing and interpreting data manually can lead to inconsistencies and oversight, potentially causing critical issues to be overlooked.

Data-driven 69

Data-driven Cost-Benefit Predictive Analytics Machine Learning 69

FineReport

MARCH 31, 2024

Acting as a comprehensive solution, the best BI tools collect and analyze company data to generate easily interpretable graphs, reports, and charts , leveraging advanced data mining, analytics, and visualization techniques. Why is Choosing the Best BI Tools Important?

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Jen Stirrup

FEBRUARY 24, 2024

In simple terms, Artificial Intelligence allows machines to emulate human cognitive functions like learning and problem-solving. While Artificial Intelligence may seem complicated under the hood, the basic premise is that it enables machines to achieve human-like cognitive functions at much more remarkable speed and scale.

Cost-Benefit 70

Cost-Benefit Data-driven Testing Risk 70

The Unofficial Google Data Science Blog

APRIL 19, 2021

This post explains why calibration matters, and how to achieve it. Why calibration matters What are the consequences of miscalibrated models? Intuitively, you want to have calibration so that you can interpret your estimated probabilities as long-run frequencies. This shows why $Pr(mathrm{Spam}) = 0.1$

Modeling 122

Modeling IT Metrics Testing 122

Jen Stirrup

NOVEMBER 15, 2022

Why do organizations get stuck with their data? There is usually a focus on the correct data, which may be a matter of ensuring which one of the many different data stores is selected that has the data as it may be duplicated across various sources and analytical systems. Information is pretty thin stuff, unless mixed with experience.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Ontotext

FEBRUARY 26, 2024

Why do risk and opportunity events matter? Using machine learning, RED indicates the impact of events on stock prices. Without it is impossible both to use graph data to interpret the new events and to extend the graph with new relationships. What they cannot do well is entity linking.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Smarten

JUNE 10, 2022

Why You Need Both BI Tools and Augmented Analytics and What to Consider When Selecting a Vendor ! As you review the list of predictions above, note that traditional and modern BI tools and Augmented Analytics with Natural Language Processing (NLP) and machine learning seems destined to co-exist for the foreseeable future.

Slice and Dice 52

Slice and Dice Analytics Key Performance Indicator Business Intelligence 52

AWS Big Data

FEBRUARY 20, 2024

In this post, we discuss how to architect a near-real-time analytics solution with AWS managed analytics, AI and machine learning (ML), and database services. Modeling your data here is very important so DynamoDB can horizontally scale based on a partition key, which is again why it’s a good fit for a transactional store.

Visualization 97

Visualization Cost-Benefit Optimization B2B 97

Ontotext

JUNE 10, 2021

This is why data-driven companies like the FAANGs, global pharma brands and the financial industry have long ago switched to graph databases. Not Every Graph is a Knowledge Graph: Schemas and Semantic Metadata Matter. Relational databases benefit from decades of tweaks and optimizations to deliver performance.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot

JUNE 17, 2021

This search for mathematical formulas makes Eureqa different from other machine learning algorithms. Why Eureqa is a Big Deal for DataRobot Customers. In such situations, traditional supervised machine learning models may be unable to learn.) Eureqa will build machine learning corrections from there.

Modeling 52

Modeling Experimentation Machine Learning Measurement 52

Domino Data Lab

JULY 22, 2019

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”. The idea of being able to use machines to crunch data that was still relatively new.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. On the one hand, basic statistical models (e.g.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

datapine

MARCH 31, 2022

1) Why Shift To A BI Career? Why Shift To A Business Intelligence Career? For instance, you will learn valuable communication and problem-solving skills, as well as business and data management. This could involve anything from learning SQL to buying some textbooks on data warehouses. Table of Contents.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Octopai

MARCH 24, 2020

By unifying different data streams on one platform, a better interpretation of that information is possible. Let’s look at why each is important to building smarter business intelligence: 1. It gives the data in your system definition and lets algorithms and machine learning programs process it. Watch the webinar!

Metadata 43

Metadata Cost-Benefit Business Intelligence ROI 43

Alation

FEBRUARY 2, 2023

They know how to interpret and harness it effectively. Many are subject matter experts for a particular kind of data, which enables them to spot anomalies in that data quickly, understand the root cause, and resolve the issue. And it’s something that can be learned from a book or in a classroom.

Management 52

Management Data-driven Visualization Strategy 52

Smart Data Collective

APRIL 18, 2019

Why AI Matters. AI, which can also be spoken about in terms of machine learning, is all about pattern recognition. Depending on the type of imaging or test, AI may actually be better at reading and interpreting test results than individual doctors.

Risk 71

Risk Testing Software Insurance 71

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. and dig into details about where science meets rhetoric in data science. Modernists and structuralists beware. I got this.”.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Jedox

JULY 23, 2019

One of the biggest challenges of automation (Robotic Process Automation) and artificial intelligence/machine learning technologies is our current mindset. These are often not “out of the box” solutions and will require us to spend time learning new skills in process automation, analytics and financial modeling. Crystal ball.

Technology 52

Technology Forecasting Finance Data-driven 52

Smarten

AUGUST 9, 2017

Augmented Analytics automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. Why is all of this important to your organization? Original Post: What is Augmented Analytics and Why Does it Matter?

Analytics 40

Analytics IT Statistics ROI 40

The Unofficial Google Data Science Blog

JANUARY 31, 2017

By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. But at other times, matters are less clear. There are a number of reasons.

Machine Learning 73

Machine Learning Modeling Statistics Predictive Modeling 73

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

It explains why answering an innocuous question ("Which content verticals have low quality?") They involve the judgment to interpret data analysis in the context of the real world. They went through the many reasons why the Like Rate for a video may be an incomplete measure of the video's quality.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Domino Data Lab

JULY 1, 2020

Much like unstructured code, poorly organized notebooks can be hard to read and defeat the intended purpose of why you use notebooks, creating self-documenting readable code. What do your readers need to learn from it? First of all, plenty of workloads in a machine learning pipeline are fully deterministic (e.g.

Testing 116

Testing Data Science Machine Learning Data-driven 116

The Unofficial Google Data Science Blog

JULY 18, 2023

Doing this requires covering a bit of the history of measurement, since we hope to answer the question: “Why would anyone measure anything this way?” There are many different rubrics that can be used to interpret kappa values. We’ll refrain from this for now, but there are more details on interpretation below.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

The Unofficial Google Data Science Blog

JULY 17, 2018

Often such decisions are the responsibility of a separate machine learning (ML) system. This measure of web page value is on a meaningful linear scale, such that our freshness metric (a weighted average) has an intuitive interpretation. by BILL RICHOUX Critical decisions are being made continuously within large software systems.

Data Science 85

Data Science Snapshot Data Processing Optimization 85

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. Missing values can be filled in based on expert knowledge, heuristics, or by some machine learning techniques.

Testing 68

Testing Modeling Interactive Measurement 68

DataRobot Blog

APRIL 4, 2022

ROI gives a standard interpretation of whether a marketing activity was profitable and to compare efficiency across media channels or campaigns. A good explanation of how to interpret coefficients can be found here ). Originating in game theory, there is a reason why Shapley Value earned Lloyd Shapley a Nobel Prize in 2012.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Alation

MAY 24, 2022

To some it means profiling statistics, like min, max, median, and null; to others it means producing an aggregate quality score that can be interpreted as good or bad. Some vendors leverage machine learning to build rules where others rely on manually declared rules. Customers: find more information on Alation Community.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

datapine

JANUARY 21, 2020

If you gather data, you need to analyze and report on it, no matter which industry or sector you operate in. The main goal of BI reports is to deliver comprehensive data that can be easily accessed, interpreted, and provide actionable insights. Online business intelligence and reporting are closely connected.

Business Intelligence 259

Business Intelligence Reporting Cost-Benefit Dashboards 259

Jet Global

MARCH 4, 2020

Why Your Company Should Be Using Manufacturing Specific KPIs to Stay Competitive. The Throughput KPI measures the production capabilities of a machine, line, or plant; also known as how much they can produce over a specified time period. This means collecting and interpreting data. Seems strange, right? Data, data, data.

Manufacturing 80

Manufacturing Metrics Reporting KPI 80

Sisense

SEPTEMBER 25, 2020

No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Interpreting better results: Statistical techniques allow users to make predictions for unseen data, more easily improving the accuracy of output and results.

Statistics 98

Statistics Modeling Predictive Modeling Machine Learning 98

Alation

MAY 12, 2022

Today’s manufacturers need the help of artificial intelligence (AI) and machine learning (ML) to analyze large and complex datasets rather than spending valuable time searching across silos for the right data. It’s also too much for any single team of humans to possibly manage. Train and educate your manufacturing workforce.

Manufacturing 52

Manufacturing Data Governance Cost-Benefit IoT 52