CIO Business Intelligence

DECEMBER 6, 2023

OpenAI’s recent announcement of custom ChatGPT versions make it easier for every organization to use generative AI in more ways, but sometimes it’s better not to. But that’s not the only problem you might run into. But this wasn’t the first time Bing’s AI news added dubious polls to sensitive news stories.

IT 125

IT Risk Management Interactive Modeling 125

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Bureau of Labor Statistics predicts that the employment of data scientists will grow 36 percent by 2031, 1 much faster than the average for all occupations. Bureau of Labor Statistics.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting.

Data Science 124

Data Science Machine Learning Predictive Modeling Experimentation 124

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. If you’re an AI product manager (or about to become one), that’s what you’re signing up for. Identifying the problem. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about.

Marketing 361

Marketing Experimentation Metrics Testing 361

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. Deep integration with SAP warehouse and SCM; low-code, no-code features.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. In this post we discuss the problem, a solution, and practical considerations.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

JUNE 14, 2023

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

Big Data 121

Big Data Data Analytics Analytics Predictive Modeling 121

IBM Big Data Hub

MAY 20, 2024

As algorithm discovery and development matures and we expand our focus to real-world applications, commercial entities, too, are shifting from experimental proof-of-concepts toward utility-scale prototypes that will be integrated into their workflows. And they perform computations in a way that more closely mirrors nature itself.

Data Processing 97

Data Processing Optimization Experimentation Enterprise 97

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML).

Management 327

Management Machine Learning Experimentation Metrics 327

Data Science 101

JANUARY 15, 2020

Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Bias can cause a huge error in experimentation results so we need to avoid them. Let us understand this in brief. This is called Hawthorne Effect.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Business Over Broadway

AUGUST 4, 2020

Experimentation and iteration to improve existing ML models (39%). In prior research, I found that data professionals who self-identified as Researchers have a strong math/statistics/research skill set. The entire list of activities (shown in Figure 1) were: Analyze and understand data to influence product or business decisions (63%).

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. ” This quandary often comes with an accompanying worry: “Are we spending too much money on cloud computing?” Why move to cloud?

Modeling 91

Modeling Cost-Benefit Optimization Digital Transformation 91

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

Experimentation 103

Experimentation Testing Optimization Measurement 103

CIO Business Intelligence

JULY 6, 2023

After completing MTech from Indian Statistical Institute, I started my career at Cognizant. Scarcely is an institution as important to a nation as a competent and impartial media house. The media conglomerate completed a hundred years in 2022. He is guiding digital transformation across the organization for the last 4 years.

Digital Transformation 89

Digital Transformation Experimentation Recreation/Entertainment Technology 89

Ontotext

FEBRUARY 14, 2024

Based on our experience, we’ve found that there are some common stumbling blocks. Let’s have a look at those. Why You need to make sure the people in your organization know the why of the knowledge graph project. If they know why they are doing it, they’ll be in a better position to deliver what they’ve been asked to do.

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

DataRobot Blog

JANUARY 17, 2023

Not actually being a machine learning problem: Value-at-Risk modeling is the classic example here—VaR isn’t a prediction of anything, it’s a statistical summation of simulation results. As discussed, we massively accelerate that process of experimentation. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). But data, at best, can only be a powerful vehicle, or a reliable GPS system. Well, that’s obvious, you say.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Rocket-Powered Data Science

JULY 7, 2019

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). They cannot process language inputs generally. Edge Computing (and Edge Analytics): Industry 4.0: Industry 4.0

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two. Data scientist requirements.

Unstructured Data 129

Unstructured Data Data Analytics Analytics Structured Data 129

FineReport

DECEMBER 11, 2019

– Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. For a while, everyone seems to have begun to learn data analysis. New York Times.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

CIO Business Intelligence

JUNE 28, 2023

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs. Belcorp operates under a direct sales model in 14 countries.

Digital Transformation 79

Digital Transformation Cost-Benefit Informatics Data mining 79

DataRobot Blog

JULY 28, 2022

Initially, the customer tried modeling using statistical methods to create typical features, such as moving averages, but the model metrics (R-square) was only 0.5 The accuracy of machine learning models is highly dependent on the quality of the training data. can make it difficult to analyze the actions of ordinary users.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Smarten

DECEMBER 11, 2017

Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation. A business user with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

Experimentation 65

Experimentation Statistics Visualization Dashboards 65

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Many companies find that they have a treasure trove of data but lack the expertise to use it to improve ROI. AI can help them wrangle the data that’s collected and quickly answer important questions.

ROI 52

ROI Machine Learning Experimentation Data Science 52

Jet Global

APRIL 27, 2023

Understand Your Research and Development Manufacturing accounted for 58 percent of US domestic research and development (R&D) spending in 2019, according to the  National Center for Science and Engineering Statistics. That In short, international taxes of supply chains now come with a plethora of considerations that weren’t there before.

Manufacturing 52

Manufacturing Marketing Experimentation Reporting 52

Octopai

MAY 2, 2022

Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. Data analytics ain’t what it used to be. You’re providing data analytics products. . Lean manufacturing.

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Business Over Broadway

FEBRUARY 21, 2021

As such, data science requires three broad skill sets , including subject matter expertise, statistics/math and technology/programming. Experimentation and iteration to improve existing ML models (25%). The practice of data science is about extracting value from data to help inform decision making and improve algorithms.

Machine Learning 60

Machine Learning Statistics Data Science Experimentation 60

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. The update sheds light on what AI adoption looks like in the enterprise— hint: deployments are shifting from prototype to production—the popularity of specific techniques and tools, the challenges experienced by adopters, and so on. But what kind? It ranks high (No.

Enterprise 307

Enterprise Deep Learning Data Governance Risk 307

Sisense

JANUARY 21, 2020

This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term. This kind of analysis, in most cases, can lead to insights that can be later translated to business actions.

Testing 104

Testing Risk Data-driven Statistics 104

Occam's Razor

MARCH 23, 2021

Data Warehousing. And, a thousand other silos (depending on your company size). One outcome of this reality is that while every team works hard to do their very best work, it is rare that they earn strategic influence from their work. That’s not really surprising, if your view of your scope is narrow… Your impact will be narrow as well.

Analytics 131

Analytics Scorecard Strategy Testing 131

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. Like other authors, they argue that causal inference has been neglected by traditional statistics and some scientific disciplines. However, I still call myself a data scientist.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. smart thermostat). an edge server). AWS IoT and Azure IoT).

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

IBM Big Data Hub

SEPTEMBER 25, 2023

The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions.

OLAP 88

OLAP Data Lake Data-driven Snapshot 88