CIO Business Intelligence

MARCH 19, 2025

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59

IT Data Governance Data-driven Metrics 59

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

datapine

DECEMBER 4, 2019

Moreover, as most predictive analytics capabilities available today are in their infancy — they have simply not been used for long enough by enough companies on enough sources of data – so the material to build predictive models on was quite scarce. Last but not least, there is the human factor again.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Juice Analytics

MARCH 11, 2021

However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) Pain Points We’ve noticed a temptation with data products to forget the cardinal rule of any product: it needs to solve a specific problem.

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Visualization Predictive Modeling Reporting Interactive 128

CIO Business Intelligence

DECEMBER 19, 2024

In especially high demand are IT pros with software development, data science and machine learning skills. While crucial, if organizations are only monitoring environmental metrics, they are missing critical pieces of a comprehensive environmental, social, and governance (ESG) program and are unable to fully understand their impacts.

IT 52

IT IoT Machine Learning Optimization 52

Business Over Broadway

SEPTEMBER 27, 2018

In our world of Big Data, marketers no longer need to simply rely on their gut instincts to make marketing decisions. Through the application of data science principles, marketing professionals now have a way of making evidence-based decisions to improve their marketing activities. underspecified) due to omitted metrics.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Birst BI

AUGUST 23, 2018

What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases? Automating data science tasks leaves room to build more AI applications with the same amount of data science resources. Source: Gartner (April 2018).

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

Smarten

FEBRUARY 17, 2022

Without understanding the shift in workflow, responsibilities and how the use of data will change the enterprise, it is unlikely that the business will succeed in its Citizen Data Scientist initiative.

Predictive Modeling 76

Predictive Modeling Digital Transformation Data Science Enterprise 76

AWS Big Data

MARCH 7, 2023

It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers. Popular consumption entities in many organizations are queries, reports, and data science workloads.

Analytics 97

Analytics Data Warehouse Data Lake Metadata 97

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. A variety of models are been trained in parallel. The Leaderboard of trained models—ordered based on your metric.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Smarten

APRIL 12, 2023

Smarten CEO, Kartik Patel says, ‘Smarten SnapShot supports the evolving role of Citizen Data Scientists with interactive tools that allow a business user to gather information, establish metrics and key performance indicators.’

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Domino Data Lab

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. Natural Language Processing.] together at Stanford University.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. What is machine learning?

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Domino Data Lab

APRIL 21, 2021

from sklearn import metrics. With this criterion in mind, we can define a distance metric to the top left corner of the curve and find a threshold that minimises it. Knowledge and Data Engineering, IEEE Transactions on, 21, 1263-1284. The class label is titled Class where 0 denotes a genuine transaction and 1 signifies fraud.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smarten

MARCH 13, 2024

For a number of years, Gartner and other technology research and analysis firms have predicted and monitored the growth of this phenomenon. In fact, Gartner predicted that, ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’

Analytics 52

Analytics Predictive Modeling Enterprise Strategy 52

Smarten

SEPTEMBER 9, 2024

These individuals may already be ‘power users’ of business applications and may have developed and reported or presented data to others with an eye toward clarifying their decision-making. Citizen Data Scientist candidates may also be IT team members who are interested in data science.

Consulting 52

Consulting Cost-Benefit Data Science Business Intelligence 52

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. Deep Dive into DataRobot Notebooks. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Smarten

NOVEMBER 8, 2022

By providing this course as a free online offering Smarten hopes to further support and encourage users and businesses to embrace the very real benefits of the Citizen Data Scientist approach to analytics and objective, data-driven metrics and results. About Smarten.

Business Intelligence 52

Business Intelligence Predictive Modeling Statistics Data-driven 52

CIO Business Intelligence

AUGUST 15, 2023

Whether a project aims to improve suicide prevention using data science or to create new revenue streams by reimagining an organization’s core business, CIO 100 Award winners demonstrate the innovative spirit of today’s IT in the face of rapidly evolving organizational challenges.

IT 98

IT Manufacturing IoT Cost-Benefit 98

Smarten

DECEMBER 3, 2023

‘Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. What is self-service analytics? We should probably explain before we move on.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Smarten

DECEMBER 3, 2023

Adopting Augmented Analytics: How to Get Started Gartner has predicted that, in the future ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’ What problems will it solve? What opportunities does it present? in the discussion.

Analytics 40

Analytics Data Warehouse Metrics Interactive 40

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

Domino Data Lab

OCTOBER 23, 2019

Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering. Gathering the Data. there is a list of data sources to extract and transform. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 1, 2021

Intrinsic methods – this technique is based on ANNs that have been designed to output an explanation alongside the standard prediction. Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. References.

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Modeling Deep Learning Machine Learning Knowledge Discovery 139

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more. 5 is labeled as low.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. This may give a more accurate representation in the variability of predictions.

Risk 67

Risk ROI Modeling Metrics 67

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. It also includes data validation and quality checks to ensure the accuracy and integrity of the data being processed.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Smarten

MARCH 20, 2025

These tools do not require IT skills or data science knowledge. When the team uses these tools, they can adopt a common language and techniques to work with IT and data scientists to create use cases and refine and share reports, formats and outcomes.

Prescriptive Analytics 62

Prescriptive Analytics Advertising Data-driven Statistics 62

CIO Business Intelligence

MAY 6, 2025

Predictive analytics: Turning insight into foresight Predictive analytics uses historical data and statistical models or machine learning algorithms to answer the question, What is likely to happen? This is where analytics begins to proactively impact decision-making. Whats holding us back?

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78