CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. from 2022 to 2028.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

Rocket-Powered Data Science

AUGUST 1, 2018

As Dez Blanchfield once said , “You don’t need a data scientist to tell you big data is valuable. ” A series of articles that drive home the all-important value of data is being published on the DataMakesPossible.com site. The site’s domain name says it all: Data Makes Possible!

Big Data 105

Big Data Machine Learning Data-driven Data Science 105

Smart Data Collective

NOVEMBER 24, 2020

The strategic decision-making in the future of business intelligence will be shaped by faster reports, deeper data insights, broader areas of data collection. BI software will gauge internal data on performance, sales and marketing, social media and other sources to build actionable recommendations for your business.

Business Intelligence 130

Business Intelligence Visualization Dashboards Prescriptive Analytics 130

Smart Data Collective

FEBRUARY 28, 2023

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. Machine learning technology has already had a huge impact on our lives in many ways. There are numerous ways that machine learning technology is changing the financial industry. What is risk parity?

Machine Learning 84

Machine Learning Risk Cost-Benefit Marketing 84

Smart Data Collective

SEPTEMBER 9, 2020

Analysis of medical data collected from different groups and demographics allows researchers to understand patterns and connexions in diseases and identify factors that increase the efficacy of certain treatments. The post 5 Ways AI Technology Is Changing The Future Of Human Society appeared first on SmartData Collective.

Technology 110

Technology Data mining Insurance Machine Learning 110

CIO Business Intelligence

MARCH 29, 2022

It isn’t practical to save all your data, but it is important to realize data may be valuable for other projects. You lose that add-on value when you throw data away. . This type of data waste results in missing out on the second project advantage. 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

Alation

JUNE 16, 2021

Alation … [offers a] dedicated data catalog… while others include this functionality as a part of a broader (e.g., Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors. business intelligence) solution. We’re in good company!

Reporting 52

Reporting Business Intelligence Data Governance Metadata 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

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. This is “table stakes” for any data governance program!).

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

Insight

MARCH 27, 2019

It is done by artificially producing new data by applying transformations on an original dataset such as rotation, cropping, occlusion, etc. To address this problem, Google published AutoAugment last year, which discovers optimized augmentations for the given dataset using reinforcement learning.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

IBM Big Data Hub

AUGUST 7, 2023

2,3 When clinical trials are prematurely discontinued due to trial site underperformance, the research questions remain unanswered and research findings end up not published. This is in line with existing sector benchmarks.

Optimization 55

Optimization Forecasting Data-driven Strategy 55

Sisense

OCTOBER 15, 2020

As real-time analytics and machine learning stream processing are growing rapidly, they introduce a new set of technological and conceptual challenges. Every data professional knows that ensuring data quality is vital to producing usable query results. Click it and select the live model you just published.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. Data Collection The AIgent leverages book synopses and book metadata.

Modeling 59

Modeling Metadata Publishing Sales 59

Jedox

JULY 23, 2019

One of the biggest challenges of automation (Robotic Process Automation) and artificial intelligence/machine learning technologies is our current mindset. AI-based machine learning and predictive analytics will start to give us more powerful crystal balls. Once cleansed, its possible to enrich the data.

Technology 52

Technology Forecasting Finance Data-driven 52

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. A weighted nearest neighbor algorithm for learning with symbolic features. Quinlan, J.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Alation

FEBRUARY 13, 2020

In his book titled “The Fourth Industrial Revolution,” Klaus Schwab describes the age as, “characterized by a much more ubiquitous and mobile internet, by smaller and more powerful sensors that have become cheaper, and by artificial intelligence and machine learning.” Each of your organizations has something impossible to accomplish.

Machine Learning 52

Machine Learning Data-driven Big Data Technology 52

Smart Data Collective

MARCH 27, 2020

Last year, Jasmine Ronald, an author with Towards Data Science, wrote an article showing that big data is changing the direction of the ecommerce market in unexpected ways. This view is shared by experts at Big Commerce and other big data publishers. How Big Data is Changing the Future of eCommerce Software.

Software 84

Software Marketing Big Data Sales 84

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. Sign up to learn more about the Insight Fellows programs and start your application today.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

datapine

AUGUST 29, 2022

Due to this book being published recently, there are not any written reviews available. 4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

The Unofficial Google Data Science Blog

JULY 18, 2023

Measurement challenges Assessing reliability is essentially a process of data collection and analysis. To do this, we collect multiple measurements for each unit of observation, and we determine if these measurements are closely related. Both published articles in the same volume of the British Journal of Psychology.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

IBM Big Data Hub

APRIL 12, 2023

In fact, the standards for the modern state-of-the-art networks suggest 10 ms collection interval for the near-real time Use Cases. Given that the typical collection period in the legacy networks is 15 minutes (900.000 ms), data collection speed must become 90.000 times faster.

Data Processing 80

Data Processing KPI Optimization Analytics 80

Occam's Razor

OCTOBER 12, 2017

The first two are from editions of my newsletter, The Marketing – Analytics Intersect (it goes out weekly, and is now my primary publishing channel, sign up!). Intro to Machine Learning. Machine Learning. Deep Learning. The post has three clusters of advice. The answer was, yes.).

Marketing 136

Marketing Analytics Machine Learning Strategy 136

Jet Global

MAY 9, 2024

Data Migration Pipelines : These pipelines move data from one system to another, often for the purpose of upgrading systems or consolidating data sources. For example, migrating customer data from an on-premises database to a cloud-based CRM system. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

NOVEMBER 15, 2021

The rise of artificial intelligence and robotic process automation gives a hint as to where machine learning capacity is headed towards. The use of specialized software can help your organization with the collection of data pertaining to KPIs and its reporting.

Metrics 52

Metrics Reporting KPI Risk Management 52

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52