Smart Data Collective

AUGUST 7, 2019

Big data has the power to transform any small business. One study found that 77% of small businesses don’t even have a big data strategy. If your company lacks a big data strategy, then you need to start developing one today. Using Big Data to Fix Your Biggest Problems as a Business Owner.

Data Analytics 100

Data Analytics Analytics Big Data Data Lake 100

CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. Data Science Dojo. Due to the short nature of the course, it’s tailored to those already in the industry who want to learn more about data science or brush up on the latest skills.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

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? It’s also necessary to understand data cleaning and processing techniques.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

BizAcuity

APRIL 1, 2023

1] With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. There are a number of open-source ML platforms like KNIME that can also be leveraged to detect and predict suspicious behavior.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

IBM Big Data Hub

AUGUST 22, 2023

Digital twins and integrated data For the presentation layer, you can leverage various capabilities, such as 3D modeling, augmented reality and various predictive model-based health scores and criticality indices. Consider some of the examples of use cases from our clients in the industry: Visual insights.

Unstructured Data 74

Unstructured Data Consulting Visualization IoT 74

BizAcuity

JANUARY 18, 2023

With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. Exploratory Data Analysis (EDA). Predictive modeling for flagging suspicious activity. These include-.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

Domino Data Lab

JULY 28, 2021

Data science is a field that uses math and statistics as part of a scientific process to develop an algorithm that can extract insights from data. All models are not made equal. At this stage, data scientists begin writing code for computation and model-building.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

JUNE 20, 2022

Otis One’s cloud-native platform is built on Microsoft Azure and taps into a Snowflake data lake. IoT sensors send elevator data to the cloud platform, where analytics are applied to support business operations, including reporting, data visualization, and predictive modeling.

Internet of Things 105

Internet of Things IoT Manufacturing Data Lake 105

Sisense

MARCH 27, 2019

Real time business intelligence is the use of analytics and other data processing tools to give companies access to the most recent, relevant data and visualizations. To provide real-time data, these platforms use smart data storage solutions such as Redshift data warehouses , visualizations, and ad hoc analytics tools.

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

CIO Business Intelligence

OCTOBER 13, 2023

Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

FEBRUARY 14, 2024

Creative AI use cases Create with generative AI Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 82

Cost-Benefit Insurance Unstructured Data Machine Learning 82

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

MAY 17, 2024

Use case 1: AI for modernization and business model expansion AI-powered tools can be incredibly valuable in optimizing and modernizing business operations throughout the customer journey, but it is critical in the commerce continuum.  The applications of AI in commerce are vast and varied.

B2B 64

B2B Interactive Optimization Forecasting 64

IBM Big Data Hub

AUGUST 28, 2023

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

IBM Big Data Hub

AUGUST 14, 2023

With QRadar EDR, security analysts can leverage attack visualization storyboards to make quick and informed decisions. With ML analytics models, your organization can gain additional insight into user behavior with predictive modeling and baselines of what is normal for a user.

Cost-Benefit 55

Cost-Benefit Machine Learning Consulting Data mining 55

Smart Data Collective

SEPTEMBER 25, 2019

Big data has been billed as being the future of business for quite some time. Analysts have found that the market for big data jobs increased 23% between 2014 and 2019. The impact of big data is felt across all sectors of the economy. However, the future is now. Choose a Career. Learn How To Be Practical.

Big Data 80

Big Data Data Science Predictive Modeling Marketing 80

AWS Big Data

MARCH 7, 2023

And AWS Data Exchange helps publish your data to third parties for consumption through AWS Marketplace. It enables you to create interactive dashboards, visualizations, and advanced analytics with ML insights. Amazon SageMaker is the ML platform for all your data science workload needs.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Smart Data Collective

SEPTEMBER 26, 2021

Working with massive structured and unstructured data sets can turn out to be complicated. It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. So, let’s have a close look at some of the best strategies to work with large data sets. It’s much easier to work with graphs.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task. ref: [link].

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

AWS Big Data

JANUARY 25, 2023

An Amazon Personalize job predicts for each line of input data (restaurants and restaurant articles) and produces ML-generated recommendations in the designated S3 output folder. The recommendation records are surfaced using interaction data, product data, and predictive models.

Visualization 77

Visualization Interactive Modeling Machine Learning 77

IBM Big Data Hub

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

Machine Learning 74

Machine Learning Modeling Deep Learning Predictive Modeling 74

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data Warehouse 97

Data Warehouse Optimization Dashboards Interactive 97

Alation

APRIL 14, 2022

A useful feature for exposing patterns in the data. Visual Profiling. Supports the ability to interact with the actual data and perform analysis on it. A cloud environment with such features will support collaboration across departments and across common data types, including csv, JSON, XML, AVRO, Parquet, Hyper, TDE, and more.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

AWS Big Data

NOVEMBER 14, 2023

To continue innovating to be the easiest streaming data service to use, we actively listen to our customer use cases. Canva is an online design and visual communication platform. The probability results are also stored in Amazon S3 to continuously retrain the model in SageMaker.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 105, -17) or even “Python” (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

The Unofficial Google Data Science Blog

MARCH 31, 2016

Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. Often our data can be stored or visualized as a table like the one shown below.

Modeling 40

Modeling Statistics Advertising Testing 40

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. The data science team needs to know and to use that data which the BI team considers to be most important.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

datapine

NOVEMBER 27, 2019

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2) Data Discovery/Visualization. We all gained access to the cloud.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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. Mobile Analytics.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. For example, migrating customer data from an on-premises database to a cloud-based CRM system.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52