IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Cloudera

SEPTEMBER 22, 2022

To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Step two: expand machine learning and AI. Here too, I recommend an evolutionary, stepped approach for advancing your capabilities while learning as you go.

Analytics 102

Analytics Insurance IoT Risk 102

CIO Business Intelligence

JANUARY 20, 2023

Business intelligence vs. business analytics Business analytics and BI serve similar purposes and are often used as interchangeable terms, but BI should be considered a subset of business analytics. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

Decision Management Solutions

AUGUST 18, 2020

DDPs accomplish this by providing a suite of capabilities that enable business subject-matter experts to define decision logic, incorporate data-driven decision intelligence technologies such as machine learning (ML), govern change, and deploy digital decisions within business applications. Does that change the offers we make?

Machine Learning 88

Machine Learning Data-driven Predictive Analytics Descriptive Analytics 88

CIO Business Intelligence

FEBRUARY 6, 2025

Even basic predictive modeling can be done with lightweight machine learning in Python or R. These traditional tools are often more than sufficient for addressing the bread-and-butter analytics needs of most businesses. Tableau, Qlik and Power BI can handle interactive dashboards and visualizations. You get the picture.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

Domino Data Lab

JUNE 2, 2019

Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.

Data Science 110

Data Science Data-driven Machine Learning Modeling 110

IBM Big Data Hub

JULY 27, 2022

Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following: Collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications and performance-monitoring tools. Predictive analytics to show what will happen next.

Prescriptive Analytics 93

Prescriptive Analytics Descriptive Analytics Data-driven Cost-Benefit 93

Business Over Broadway

MARCH 13, 2019

IBM is using the power of its Watson Studio platform to extend the power of AI to people who fall outside the realm of data science, machine learning and AI experts. IBM Watson Studio is an end-to-end analytics solution to help you gain insights from your data. The first step is to load the data into Watson Studio.

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Sisense

JUNE 30, 2020

When BI and analytics users want to see analytics results, and learn from them quickly, they rely on data visualizations. Analytics acts as the source for data visualization and contributes to the health of any organization by identifying underlying models and patterns and predicting needs.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Cloudera

MARCH 2, 2018

The private sector already very successfully uses data analytics and machine learning not only to realise efficiency gains but also – even more importantly – to create completely new services and business models. Gain improved intelligence on operating context and needs through expanded use of descriptive analytics techniques.

Data-driven 43

Data-driven Prescriptive Analytics Machine Learning Descriptive Analytics 43

Domino Data Lab

JANUARY 21, 2021

It’s worth noting that there is a landscape of proprietary tools dedicated to producing descriptive analytics in the name of business intelligence. And more generally, one of the core challenges of machine learning with unstructured data is that preprocessing often goes directly from a raw, unstructured state (e.g.,

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Grooper

AUGUST 19, 2019

I have learned that trust is required to take data modeling in production. What is machine learning? Machine learning. We’re basically making the machine learn by itself. The ways we do that are called machine learning algorithms. What are some examples of effective machine learning?

Machine Learning 40

Machine Learning Metrics Descriptive Analytics Testing 40

BizAcuity

MARCH 31, 2022

Artificial Intelligence Analytics. AI can be applies to all 3 major types of analytics: Descriptive Analytics: The entire journey of the descriptive and diagnostic analytics process includes data extraction, data aggregation and data mining; 3 applications where AI is widely used to reduce costs, and eliminate complex actions.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

CIO Business Intelligence

MARCH 27, 2024

A questa base informativa sta affiancando, in misura crescente, altre tecnologie data-oriented, come i droni e le tecnologie satellitari per i rilievi e per l’arricchimento del patrimonio informativo regionale e l’IA, nella forma di machine learning per le analisi predittive sui big data regionali nelle diverse aree. “Il

Data Governance 52

Data Governance Data Lake Data Strategy Data-driven 52

FineReport

MARCH 24, 2024

Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue.

Statistics 52

Statistics Data mining Visualization Reporting 52

FineReport

APRIL 26, 2023

The Big Data ecosystem is rapidly evolving, offering various analytical approaches to support different functions within a business. Descriptive Analytics is used to determine “what happened and why.” ” This type of Analytics includes traditional query and reporting settings with scorecards and dashboards.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Rocket-Powered Data Science

OCTOBER 6, 2023

In the enterprise, sentinel analytics is most timely and beneficial when applied to real-time, dynamic data streams and time-critical decisions. The analytics triage is critical, to avoid alarm fatigue (sending too many unimportant alerts) and to avoid underreporting of important actionable events. Pay attention!

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Statistics 157

datapine

JANUARY 31, 2022

Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was. BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Jet Global

MAY 1, 2023

Predictive, the Up but Not Coming Over time, analytics grow and level up. Leading research and consultancy company, Gartner describes the path that businesses take as they move to higher levels: Descriptive Analytics: Describe what happened (e.g., Diagnostic Analytics: No longer just describing.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52