Smart Data Collective

DECEMBER 19, 2021

Besides, it offers data model creation, systematized data sets, developable web services, ML-powered algorithms, versatile use of data mining and so many other very efficient functionalities that make it very flexible and productive to use for Data Preprocessing. Why Choosing Python Over Other Technologies in FinTech?

Optimization 141

Optimization Machine Learning Data mining Statistics 141

CIO Business Intelligence

JANUARY 20, 2023

BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information. 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

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

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. To do so, the company started by defining the goals, and finding a way to translate employees’ behavior and experience into data, so as to model against actual outcomes.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Decision Management Solutions

AUGUST 18, 2020

The paper has some great discussion of this critical point to which I would add a couple of observations from our work with clients around the world: Use decision models to understand your decisioning problem and find the right technologies to automate it. Build a decision model using the Decision Model and Notation standard first.

Machine Learning 88

Machine Learning Data-driven Predictive Analytics Descriptive Analytics 88

Domino Data Lab

JUNE 2, 2019

We had data science leaders presenting about lessons learned while leading data science teams, covering key aspects including scalability, being model-driven, being model-informed, and how to shape the company culture effectively. Data science leadership: importance of being model-driven and model-informed.

Data Science 110

Data Science Data-driven Machine Learning Modeling 110

Domino Data Lab

JANUARY 21, 2021

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. The reward is clear — properly analyzed datasets result in better models, faster.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Business Over Broadway

MARCH 13, 2019

IBM Watson Studio , an end-to-end analytics solution to help you gain insights from your data, was designed for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. In this step we need to first import the data asset to the Modeler Flow.

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Sisense

JULY 23, 2021

“While data and analytics are nothing new to the Olympics — they’ve been used in some form or another for many, many years — what is new is the importance of using data to manage the evolving changing models for delivery of the Games,” Chris says. >>>Infused Using data to create a more modern Olympics. “We

Unstructured Data 105

Unstructured Data Internet of Things Data-driven Data Processing 105

Sisense

JUNE 30, 2020

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. Broadly, there are three types of analytics: descriptive , prescriptive , and predictive. Visualizations: past, present, and future.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Cloudera

MARCH 2, 2018

Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. 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

Sisense

SEPTEMBER 12, 2019

Originating with Gartner, this chart includes the analytic features needed for a full analytics strategy, and what our AI team believe to be the absolute future of analytics – Cognitive Analytics. . In order to know where to go, you must first find yourself on this chart.

Strategy 81

Strategy Dashboards Diagnostic Analytics Prescriptive Analytics 81

Smarten

NOVEMBER 26, 2023

This approach typically focuses on descriptive analytics based on historical data to answer the question “What happened?” or What is happening? The primary difference between traditional and modern BI lies in flexibility and accessibility.

Marketing 40

Marketing Business Intelligence Key Performance Indicator Prescriptive Analytics 40

Grooper

AUGUST 19, 2019

First, I have to understand the business model to actually see. It’s complex – you build a model, predict outcomes, now you have to convince business leaders to trust and believe in your model. We should use laymen terms to explain model and build trust in it. Then we can dive into creating a model.

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

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

For example, a computer manufacturing company could develop new models or add features to products that are in high demand. 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.”

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

CIO Business Intelligence

APRIL 9, 2025

Until we can connect data to the nuances of the business through active governance and trusted context with semantic models that mirror the business, our gut instincts will take priority. Absent governance and trust, the risks are higher as organizations adopt increasingly sophisticated analytics.

Enterprise 102

Enterprise Analytics Experimentation Machine Learning 102

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. These advanced analytics become easy for users to apply in their own analyses.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52