Smarten

JUNE 25, 2018

In order to understand how best to make use of this algorithm; let’s look at some general examples, followed by some business use cases. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate for each applicant, based on the amount of risk. Use Case – 2.

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 27, 2018

Business Benefit: The bank can decide which customer segments are eligible for any type of loan and the customer segments that should be denied any loan, as they are likely to default. In this way, the riskier customers are identified easily and bank can avert the risk of delinquencies. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JULY 10, 2018

Use Case(s): Group loan applicants into high/medium/low risk based on attributes such as loan amount, installments, or employment tenure, organize customers into groups/segments based on similar traits, product preferences and expectations and more.

Analytics 40

Analytics Forecasting Statistics Sales 40

datapine

NOVEMBER 27, 2019

Over the past decade, business intelligence has been revolutionized. 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. Suddenly advanced analytics wasn’t just for the analysts.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. If anything, this focus has shifted to the ML or predictive model.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Smarten

JUNE 25, 2018

Business Benefit: By checking Q1, Q3 and Inter quartile range (Q3-Q1) values of each step of the process, the owner can determine which particular step has a scope of time reduction. Business Problem: A stock broker wants to analyze the price volatility of a stock as a measure of risk. 5) Standard Deviation/Variance.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

Depict Data Studio

SEPTEMBER 17, 2019

His experience includes evaluation and outcomes studies, ROI analysis, IBNR determination, predictive modeling, risk adjustment methodologies, advanced data visualization, dashboard design and implementation, database development and management, and identifying and evaluating trends and forces in data.

Visualization 40

Visualization Consulting Dashboards Reporting 40

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. ETL is primarily used for data warehousing and business intelligence applications. However, data pipelines and ETL are not synonymous.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52