article thumbnail

What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

article thumbnail

Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

Low-latency data access and delivery (system requirement) is necessary for delivery of low-latency analytics products (business user requirement). Along with the massive growth in sensor data (including location-based and time-based streaming data), there have emerged some special analytics categories that are growing in significance.

Analytics 166
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

You can use third-party data products from AWS Marketplace delivered through AWS Data Exchange to gain insights on income, consumption patterns, credit risk scores, and many more dimensions to further refine the customer experience. Plan on how you can enable your teams to use ML to move from descriptive to prescriptive analytics.

article thumbnail

Decoding Data Analyst Job Description: Skills, Tools, and Career Paths

FineReport

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.

article thumbnail

Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers. Will this next trade return a profit?

article thumbnail

10 Best Big Data Analytics Tools You Need To Know in 2023

FineReport

.” This type of Analytics includes traditional query and reporting settings with scorecards and dashboards. Predictive Analytics assesses the probability of a specific occurrence in the future, such as early warning systems, fraud detection, preventative maintenance applications, and forecasting.

article thumbnail

Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

If your business is using big data and putting dashboards in front of analysts, you’re missing the point.”. Clearly in these situations, becoming model-driven addresses an existential risk and that’s the point Nick Elprin made in his keynote. Being model-driven is like using GPS.”. “If Because of compliance. Worse than flipping a coin!