IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 61

Machine Learning Data Science Statistics Deep Learning 61

datapine

MARCH 31, 2022

Being numbers and data-driven: There are many expectations when it comes to working with BI and data analytics. This will also require you to do some tedious work at times such as fixing formatting issues, labeling mistakes, tracking missing data, among others. SAS BI: SAS can be considered the “mother” of all BI tools.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

datapine

MARCH 25, 2022

Well, what if you do care about the difference between business intelligence and data analytics? The most straightforward and useful difference between business intelligence and data analytics boils down to two factors: What direction in time are we facing; the past or the future?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

datapine

NOVEMBER 27, 2019

The consequences of bad data quality are numerous; from the accuracy of understanding your customers to constructing the right business decisions. That’s why it is of utmost importance to start with utilizing the right key performance indicators – there are numerous KPI examples that can make or break the quality process of data management.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361