datapine

MAY 28, 2019

What kind of database you’re currently working with and do you need various data connectors to unite all your flat files, databases, marketing analytics, social media, etc. Maximum security and data privacy. Reducing the reporting time. Challenges : Reducing IT involvement.

Business Intelligence 317

Business Intelligence KPI Dashboards Reporting 317

datapine

AUGUST 14, 2019

Therefore, there are numerous data science tools and techniques that provide scientists with an easier, more digestible workflow and powerful results. Our Top Data Science Tools. The tools for data science benefit both scientists and analysts in their data quality management and control processes.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

datapine

DECEMBER 4, 2019

As they expand, enterprises, organizations, and businesses of all sizes need a proper data framework to prepare and connect all the information they collect and, ultimately, extract value from it. Graph Analytics. Final on our list of business buzzwords for 2020 is the graph analytics.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Ontotext

JANUARY 6, 2022

Spotting Data Consistency Issues. We integrated only a bit of Transparency data, but have already found various data quality issues. This is possible because by semantically interlinking different types of data coming from different sources, we can look at the bigger picture and can easily see problems in the data.

Marketing 52

Marketing Data Quality Dashboards Visualization 52

datapine

FEBRUARY 17, 2023

That said, data and analytics are only valuable if you know how to use them to your advantage. Poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. In fact, poor data quality management currently costs businesses a combined total of $9.7 million per year.

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

Occam's Razor

APRIL 21, 2014

The mistake we make is that we obsess about every big, small and insignificant analytics implementation challenge and try to fix it because we want 99.95% comfort with data quality. We wonder why data people are not loved. :). When you feel you have enough, use it to buy time/money to go fix the configuration problems.

Marketing Analytics 132

Marketing Analytics Marketing Analytics Metrics 132

Andrew White

JANUARY 11, 2021

On end user clients calls, are you hearing a greater focus on use cases and greater need for prescriptive analytics, ex marketing analytics, sales analytics, healthcare, etc. where performance and data quality is imperative? Yes, prescriptive and predictive analytics remain very popular with clients.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52