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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

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AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Jen Stirrup

If you’ve previously done work in SQL Server Analysis Services, you will know that Analysis Services had data mining functionality. Excel specialists may know that Excel also has a series of Data Mining Add-ins. You may even have used the add-in Solver , which has neural net technology behind the scenes.

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Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

datapine

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

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3 new steps in the data mining process to ensure trustworthy AI

IBM Big Data Hub

To help data scientists reflect and identify possible ethical concerns the standard process for data mining should include 3 additional steps: data risk assessment, model risk assessment and production monitoring. Data risk assessment. Detecting and defining bias and unfairness isn’t easy.

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Analytics Changes the Calculus of Business Tax Compliance

Smart Data Collective

Big Data Tools Make it Easier to Keep Records Newer tax management tools use sophisticated data analytics technology to help with tax compliance. According to a poll by Dbriefs, 32% of businesses feel data quality issues are the biggest obstacle to successfully using analytics to address tax compliance concerns.

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Data Cleaning Guide: Saving 80% of Your Time to Do Data Analysis

FineReport

Experienced data analysts know that a good data analyst must be a data cleaning expert. You must know that in the entire data analysis process, whether it is time or effort, data cleaning accounts for about 80%. Data Quality Guidelines. Data Cleaning in Python.

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Take Your SQL Skills To The Next Level With These Popular SQL Books

datapine

One of the best beginners’ books on SQL for the analytical mindset, this masterful creation demonstrates how to leverage the two most vital tools for data query and analysis – SQL and Excel – to perform comprehensive data analysis without the need for a sophisticated and expensive data mining tool or application.