The Importance of Data Preprocessing in Machine Learning
While analyzing data, you must ensure that it has no errors. Mistakes give a false impression of the overall statistics of the data. To cut down errors, you need to prepare your data. Here's how.
All You Need To Know About Reverse ETL
ETL is an integral part of data warehousing. In addition to being complex, it can also be time-consuming. In recent years, reverse ETL has grown in popularity because it is simpler and faster to complete than traditional ETL.
Mistakes in Identity Resolution that could be Costing Your Business Customers
Are you struggling with identity resolution in your business? Learn about the common mistakes that marketers make and how to avoid them!
Data Chaos: What Is It And How Can It Be Resolved
With the voluminous inflow of big data in every enterprise today, data chaos is a reality. Managing data is a must requirement.
The Why, What and How of Web Analytics
The advent of standalone digital channels today has made Web Analytics a more all-encompassing term than SEO Analytics which focuses solely on search engine optimization.
Use Location Analytics to Improve Bottom Line of Businesses
Geospatial data can enhance a business' data assets and provide more valuable insights. Incorporating geographic information into performance analysis can bring to light new insights.
Detect Outliers in Data Wrangling: Examples and Use Cases
How to Detect Outliers in Data Wrangling: Examples and Use Cases Outliers is the term that strikes your mind whenever you speak about analyzing data. If you are planning to inspect any task to examine data sets, you can make assumptions about the methods used to produce
How to Improve Your Brand’s Net Promoter Score (NPS)
A Net Promoter Score is a key metric to measure what your customers think of your brand. There are many ways to improve this score.
How to Use Propensity Modeling to Predict Customer Behavior?
Propensity modeling is a statistical technique used to predict future events. Machine learning allows companies to build robust propensity models and make accurate forecasts. In marketing, for example, propensity models are used to predict customer behavior.