Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you’re trying to solve will determine...

Bad data costs time and money. The majority of companies know that bad data is an issue, yet have no solid plan in place for eliminating bad data and its effects. In fact, one study concluded that 85...

Most data scientists do not manufacture drugs, and most pharmacologists are not also data scientists. Still, the pharmaceutical industry depends heavily on data scientists – especially when it...

Data quality management has emerged as an increasingly important factor in determining the success of big data analytics and AI/machine learning initiatives. Even at a purely operational level, the...

Data quality helps you understand your data and ensure it’s accurate, consistent, and complete while a data governance framework allows you to proactively find and manage your data for better...

CRM data can be notoriously problematic. Personal and business information is in a constant state of flux. Customers or prospects often move to new locations, get new email addresses or phone...

Data integrity is defined as data with accuracy, consistency, and context for confident decision-making. Achieving true data integrity calls for technical capabilities such as data integration, data...

What forces are driving expanded adoption of data governance programs? Data governance has become a primary concern of companies everywhere. And with good reason – businesses must have strong data...

You want to make the most of your big data. However, data quality is paramount to achieving that goal. Implementing an effective data quality management program ensures that your data is of the...