Data activation maximizes the value of existing data infrastructure by reducing manual work, increasing reach, and enabling data teams to drive more strategic impact.

Picture this: As you’re analyzing sales data in Qlik or in Power BI, you spot an obvious error in a customer name and think “I wish I could just fix this right here instead of sending another email to IT.” If this sounds familiar, you’re not alone. The good news is that the business intelligence (BI) world is finally listening. Gone are the days when BI platforms were solely for consuming and analyzing historical data.

The Wisdom of Crowds Business Intelligence Market Study from Dresner Advisory Services is one of the most recognized annual benchmarks for BI and analytics. It captures how organizations are investing, what priorities they set, and which technologies are delivering the most value. The 2025 edition highlights steady growth in BI spending and shifts in user objectives, from efficiency and revenue impact to security and data quality.

Before jumping into AI data cleaning directly, let’s first understand data cleaning itself. Data cleaning, also known as data scrubbing, is a critical data preparation step where organizations remove inconsistencies, errors, and anomalies to make datasets ready for analysis. The cleaning process may involve actions like removing null values, correcting formatting, fixing syntax errors, eliminating duplicate data, or merging related fields like City and Postal Code.

Everything you need to know about dbt explained; plus how SQL-based modeling helps teams turn raw data into analytics-ready outputs.