Less Number Crunching & More Analysis With Automated Causal Discovery

It is a simple truth universally acknowledged that data analysts and marketing science professionals are always busy. They possess skills almost no one else in the organization has, and their analytical outputs are desperately craved by other researchers who aren't statisticians. Without their magic, we'd only be presenting regular bar charts and line graphs, and who wants to formulate strategic recommendations off just crosstabs? Not us. Not anybody.

Find the Solution by Understanding the Problem

Ask any data scientist what major obstacles they face and the lists are likely to be very similar:

  • More requests than time
  • More work than staff
  • and more projects than hours to attack each of them

When they're juggling between segmentation analysis, driver analysis, conjoint analysis, structural equation modeling, causal data analysis, and a whole lot of number crunching, it would be an understatement to say they could use more time and extra bandwidth. 

So the question becomes how can we maximize data analysis to create the best possible outputs? How can we help data scientists analyze complicated datasets more efficiently to propel the robust and visually compelling research stories we all want to deliver to stakeholders in the end? The solution is Inguo.

Reclaim Time for Implementation

Now, some analytical methods require more hands-on nurturing than others. Some, like correlation or "derived importance" runs are fast, but not incredibly insightful. Others require weeks of modeling, using a human brain, to produce the desired outputs. And don't even mention data cuts - most models are being developed for the total sample, as creating ones for males vs. females or Millennials vs. Gen X vs. Boomers, and can be near impossible to manage within scope. 

Instead, Inguo provides automated causal discovery/modeling that produces full causal graphs (DAGs) in minutes, without any hint of human bias. Need to rerun the graph by Segment B? Done. Want to see why people in the Southeast score your brand the way they do? No worries. Within moments you'll have the exact data and quality results you need. 

The speed, accuracy, and lack of bias gained by leveraging Inguo's causal discovery platform not only benefits data scientists but trickles down to researchers and stakeholders too. Check out what they've had to say! Data can be approached from different angles, shifted viewpoints, and varying demographics to reveal the "WHY" in datasets and provide the actionable insights needed to improve ROI. By using Inguo's automated causal discovery/modeling together data scientists and research teams are able to reclaim time in the early stages of research and analysis and focus more on interpretation and implementation. 

Breaking it Down to the Finest Features

As most data analysts will attest, the length of a study doesn't always equate to its difficulty. Correlations or regressions (driver analysis) don't require sophisticated statistical knowledge to produce but can take a lot of time. 

Inguo was built with this in mind and automates the painstaking, lengthy number-crunching tasks upfront to allow more time to spend thinking about the "So What?" questions. The platform foregoes probabilistic modeling or causal inference and automatically produces causal graphs in minutes with results that can be tweaked based on the user's expertise or assumptions. This way your team gets game-changing insights in only a fraction of the time. 

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