Pinch Me: How Inguo Delivers Accurate and Sophisticated Models Faster Than Any Other Analytic Method

In this era of seemingly unlimited data, there is great potential for unearthing intelligence that can substantially improve business decision-making, and that is through data analytics. Growing sources of data are available, from product development studies to brand trackers to customer satisfaction/CX research. So much so, we don't have the time to sort through it all. Up to 73% of data within an enterprise goes unused for analytics. While companies are collecting data at higher rates than ever, these same companies are not putting that data to good use with analysis. Data is only valuable when it can be interpreted properly. 

What is keeping data analysts from more productivity? Ask any statistician that you know; correlation and "derived importance" runs are quick (but not that insightful), while causal is incredibly hard. Analytic methods such as Structural equation modeling (SEM) or Bayesian Belief Net (BBN) are exceptionally time-consuming and laborious. Plus there is significant guesswork and a whole lot of number crunching - testing each model for goodness of fit requires a considerable amount of labor. Data scientists are typically running factor analyses before even beginning these types of analyses to have a more manageable set of variables to model, even while acknowledging this limits their ability to formulate pinpoint recommendations on the back end. This analysis can take weeks, as statisticians hypothesize how consumers feel about a given category, brand, or new product concept. In the end, data scientists spend the majority of their time producing outputs, not synthesizing what the data means to clients and internal stakeholders. Businesses are missing critical opportunities for positive groundbreaking innovation. 


Better Use of Time

Fortunately, there are ways to recognize shortfalls and leverage new technologies so that you can create sophisticated models in a fraction of the time it takes to use these other, antiquated methods. The answer? Automation and machine learning. 

Imagine if you could have a computer spit out causal models without any human bias in a handful of minutes. Businesses need time-bound decisions, and automation assists that need. While no amount of automation or machine learning can entirely displace the need for data scientists, they can significantly take the "headaches" out of the process. Automated data analysis through causal discovery with Inguo allows data and marketing scientists to be testing up to 500 variables and 500,000 records with a mix of categorical, binary and continuous data. Human-driven analytics tools cannot possibly create models with datasets this large or complex. Inguo explicitly cuts down on laborious number-crunching by trading in manual methods for automated modeling and simulation. Significantly speeding up workflows, Inguo allows data scientists to spend more time on the interpretation of graphs, fine-tuning, or other demanding projects. For smaller research companies that don't have many PhDs on staff, causal automation is a lucrative solution to produce sophisticated analysis that are premium-priced. 


No More "Unknown, Unknowns"

Have you ever heard the phrase "You don't know, what you don't know?"

A benefit of Inguo is that automated data analysis provides data scientists with the assistance needed to test for scenarios that they may not have ever considered. Immense volumes of data are gathered by businesses every day (surveys or other 1st-party data) and finding relationships within these datasets is not easy. Even smaller datasets can pose problems. Fortunately, data scientists no longer need "big data" sample sizes to see results. Data sets with 10 to 200 variables are enough to spot the root cause and deliver an intuitive causal graph, thus allowing marketing scientists to see the relationships between nodes and to what degree different nodes influence the dependent variable. Additionally, goodness-of-fit measures confirm model accuracy. So you can feel confident that you are providing the most robust and actionable insights through key driver analysis and simulation. 


Focus on Communicating Results

Inguo's causal graphs intuitively display how data is interrelated and identifies what variables drive key outcomes. Given the recent focus on storytelling and data visualization, Inguo checks all the boxes. Additionally, Expert Knowledge can be added at any time by data scientists to refine the model, resulting in true man-machine collaboration. 

Causal discovery through automated data analysis promises to be a game-changer for data and marketing scientists everywhere. From lightening-quick model generation to uncovering previously elusive insights, Inguo provides value through automation. With less time spent on laborious number-crunching, statisticians can spend more time analyzing what the data actually means without sacrificing accuracy. It is a revolutionary time-saver, will lighten the load for your statisticians, allow you to tell better research stories, and provide any analytics team with significant operational efficiencies. Welcome to analytical utopia!  


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