Ugly Research

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Information about Ugly Research
Business & Mgmt

Published on March 13, 2014

Author: TracyAllisonAltman



I was dissatisfied with the available methods for explaining outcomes and making recommendations to decision makers. So I created something that worked better for me. My method is now available as PepperSlice, designed with the decision maker in mind. It's a straightforward way to tell a data story: To demonstrate value, explain outcomes, and recommend action.

DATA IS EASY. DECIDING IS HARD. ©2014 Ugly Research 0 0.5 1 1.5 2 2.5 3 3.5 4 We measure lots of things. This is good if the right things are measured, and the data gets noticed. Data is easy. ©2014 Ugly Research 0 0.5 1 1.5 2 2.5 3 3.5 4 Deciding is hard. Need to know what explains outcomes. What actions will improve results? This doesn’t speak for itself. ©2014 Ugly Research The good, the bad, the ugly. What is tech getting right?  Data-driven decision-making  Self-serve capability (BI, operations)  Predictive analytics What’s missing?  Insight-> action-> outcome sequence  Decision culture (vs. data culture)  Explanations of outcomes ©2014 Ugly Research What should decision makers get? Not just this Or only this Or this ©2014 Ugly Research you can expect this result. If you take this action Recommended ways to improve outcomes What should decision makers get? Supporting data: Explanations of the data Actionable insights Search for… Outcomes Actions Data sources People Analytical methods Method: Linear Source: RCT Confidence: 80% Explanation: Evidence Method: Bubble Source: Cust Confidence: 90% Explanation: DataSci Method: Dynamo Source: SAS Confidence: 70% Explanation: Cruncher ©2014 Ugly Research unbounded bounded automatic actionable insights analyticsdata Decision management, business rules Self-serve BI, analytics, operational Decision-maker interface Type of info delivered Typeofdecision Moving up and to the right. ©2014 Ugly Research For example.  Mining: Predicting equipment failures (IBM Smarter Planet).  Health informatics: Connect health and operational outcomes.  Retail: One-off decisions about store locations.  Technology: Attributing revenue to R&D, support, sales programs. ©2014 Ugly Research Introducing PepperSlice. Matchmaking process: 1. People who need data (decision makers). 2. People who have data (analytics, operational data, BI, evidence). ©2014 Ugly Research actionable insights PepperSlice more data visibility and context  better data matched to the right people   Everybody wins.

Find out more. Contact Ugly Research email: twitter: @UglyResearch phone: 510.891.1033

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