With the constantly changing tech and business environment, the ability to offer valuable and timely insights through data becomes increasingly important. Lean analytics applies the lean principles (delivering value in short cycles, eliminating waste and receiving constant feedback in each cycle) and focuses on choosing the pieces of data that improve both learning and decision making.
One of the key concepts in lean analytics is the 'Only Metric That Matters' (OMTM). This is a metric that addresses the aspect you want to focus on in order to achieve a business goal.
In this workshop we will simulate the lean analytics cycle with an example scenario including a business problem, some relevant user data and 2 iterations of 20 minutes each.
Participants will be split into teams of 4 or 5, then given a printout with a business problem and a list of 10 posible features to be built.
In the first iteration, the teams will have to rely on their intuition to choose and construct the features they consider most valuable for the customers. They will have to:
Then the teams will have 10 minutes to chose their OMTM from a list I will provide. The list has the metrics and their initial value. The teams will have to discuss which one of the metrics is more helpful to keeping track of how close they are to their respective business goal.
Before the second iteration, each group will be given a list of customers' answers to a survey. The goal of the second iteration is to contrast their guesses with the customers' answers and then find pieces of information that support their intuitive choices from the first iteration. This will provide insight into which features to change. The second iteration includes the following activities:
The teams will then have 10 minutes to see how building the features impacted their OMTM. To do this they will refer to a table I'll provide, which has the correspondent increase or decrease on each of the OMTMs in connection to the chosen features. If the team decided to improve a feature, they will get twice the value of the difference in the metric.
To finish the session, I will explain how the lean analytics cycle was applied during the exercise and take questions at the end.
Maria is trained and experienced in database management, business intelligence, descriptive and predictive analysis, and knowledge discovery from databases. She is an agile practitioner with experience in banking, airlines, education and logistics projects. She's also a data science enthusiast fascinated by transforming data into knowledge - and an advocate of lean analytics.
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