X and o data12/21/2023 When weighing whether or not to include certain facets of O-data, ask yourself: What are the Benefits of including this data? What are the Risks of including this data? What are the Alternatives to this data? What does Intuition say about these data? What happens if these data are Not included? When you hit the last mile of your data refinement, this can be a useful acronym.Īfter you’ve built the right data ecosystem, be sure to review it periodically with a critical eye and make changes where needed. Focusing on change means preserving the firm’s attention for change, too. Ultimately, our goal should be to improve experiences in order to create economic value. Not only does this create valuable consensus, it engages and embeds stakeholders into your efforts. Oftentimes these opinions are rooted in deep experience that is easier to express than to explain. In a workshop setting, execute a prioritization exercise by asking stakeholders what they think should be included alongside your X-data and O-data. Sometimes you have to just give the people what they want! (In case you didn’t know, the bold is Latin for “the voice of the people is the voice of God”). These variables represent the vocabulary of the frontline and management, helping everyone to understand X-data better. When measuring employee experiences, variables such as staffing levels, turnover data, team size, absenteeism, etc. For example, when measuring customer experiences, you could include store-level sales, footfall, number of SKUs, etc. Include O-data elements which are naturally adjacent to your X-data. Talk to the people who understand the specific use cases and keep in mind: To combat this, do two things: 1) limit the O-data volume by attaching it only to existing X-data records, and 2) ensure both X-data and O-data systems are representative in all analyses.Ĭhoosing the Right O-data with Business Acumenĭon’t discount what people across your organization already know. If we analyze outcomes with both types mixed in, almost all drivers will be O-data based due to their overabundance. By volume, the number of O-data variables is vastly larger than X-data, and in some cases could exceed a ratio of 100:1. Ensure your data are statistically independent to ensure you’ve got a light, nimble mixture in your data. For example, there is a surprising redundancy in our data ecosystems we don’t need 4 KPIs when 1 or 2 will do, and we don’t need hourly sales data when monthly will do. ![]() Ensure your data are orthogonal, that is, where each datum tells you something new or unique. We want to carry the volume of data that arms us with knowledge and insights but doesn’t weigh us down. Data creates gravity: the more you have around you, the more it sucks you in. ![]() Use analytics to control for these so that you can recalibrate your understanding of what constitutes and drives “good experiences.” service quality, friendliness, cleanliness, etc). Simple things like the number of restrooms, whether you serve sandwiches in your store, the economic level of the area, can all have a material impact on customer and employee experiences that we typically explain with X-data variables (e.g. The easiest way to do this is to include what is statistically significant in a driver analysis in your data ecosystem. Improve experiences by focusing on O-data which explain meaningful outcomes.
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