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The focus of this article is on the application of #data for #growth through #innovation. The insights are independent of company structure or leadership. The prime recommendations are One. Discover and break the right number of critical links between outcomes and rewards/ incentives. Two. Find and modify reinforcement linkages between outcomes and culture so that all questions can be rewarded.
Figure 1 details the blocks that are used in the diagrams which will describe the systems of innovation. The blue circle is choice or a decision-making block; the grey squares are where people are involved in the system, and the oval shape is the beginning or end of a process.
Figure 2 presents a generic & simplified innovation process in startup-land. The purpose is not to explain all innovation in all start-ups, but to identify critical differences to the corporate world and why innovation feels easier.
Explaining figure 2. Starting from the white block positioned bottom middle of the diagram, “Hypothesis or Thesis”. Typically a team will come together with an idea, and the Hypothesis Or Thesis determines the data required for the Data Lake. There is a flow from a Data Lake to Knowledge and Insights on the left. The process tests the Thesis the team created through analysis using the available data. The analysis process generates Knowledge and Insight, which closes an agile feedback system as knowledge and Insight refines what data we need in a Data Lake to complement the analysis which tests the team’s Thesis or Hypothesis. Knowledge and Insights give rise to recommendations which leads to Decisions. Decisions over time, become Outcomes, which we measure. Knowing and measuring Outcomes help us refine and define better the requirements we have for the data in our Data Lake, thus creating a second slower iterative improvement system. Our Data Lake has a relationship or correlation to the Bias and Assumptions we have as a team. There is a closed-loop system between Bias and Assumptions and the Data Lake that further helps inform the Data Lake and reminds us of our Bias and Assumptions. A second influence on Bias and Assumption is the team’s Beliefs and Culture…
