Unlocking the value of your data starts with mapping your data ecosystem. What's the data ecosystem? A data ecosystem is a map of all the data used in end-to-end business processes. For example, imagine that you own a chain of apparel retail stores. What might be included in your data ecosystem? A customer in your clothing store purchases an item. That's a data point. If you aggregate that with all other purchases across stores in each region, you have a type of data transactions. We call this a dataset. Another dataset might be item returns. Another is footfall in your stores. All of these datasets are types of user data. User data is data from customers or users of your service or product. User data, therefore, is your first data bucket. Now, let's think about data that is more operational. For example, data about staffing levels in each store, stock delivery dates, overall sales performance of each store, store staffing structures. Example, how many people are in the fitting room versus on the till? These fall into the second bucket of data, corporate data. Corporate data includes things like sales patterns and operational data taken from the company. Can you think of more datasets that you'd have as the retail owner? The third bucket of data is industry data. Industry data is data found outside of an individual organization that everyone in the sector has access to. This could include wider trends, purchasing patterns, and publicly available research papers. These three buckets make up your data ecosystem. The more datasets you add to the list, the richer the insights that you'll eventually be able to gain. You'll notice that as we map the ecosystem, I am making a note of which datasets I currently have and which datasets I think I can get. As you can see, we have used a check mark and a question mark to indicate the difference. We will come back to this in a later module. Now you have your data ecosystem mapped out with a list of the different datasets you have or think you could get. What next? Start playing with the intersections between your datasets. Take two or more datasets and ask yourself, what insight could I gain if these datasets were combined? For example, suppose you're a sales manager at a biomedical diagnostics company. You provide a range of diagnostic tools to hospitals and laboratories across the region. One of your datasets is sales of each product. You also have some datasets about the hospitals themselves, including specialties, location, patient turnover, and central laboratory facilities. In this case, by integrating how many products are sold at each hospital with all of the other datasets you have for the hospital, you have a clear insight into what makes your ideal customer. Here's another example. Jane, who appeared in the earlier module, is a personal banker. What does her data ecosystem look like? Well, user datasets might include user demographics, user financial history, and previous user interactions. Corporate datasets might include sales by financial product, sales conversation call logs, and performance metrics of financial portfolios. Banking is such a heavily regulated industry, that there is a lot of industry data available, including industry benchmarking. Another set of industry data is stock performance and other investment trends that all of Jane's colleagues and competitors at other banks have access to. In this case, by integrating her user demographics data with product purchases, she can begin to uncover what demographic indicators are significant in predicting product purchases.