As data people, we immediately wondered about what kind of analytics can get generated from the point-of-sale system. We figured it was pretty easy to see when there might be surges in the number and kind of orders placed, but maybe no way to tell how long customers waited for these orders, or how many might be waiting at any given time. But some back-of-the-envelope calculations don't seem unreasonable, something like x number of minutes per order to figure out a time range for each customer/order, and then break the time into smaller buckets and count how many open orders in each bucket and compare that to other times of day and...well, you get the drift.
As we walked away with our coffee, we began to speculate idly about a whole analytics and decision making exercise. Let's see: we need some analysis to fully document the extent of the issue, from which we'll develop a glitzy enough presentation to ensure managers understand the situation and could be engaged in investigating further. That could lead to some kind of cost/benefit estimation around various potential solutions (more staff, more automation, additional materials preparation, etc.), maybe even an A/B test on different days or different locations. But wait--what additional data would be useful, how could we collect it, could we train AI on this model data and have it pre-analyze our solutions, and so on. (Don't mock us - we don't tell you what to daydream about!)
For all we know, the company in question has already done this work, and determined that whatever efficiency measures that could be employed wouldn't pay for themselves, or that this bottleneck only occurs once a day for a short time and doesn't seem to impact revenue, or that, due to location or brand recognition or what have you, the market is essentially captive and there is no business impetus to speed things up. (Remember, this barely qualifies as a thought experiment. We haven't been to enough locations at this time to know whether what we saw was representative, we don't know if each location is an autonomous franchise or if there is strong central corporate control, we don't even know if the company itself is independent or if the whole operation is an accounting exercise for some multinational behemoth!)
Still, it can be fun to think about some other organization's challenges, and the ways data might illuminate those challenges. And we'd like to believe that it stretches and strengthens our mental muscles, so to speak, to look up from the tasks in front of us. But we think there are some general principles or guidelines that come into view here, regardless of whether you're performing a training exercise, running a business, or letting your mind wander.
The first principle is that your organization's data, and your data analysts, are not doing you much good if your data management and analysis activities aren't driven by your business needs and strategy. If it's a strategic imperative that you hustle customers in and out of your coffee shop as quickly as possible, then you'll want the data to enable understanding how successful you are in meeting that objective. If your business strategy is focused on maximizing profit, however, and the profit margin on an iced cappuccino is much greater than on drip coffee, then maybe your analysis should look into whether the additional time it takes to create and serve the upscale beverage generates more profit than you'd realize if you had more of an assembly-line operation around your dark roast.
Our second principle posits that business ideas that could be enriched or informed by data can originate anywhere in your organization, not just from C-suite decision makers or data scientists or unit heads. We wouldn't expect the barista who's busy perfecting our latte to also perform sophisticated queries against POS data (though we also wouldn't be surprised if she could), but that person could certainly observe that it seems to get awfully busy every weekday around 8:30am, and the rush doesn't die down until midmorning, and so forth. We realize you don't run a coffee shop, but you probably have plenty of employees who pay attention, and whose observations might spark a powerful change. And it stands to reason that the more your employees encounter data, and the stronger their data literacy competencies become, the more these suggestions for improvement might bubble up. We might extend this observation by noting that even those aspects of your business that don't look that data-intensive could still be points of entry for conversations about how to make more and better use of data. Have a process in place where employees can make improvement suggestions that will get looked at.
Our third principle will be familiar to our customers, and probably anyone who's had to spend too much time next to us in line at, say, a coffee shop. Even a relatively simple analytics experiment such as we described above can go off the rails before you even get started if you aren't using data-related terminology consistently, or if you don't know what problem it is you're trying to solve, or if you can't describe the outcome(s) you're looking for. When we're talking about customer wait time, for example, do we mean the amount of time between placing an order and receiving the product? Or do we mean the time between coming in the door and receiving the product? And what metric would we employ here? Average wait time by time of day? Median wait time by product type? Correlation between wait time and staff experience? Before we get any distance down this line of inquiry, we should make sure everyone who's involved is using the same terminology the same way, and that we know what questions we're trying to answer, and why those questions interest us in the first place. Have a place for this data-related terminology that is accessible by employees.
Your business strategy should drive your data strategy, to the extent they can even be decoupled. And the decisions you make about how to govern your data will affect your ability to act on your data strategy, and your ability to ensure its continued alignment with your business strategy. Your data is a critical business asset, and to get the most value from it, organizations need to find ways to allow nearly all their employees to utilize it. Employees can utilize data both more effectively and more appropriately when they have easy access to governed data, curated data sets, and certified data products. And to be successful in those curation and certification efforts around your data, you're going to need effective communication about what data is called, what it means, why it's been collected, where it lives, who is responsible for it, and so on. You might very well discover you need some help with this work, possibly even a lightweight, user-friendly data intelligence and data governance solution. We'd love to talk to you about it. Maybe over a cup of coffee sometime?
We hope you found this blog post useful. Also check our our data governance spotlight resources located at https://www.datacookbook.com/spotlights. IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
Photo Credit: StockSnap_T2DS9PUWRZ_coffee_datacaffeinate_BP #1306