16.6 Tools: Adopting and Using Lean Analytics

Data-driven decisions are at the center of analytics. However, the sheer volume of data also means that your company is far more likely to be drowning in data rather than not having enough data. For example, Walmart’s Retail Link database collects up to 2.5 petabytes of data from 1 million customers every hour. The data itself is unstructured, which means that they are basically a mass of numbers and letters with no immediate use. Think of it as high-quality marble: you still need to chisel away at it to make it useful. Following the same analogy, you are usually trying to sculpt a new artwork rather than simply imitating something that someone else has already done. In other words, lean analytics is most readily applied to developing new products and services.

Of course, you can jump into the data head-first and try to figure it out as you go, but doing so would probably mean lots of wasted time and energy. Along the way you might make mistakes or realize that some data points contain errors. Then you would need to backtrack to see when the erroneous data entered your analytical process. In short, because advanced technologies are allowing companies to collect an enormous amount of data, complex analytics requiring multiple interdependent steps must also be managed in such a way that minimizes potential error—hence waste—so that insight generated through analytics can be trustworthy the first time, every time.

This is what lean analytics is all about: how to maximize data-driven learning within the shortest amount of time. Doing so will allow you to quickly use data to identify new market opportunities, build and refine your new products and services, and attain first-mover advantage. Here is how you can do that:

1. Purpose. Every business has a primary purpose beyond simply increasing sales or market share. To apply lean analytics, usually you are focused on identifying a new way to serve the market. In other words, you need to understand your prospective customers: Who are they? What do they need that isn’t currently being served? Is there anything you can offer that would satisfy their need? Will they care about your product/solution?

2. Data. To make sure that you are getting the most out of your data, you first have to make sure that the data itself is free of errors and that you are selecting the most relevant measures. For instance, below are some questions you should ask:

   • Is my data standardized in terms of the basic units of measure?

   • Does my data contain outliers, trend, and seasonality?

   • Does my data contain all metrics that are necessary for my analytical purpose?

   • Does my data contain metrics that I don’t need for my analytical purpose?

3. Focus. Once you have identified a purpose, and that purpose has helped guide you to shape your data as well as metrics, it’s time to figure out just what is that one metric that rules above all else given the current stage of your product or service development: Is it something that sets your product or service apart? Is it a revenue or profitability target?

With these three steps as your guide, you may quickly bring your ideas to life by doing small batch tests, gathering data, analyzing based on the one metric that is your primary focus, and continuously shaping your product or service to become closer to what you originally envisioned. The key things to remember are the following points (Figure 16.4):

• Ideation: Coming up with a successful idea requires you to really understand who you are serving. Just like everything you have learned in Lean Six Sigma so far, it starts with a clearly stated project that, if successfully developed, will result in substantial improvement, or value creation. For example, Tesla understood that its early products needed to satisfy range requirements above all else. That is why it spent the bulk of its early product development efforts on designing the battery pack that goes into all of its cars.

• Test, re-test, expand: Your first product is not always going to be your most successful product. The important thing is to maintain your metric focus on the dimension that matters: what value you intend to create. Tesla’s first car was a roadster, clearly not a car intended for the masses. Likewise, other automakers would release concept vehicles at auto shows to gauge market interest and buzz. The most successful elements are then adopted as the basis for new model development: the battery pack, with the sole emphasis on alleviating “range anxiety,” perfected through Tesla’s early prototypes, later became the reason for Model S’s and Model 3’s success; Nissan’s luxury brand, Infiniti, released a “Q Inspiration” design language emphasizing “Empower the Drive” and was later adopted into many models in the brand.

  Figure 16.4: Lean Analytics for New Product Development

• Efficiency and reach: As your product increasingly enjoys mass adoption, your metric would naturally need to shift focus toward improving your production efficiency and distribution reach to ensure your ability to serve an expanding market in terms of both quantity and sales support. After all, you cannot benefit from your increasing sales without achieving production economies of scale for lower costs and delivery capacity to actually get your products to your customers. For example, Sony’s Playstation 5 should be dominating consumer electronics by now, except that it could not produce enough due to microchip shortage or distribute them quickly enough due to logistical constraints.