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Cash-to-cash cycle time (also known as cash-conversion cycle or order-to-pay cycle) measures the days between (1) the purchase of materials/inventory from a supplier and (2) payment collection for sale of the resulting product(s). A company’s operating capital generates no value if it is tied up in inventory, making cash-to-cash cycle time a critical analytic for finance.

We need 4 things to become a data-driven culture.

1. A data-driven culture needs data. Makes sense, right?

2. We need the tools by which we can analyze our data.

3. We need the skill sets to be able to use the tools to analyze our data.

4. We need a mindset shift to get people thinking about and using data for practical decision-making.

As states of emergency were declared and stay-at-home orders obeyed, some companies declared analytics useless. For these companies, the primary goal was to keep the company operating. For several of my clients, the metrics and analytics that we had put in place in the last 6-12 months became the data tools by which they managed their business decisions during the pandemic closures. Here are their stories.

Those that operate in the learning space are familiar with The Kirkpatrick Model® for measuring training effectiveness. This article will outline a simple method for evaluating online and on-site workshops. 

Individual locations of large restaurant chains are supported by the enterprise systems in their corporate locations. These systems assist each location in viewing sales, sales patterns, usage of ingredients, profitability, inventory analysis and more. An individual owner of a restaurant or bakery doesn’t have the luxury of corporate support and enterprise computer systems but still has to figure out how to compete with these chain locations. Data-driven decision making has become an invaluable tool for these entrepreneurs.

Why is product rationalization important? As the number of products increases and products move through their life cycles, the percentage of products that become low volume and/or low profit increases. So how does product rationalization work? 

In the analytics world, there are teams that are “stuck.” This article is to help those teams get “un-stuck.” Perhaps you headed down the path of analytics without a plan. Perhaps you had a plan but the ever-changing data privacy regulations halted that plan. It’s time to refocus so you can begin to move forward once again.

The following overview is written as a guide to those new to analytics or new to solving business problems.

The concept of the Net Promoter Score (NPS) was first published in 2003 in an article called,  One Number You Need to Grow. It was created as a way to measure and manage customer loyalty without the complexity of traditional customer surveys. Since that time, the same concept has been applied in multiple functional areas to replace longer surveys that require too much of a participant’s time. 

Regardless of what you do each day, you’ve probably realized that you are surrounded by data. The quantity of data that exists in the world grows substantially with every passing hour and it is said that 95% of it has yet to be analyzed. That’s okay, because success in the data and metrics world is not about analyzing data for the sake of analyzing data. It’s about analyzing data to answer a question. There also has to be value in finding the answer to that question or you risk wasting valuable resources (time and money).