Lots of views and feedback on Part 1, so thank you for that. In response to some feedback, I’ve updated the title of the posts.
In Part 1, I discussed two of the most common gaps we see in supply chain analytics strategies today. Namely, having limited, or no, visibility into your operations and/or having a data set that is way too large and complex to yield meaningful results. It can be daunting knowing where to begin. It can also be frustrating figuring out how to overcome common barriers, particularly when your operations expert just knows “that’s how it works,” because he’s been there so long, or you’re trying to figure out how weather patterns and geo-political events are going to help you: build a perfect order, gets shipments out on time, or make sure your warehouse team is keeping pace with expectations… Heck, it was pouring this morning while I was looking at a weather report calling for sun all day, and we have Waze for real time traffic optimization. If you're like me, you're asking how is this data helping me solve my supply chain challenges? Let’s focus on data that actually helps us.
Here are two new concepts that will help you get started.
Tip #1: Focus on Smart Data
Pulling all of your enterprise-wide data into one solution is the best way to handle analytics, isn’t it? After all, doesn’t more data mean more insights? Hardly. The key to a successful analytics strategy in any industry is focus.
Whether you are already using an analytics solution or are just starting to figure out your strategy, in order to successfully implement one, you must determine what specifically you are trying to solve for and what metrics or key performance indicators (KPIs) you are trying to improve. By determining your goals in advance, you can narrow down the data you need to get there. This data is what we call “Smart Data” or the relevant data needed to get results.
What constitutes Smart Data varies by industry as different kinds of information are relevant to each one. For example, can you image if both accounting and marketing tried to use the same sets of data to drive results? Neither would be able to effectively create the metrics they need, or worse, they may confuse what they are trying to solve for with the extra data distracting them. In many ways, it is like one of those pesky word problems you used to hate in school with extra data lingering trying to trick you on what you were truly trying to answer.
Smart Data in the Supply Chain
For supply chain analytics, you should focus only on the data you need to drive improvements to your defined KPIs. What does this mean? Well, this could include data from any of your supply chain execution systems – Warehouse Management System (WMS), Transportation Management System (TMS), Inventory Management Software, Labor Management System (LMS), etc.
The number of systems you have in place will determine the quantity of data you have and the types of insights available. If you are currently using multiple execution systems, pulling the data into one analytics solution can make cross-functional KPIs possible.
Tip #2: Avoid Data Creep
To put a finer point on the importance of focusing on the right data set, or Smart Data, it’s important to understand the inverse. For those of you who have run projects, been trained in Project Management, or have been exposed to projects, you are aware that “scope creep” is the number one reason that projects fail. Well, the same is true for how you manage your data when you are building your analytics and BI solutions.
We’re introducing a new term in this post – “Data Creep.” Projects fail when the scope: requirements, goals, and deliverables, are changed after project kick off. This happens, because the project governance is lacking, and the project manager allows executives, customers, and any other constituent to insert their “must haves” in the middle of a project. Bad move. When companies approach data management, they must think in these same terms. Far too often, I see companies wanting to bring more and more data into the analytics platform, and they lose focus on what they are trying to achieve.
You should be trying to achieve very specific business goals with your analytics platform. Define what you want to solve or improve, determine the Smart Data needed to accomplish your goals, and then diligently manage Data Creep. Once you have a success under your belt, define a new set of goals, update your Smart Data set, and manage Data Creep. Your life, and your analytics solution, will be much better for it.
Demand More from Your Data!