The hardest part about Data Science - the art of putting data to use - can seem like all of the math and technology, but it really starts before any of that: Asking the right question.
Ask, and you shall receive...
The kind of questions your team needs to answer are often complex and time-sensitive ("what inventory do we need to complete our Q3 projects?"). When customer needs change, new questions also pop-up ("The contract has changed, do we have inventory to move ahead?"). Once you get an initial answer, you usually have more questions to add (i.e. how many to order, what's the new timeline, do we need to hire).
As the cycle continues, you find yourself with more questions than answers, and no great decisions. Issues like information overload, chasing rabbit holes, and burnout do occur. These factors have a negative impact on the bottom-line and customer satisfaction, both criterion for getting the job done.
Thinking with data
So how can your business consistently get the right insights to fuel strong decisions? It all starts with having the right goals (i.e., annual sales, total customers) to know what questions to ask and how to answer them. This is when data - raw pieces of information - can be used to give insight into what your next steps should be.
Using data may seem like it starts with some software or a spreadsheet, but really begins with breaking down a problem into a good question. By doing so, you know what data you have, what data you need, and what you need to do with the data.
The Data-Driven Checklist
Here are 5 simple steps to help you ask (and answer!) a data-driven question:
Step 1 - State your goal AND your problem. It may seem obvious, but considering your objective at each decision-point can be challenging as products change, customer demand increases, and systems grow. Start by calling out your goal (reduce production cost by $1.3M) and what the challenge is (an outdated production system). Using the SMART approach can help you create strong objectives.
Step 2 - Describe what will occur if you don't solve the issue. Knowing what will happen if you don't succeed, will help to prioritize this issue against other objectives. State the financial risk (loss of $4.0M in sales) and operational risk (maintenance increase by 34%).
Step 3 - List out the contributing factors you think are important. Brainstorm ideas on what has led to this point (i.e., aging warehouse, out-of-warranty machines, manual tracking), what has helped (new conveyors), and what has not helped (hiring more technicians). A fishbone diagram is a visual way to look at causes and effects.
Step 4 - Evaluate the data you have to analyze. At this point, you'll learn what data your business has and how much there is to use. Work with your team to make a list of supporting details that describe: - contributing factors (maintenance schedules, inventory loss, replacement options) - how they are stored (excel files, receipts, weekly reports) - who has them (warehouse lead, equipment manufacturer)
Step 5 - Include the information into your decision. With data in hand, you now have a good set of facts to analyze. When combined, the information should provide a short-list of solutions (facility upgrades, improved scheduling, waste material reuse). Consider hiring data experts to fast-track your insights and create new capabilities for your business.
Learning how to ask a good data-driven question takes time, but can produce real results effectively. When you are able to put your data to work, the sky is the limit on how it can transform your business and give you a competitive advantage. No matter what size or type of business you are, data has an important role to play.
If you want to learn more, check us out at OG Data Labs.