Companies are leveraging data to make more and more business decisions. Unfortunately, some of the approaches are lazy, ineffective, and misleading, costing companies a lot of opportunities. If you’re a part of the data industry, there are things you should watch out for. There are also ways you can break free from everyone else to serve your business goals better. Here are some mistakes you can make with data that could destroy your business instead of improving it.
#1 Mistake – Starting with Metrics
Instead of starting with metrics, you should start with a well-defined goal. You can collect, store, and analyze the data, but you need a detailed plan to gauge your success. However, if you don’t have a clear point of view, you’re chasing a moving target. Start by asking questions and creating a clear goal, so you don’t have to rewrite history as your data comes in. Next, you want to clarify what your key metric is that will determine your success. For example, say you’re looking for new users, but a different demographic is attracted.
If you want to consume data correctly, you want to stipulate your goal and definition of success clearly. Create a scorecard where you’ll judge your success, and write it all down before the project even begins.
When asking the people involved in data how a featured product is going, they may come back with fun facts instead of the goals and numbers. Therefore, figure out the best path to get to where you want to go. Decide on a top-level objective and be honest about it. Writing it all down will allow you to stay focused on your original goal.
#2 Mistake – Hiring a Data Scientist
You might think it’s a good idea to have an employee dedicated to figuring out what the data means and how to use it. Unfortunately, many would believe that data science is a collection of skills and not a job itself. Yet, everyone on your team should have some of the skills that bring together the ability to analyze data and do something about it. With data science, everyone should take responsibility and be capable of working with data and making decisions from there.
Data science requires you to look at statistics while knowing about your market and how the business functions. Strong programming skills are also an asset. Often when a data scientist is hired, they can only use their statistic skills but can’t see the business context that would help them further. This isolation doesn’t allow the data scientist to understand the reality of how the business operates, which influences their recommendations. If you’re hiring a data scientist, they should also have some business sense and know the company inside and out. Otherwise, they don’t have the context to be successful, which means you’re not successful as they have no idea what to look for and what questions to ask.
Instead of focusing on a narrow title with someone who only does one thing, look to your team. It’s likely your developer or someone who took a statistic class while working on their business degree will be more effective. They already understand your company and know what to ask. The CEO can also be an important asset in analyzing data, whether they realize it or not. Start-ups should steer clear of costly data scientists. If you’re a smaller company starting out, likely, you don’t even have enough data.
More important than hiring a data scientist is developing engineering skills and statistic and business knowledge – support employees willing to try to help you out with this. Early in your business’s life, hire three experts and bring them together on analyzing data. It will cost you much less, and you’ll have a dynamic team that has worked on this since the beginning.
#3: Chasing After the Latest Tools
Many people with smaller businesses will try varying apps and tools to save them time and help them figure out how to run their business best. There are always new tools coming out, but they will not help make your data strategy perfect. A tool may absorb events you sent it, but all said and done, it’s you who decides what the events are and the meaning behind them. You have to ensure those events don’t change. You want accessible data and flexible tooling that works in any way you need it to.
Start with clear definitions and a process that works well. You should clarify the goals of the company while allowing for experimentation on productivity. With the product, you should define its core features and test it with a dynamic tool. You want the data to be accurate and work for you, so you can make the necessary changes to enhance your business.