Questions for Data Driven Decision Making
What is Data Driven Decision Making?
Data. The buzzword of the century it seems. According to DOMO, "Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth." The sheer size of those numbers is staggering, but leveraging that data is paramount.
The idea of Data Driven Decision Making (or DDDM for short) is making decisions based on data rather than some form of intuition or even observation based. With the amount of data businesses are able to collect today, they are now able to use this data to make relevant decisions that impact nearly every aspect of business. For this post, we will dive in to four questions that can help industry leaders understand how they can use DDDM.
What can leaders do to overcome resistance or hostility among team members to data driven decision making?
Professionals are rightfully proud of their experience and expertise, and it's important for those individuals to understand that their expertise and opinions still matter. Data should be viewed as an ally to help them more efficiently accomplish their goals. If the data is difficult to collect or the infrastructure isn't easily available, team members may view the process as cumbersome and unnecessary.
While it's true that team members need to be on board with leveraging data, it's equally important that the effort to collect the data should be proportional to the impact of the decision that data is helping to leverage. For most organizations that means the tools and infrastructure should be in place to quickly gather and analyze the necessary data.
For example, at Reviewbox we help collect e-commerce data such as pricing information, product review, and ad performance. By bringing that data into a single place where it can be analyzed, compared, and exported, we help teams access the data quickly and efficiently. This allows them to leverage the data in a way that lets them make more informed decisions. By having a holistic view of their ecosystem, companies are better able to see all angles of their business.
What are the most common errors or pitfalls that businesses encounter when they try to implement data driven decision making, and how can they overcome these?
The most common error businesses encounter when implementing DDDM is not setting clear expectations around the decision making process. For example, what do they expect team members to bring to the table? How will decisions actually be made? When will there be enough data to make the decision? The best way to overcome this is to come up with, document, and communicate this process to all the relevant team members.
The first step is to fully evaluate what data can be made available and how this data may be used to benefit the organization. Afterwards, it will be necessary to procure the right tools. Not providing the necessary tools to actually collect, analyze, and present the data is another pitfall businesses encounter. Understanding what you want to collect is important, but being able to understand the data with the appropriate tools allows for data literacy to be much easier to obtain.
It's vital that all team members, including the leadership, consistently apply these principles to make decisions. While some exceptions are inevitable, these exceptions should be fully communicated when feasible so that team members understand their efforts are not in vain.
What does the future of data driven decision making hold? Is it appropriate to try to automate DDDM? Are there real dangers that DDDM will remove important qualities of human judgment from decision making? What are the impact of these trends on workers
Professional roles across all industries are becoming data-driven, and the organizations that are best able to capitalize on that will be the eventual winners. At the same time, it's very important to understand that the importance of human creativity will only increase. In a world full of data-driven organizations, the actual data analysis will become table stakes. Organizations that are able to make decisions quickly given limited data will often succeed over organizations that wait too long to make decisions.
Because of this, full automation is not an ideal state– organizations that remove creative, risk-taking individuals will suffer. Given that, over time, we do expect that low-level, repetitive tasks can and will be increasingly automated. For example, within e-commerce, we expect brands to increasingly automate many ad bidding tasks (i.e., adjusting the keyword bid based on the time of day).
How can DDDM advocates build a case for data collection/analysis for decision making at an organization that is new to it?
Approach the problem in two directions: First, communicate how industry leaders are doing it. What is their approach? What tools do they use? Second, it's okay to start small. Pick a limited project and go through the motions with that project. Assuming it succeeds, use it as a platform to launch larger initiatives.
What Does This Mean for e-commerce?
Data Driven Decision Making has become the crux of successful organizations. Those that are better able to collect and analyze data have the upper hand in a world that has collected 90% of its data ever in the past two years. In e-commerce, this data allows companies to aggregate product reviews and questions, monitor MAP, Buy Box, and third-party sellers, monitor changes to your listing's content, discover counterfeits and new brand competitors, and optimize ad campaigns. Reviewbox just happens to be the all-in-one company capable of collecting and managing all of that data for you.