"Data-driven" is a label that most companies like to associate themselves with.
Almost none call themselves "data-informed", but there's value in understanding the distinction. With the right analysis, data turns into a proactive tool that generates valuable insights for your business. Decisions based on these are the backbone of successful growth strategies.
In this blog post, we'll explore how you can extrapolate knowledge starting from these two approaches: data-driven vs data-informed. We'll look at the differences between them and explain how to think about deriving real-world insight from your data.
When you take a data-driven approach to making business decisions, you start your decision-making process from the data you collected and analysed in the first place.
This is true even when the data doesn't back your past experience, goes against your intuition, or seems erroneous—you use the data itself as the prime teller of what to do next.
This approach is based on the idea that data never lies.
(But—as described by Bob Fisch—this can be a logical fallacy)
Pure numbers and high-quality analytics can provide valuable insight and back your business decisions. In many situations, the data-driven approach is highly warranted.
For example, when an online store applies a data-driven approach, it can analyse its customers' purchase history and figure out which items they are likely to buy in the future. This helps the store send them relevant offers or serve the right ads.
In this case, it's an effective business strategy to use the data itself to inform what the customer will be offered next, as their history showcases interests and trends hard to surface otherwise.
Let’s look at another example. A furniture store conducts a customer survey to explore the possibility of offering furniture assembly services.
If the store decides to trust this one piece of information as the main driving force for offering a new service, they are adopting a data-driven mentality.
(More on the data-informed approach in the next section)
By implementing a data-driven approach, a company chooses a strategy that's based almost entirely on the data itself—even if it goes against intuition.
By preventing personal bias, the data-driven approach is believed to save time and money while yielding predictable results. However, due to common logical fallacies (e.g. "quality" data in, therefore outcome out), it's not always the best way to revolve business decisions around.
Source: Walmart
Back in 2004, when Walmart learned about an upcoming hurricane, they needed to figure out what items to stock before the storm came.
The company's analysts looked at the purchase history (460 terabytes of data) to figure out what the most popular items were when the last hurricane hit.
By stocking the necessary products before the hurricane arrived, they managed to achieve impressive sales and help Florida residents in the process.
Instead of focusing primarily on data to make business decisions, a data-informed approach uses other tools and resources, such as user research, personal experience, and third-party insights.
While data remains an important part of the decision-making process—it's not the only player on the field.
Rethink for a moment of the previous example of the furniture store.
While it's great that the majority of customers want a new feature, the added operational costs would be a burden for the business.
With this in mind, the store owner can lean on their own intuition and informal analysis to "block" the process from moving forward if considered too risky. Since the furniture is easy to assemble, offering an assembly service would only be seen as a "nice-to-have" that requires a strong premium to make sense financially.
A data-informed approach analyse data while drawing upon other relevant factors, using the results to help the business make an informed decision.
Consider the Walmart example above from a data-informed perspective.
While analysing data related to customer preferences, decision-makers and researchers can also implement personal insights and lean on community experience to figure out some of the products that would be most useful during the hurricane, on top of the ones suggested by historical analytics are the most useful during the hurricane.
The data-informed approach blends human intuition with data to provide educated and experienced-based results. In more nuanced or sensitive cases like a disaster response or large business shifts, this type of decision-making wins over data-driven decision-making.
Source: Airbnb
When we think of Airbnb, we imagine its current platform, modern and fast, but it was not always so. If you accessed the platform in the beginning this was what you saw:
Source: Airbnb on Medium
It was basically a list.
Airbnb then realised that the best way to improve its service was to redesign its platform and conform it to the wants and needs of its audience. So, after some qualitative studies, Airbnb redesigned the platform, and this was the result:
Source: Airbnb on Medium
The second version of the design appeared more appealing than the original due to its increased use of listing photos, improved search map, and reduced impact of accompanying text.
This was achieved through a series of "data-informed" decisions that combined the team's expertise with the user research and the historic website usage data.
When it comes to making the best decisions, both data-driven and data-informed approaches can work. Each method comes with certain pros and cons.
The main advantages of the data-driven approach include:
One of the main benefits of a data-driven approach is that it's always stable and available. While insights, experience, and intuition change over time, data always stay the same. As long as you practice effective data-gathering and management methods, you can rely on the information.
The disadvantages of data-driven data are:
Studies show that poor data quality results in social and economic losses that add up to billions of dollars. If you aren't 100% sure about the data quality, decisions based solely on this data could be erroneous.
The key advantages of the data-informed approach include:
Overall, the data-informed approach is more comprehensive. It gives you a wider range of elements to base your decision on.
The main disadvantages of the data-informed approach include:
In some cases, you could find that data leads you to one decision while your team's opinions and perspectives warrant another. Figuring out which decision to make in this situation can be complicated.
Factors such as company culture, industry, and business goals can influence the decision-making process. Choosing the right approach to decision-making depends on several factors, including:
To sum things up, data-driven and data-informed approaches can both be useful for effective decision-making. While the former focuses on data as the primary indicator of where to go next, the latter also takes full advantage of human insights and experience.
Business owners can leverage both data-driven and data-informed approaches depending on data asset availability, analytic tool quality, and current decision-making goals.