Making a decision often means conducting decision-driven research. But there are many problems with executing decisions based off of data-driven research that end up producing less than ideal results and decisions that could be improved with decision-driven research.
The struggle is, your research methodology is so ingrained in your day-to-day processes, it can be difficult to move away from those processes. In the end, it’s worth it though. Here’s a peek at the seven problems with data-driven research and how decision-driven research can help.
Those who work to execute data-driven research are often left feeling like their efforts were unproductive. That’s because in the end, this research often does not lead to the decision you set out to make. We jump into research seeking an answer to a question when we find ourselves lost in a sea of data and distractions. This process thus eliminates some possible results by jumping to a hypothesis.
We can enter into a data-driven research project without knowing all the data and information - that’s a given. More importantly, we may start our research effort without a clear definition of success or what goal or objective we are driving to realize. The powers that be tell us vaguely what we should be making a decision on and we’re forced to try and fill gaps that truly aren’t fillable given the research parameters. It leaves us with a sub-par answer because we didn’t have a strong metric to guide our effort. Measures of success are key for decision-driven research and you are more likely to succeed with this approach.
Data-driven research often includes finding information that backs up the decision we want to make. We seek confirmation for what we want to know instead of what we should find out. Thus data-driven research can be seem more efficient. On the other hand, decision-driven research decomposes our work into insights, predictions and then actions. This takes more time and discipline to accomplish, but it is well worth the effort.
When research isn’t your background or career choice, it can be exceedingly time-consuming to learn and understand the research you find. Now you’ve taken on the time-consuming task of completing research only to move onto the time-consuming process of compiling and trying to understand that research. It isn’t something that is structured to quickly and easily see what matters. Yet, decision-driven research can be much easier to consume from its “cliff-notes” structure.
Decision-driven research can be more efficient and focused when it is done well. You can explore seemingly interesting avenues with data-driven research. You are more likely to give your curiosity a voice with data-driven research. The large time commitment you have to make to this type of research and resulting decision isn’t nearly as satisfying as having data and insights to drive your decision. You’ll feel much more productive from data-driven research because the end result is much more satisfying and rewarding.
How do you know when your decision-driven research is complete? Is it based off of a simple gut feeling of “that should do it”? It’s tough to know when you have done enough investigation to proceed to a decision because there’s no clarity on when you’ve ingested as much information as possible to inform the decision. In contrast, data-driven research seems to offer more clarity on completion when there is no more data to process. But consuming all your data is not a valid metric of decision success.
When you conduct decision-driven research in a data-driven research institution, there isn’t a ton of clarity around who the decision maker is, who decides what research assets to acquire and what process to follow to execute the research. This lack of clarity results in research that isn’t acquired following a process and is therefore less effective.
Coegil offers a platform where you can harness data-driven research to guide your decision-making – that’s what we call decision-driven research.