Great Data Expectations

Photo by Helloquence on Unsplash

One thing we can probably all agree with is the fact that important decisions should not be 100% subjective. But at what point data stops and decisions begin, that's a whole other story. 

In past times, strategies were developed using available, limited data or even made completely by anecdotes. Now we live in exciting times with a wealth of data always at the ready. We can Google any question,  slice and dice data to get an accurate portrayal of our audience, create predictive models based on past data. Isn't it exciting?!!!

Ok...maybe the data honeymoon phase has waned. Lately, we've been facing what I think of as data malaise with the frustrating sentiment being; why isn't data making our lives easier yet?

We've realized over the last few years that living in this data-driven era doesn't make everything easier.  In fact, much like media, we can be overloaded with information that it becomes paralyzing to make a decision or even move on to the next step in a decision. What was I doing again? Oh yeah. My point. We forget that our job is to interpret data and then do something with it. 

The problem isn't with data itself, it is our expectation of it. We are putting too much pressure on data to do the heavy lifting. In order to use data to its best potential, we should set the right expectations about what data does and doesn't do for us.

Overall, there are three main principles that I keep in mind when working with data.

1.) Data illuminates

This is the most important principle. Data should do what data does best; give us the facts. Data can tell us what worked, what didn't. Tell us which audience likes a new logo, who doesn't. What features to promote, which ones to improve. The important part here is data illuminates the specific questions that we ask. Obviously, it cannot answer unknown or vague questions.

We have a multitude of tools and data sources at our fingertips that can help give us a much more accurate portrayal of an issue or question at hand. We can take a better look back into what was which helps us to illuminate what could be. We can look at what our competitors are doing, analyze our audience and predict outcomes in a much more sophisticated & swifter manner than before.

This is the exciting part of data and why we should continue to be optimistic about the road ahead. We have more opportunity to make sounder decisions and bridge larger gaps.

2.) Data can build confidence

This second principle is not about the quality of data but more about what the researcher gains with exploring data. When research & analysis is done right (and this is an important distinction), data gives us confidence. It's important to realize that data confidence is a process. Once a researcher explores research, data paints a better more comprehensive understanding of the question at hand. Data is not a mere answer but expands our knowledge and helps build our confidence in order make decisions

Whatever the project, after we have facts and look at the data with a critical eye, we can take a breath and move on to the next step. This emotional quality of confidence is very important and can prevent the frustrating situation we've all seen of redoing research or scratching a project altogether.

Data not only gives the researcher confidence but it gives the audience confidence. Think about someone giving a presentation. Once facts are presented alongside an argument, we then sit back and carry on with the idea at hand.

3.) Data is not insight

Lastly, data is not insight. Insight is contingent upon data analysis. By looking at data itself as the insight, we are jumping to conclusions in every sense of the term and skipping crucial steps in the research process. Data needs to be interpreted and analyzed before an insight can happen.

Data can help us make a decision but at the end of the day it is just a tool and cannot think for us. For instance, I've been in meetings where executives argue about a course of action and at the end of the discussion it is determined that "we'll let data direct us."  This is a blatant misuse of data. If you're asking vague questions like "What should we do? or "How do we solve this?" you will get meaningless results and will run the risk of confirmation bias. We can even apply this flawed approach to how we handle bigger societal issues. Often times in disaster situations, the question is "why didn't we know about this?" and "why weren't we prepared?" It's because we didn't first ask questions and we, smart and hard working people, need to do more of the heavy lifting.  

You can't look to data to do the hard work for you. 

We should not look to data as a virtuous hero but rather we need to pick up the reigns and make decisions from what data illuminates for us. In closing, it's important to be truthful about the realities of data. We should be honest with its benefits, shortcomings and where data's job ends and ours begins.