Every week, I encounter work orders from research analysts requesting data analysis on our Research Partners’ test results that are difficult to understand.
I think there’s a reason for this – poor communication.
I’ve noticed a lack of understanding of how data analysts think. Research analysts and many marketers do not define projects and goals the same way data analysts approach a data challenge.
Data analysis takes time and resources, so the less time spent interpreting desire over data will leave more room in the budget for necessary analysis. Better, clearer communication means everybody wins.
I wanted to share with you four tips to boost your team’s communication that will hopefully save you a little time and money in the process.
Tip #1. The more specific you are, the faster we can help you
If you’ve had experience working with data analysts, then you may know sometimes the conversation can be like asking someone for the time of day and they explain how their entire watch works, even if you just wanted to know the time.
But, who is really responsible for the failure to communicate here?
Is it the timekeeper for being specific, or was it the vagueness of the person who needed the time?
My point here is these kinds of communication mishaps often ring especially true in the analytics world. I can attest that an analyst with clear objectives and goals will be able to perform analysis at an accelerated rate with less revisions and meetings necessary to achieve results.
So, instead of asking for general analysis of a webpage, email campaign or other initiative, try asking for the specifics you want to know.
When an analyst hears general analysis, it’s like giving us a set of Legos and expecting us to instinctively know you wanted a plane constructed instead of the impressive 40-story futuristic building.
For example, let’s look at the following requests that highlight how just a few more details can make all the difference:
- Request #1: “I need to know the clickthrough rate for new visitors compared to returning visitors.”
This question is going to get you what you need faster than asking for a general analysis of a webpage.
- Request #2: “I need to know the clickthrough rate for new visitors compared to returning visitors for the second to third step of the checkout funnel.”
The second request would likely deliver the rate from steps two and three for the different visitor types.
Now, if I only had the first request to work off of, I would deliver the clickthrough rate for every step of the funnel, which takes substantially longer and costs more.
This is also because data analysts have a tendency to flex their advanced analytics muscles when given the opportunity. We want to deliver quality work.
But, the time and effort to achieving those impressive results when something quick and easy would have been equally beneficial to your needs is a waste.
Tip #2. Knowing how you’re going to use the data helps
To better help you with a project, we need to know how you will use the data.
So, when starting a new project, take some time beforehand to sit down with your analyst, it’s not as bad as you think, and discuss which specific topics or characteristics will help you gain the knowledge you need quickly.
If the data will be used for internal discovery, analysts will likely approach analysis, especially the final reporting, somewhat differently than for external reporting.
Tip #3. Creating fancy charts should be the exception, not the rule
Knowing how the data is going to be presented will help your analysts avoid wasting precious computational time making fancy charts and graphs if you only need the information for internal use.
Formatting of charts and graphs can end up taking way more time than one would imagine, so an analyst should worry about pretty charts only when needed.
Another reason it is important to discuss how the data will be used is because your analyst might use a more efficient reporting structure. They may use graph and chart types that you ask for when in fact, they could have used a more sophisticated technique if they knew what the end reporting needed to show an audience visually.
For instance, conversations that ask, “Do you need bar graphs for each individual variable?” should happen a lot more often than they do.
This can become cumbersome and meticulous leading up to final presentations, but if the information is represented with clarity and efficiency using the right combination of charts, everyone wins.
Tip #4. Make sure you know what you’re getting
When your analyst gives you the final results, take the time to fully understand the interpretations, all of their possible meanings, as well as the limitations associated with those results.
If you are the one responsible for putting together results or presentations, then I suggest you double check how you are reporting those results.
I also highly recommend you send documents back to the analysts who worked on them for a final quality assurance approval before reporting.
In statistics, there are many misused nuances.
The difference between using valid, validity, sufficient, significant, caused, reject and others all have slightly different meanings for a data analyst or scientist than the general population.
So, try to make sure you understand those differences and present them in the correct fashion to avoid costly and embarrassing gaffes during a presentation.
For instance, if your analyst tells you there is a correlation between two variables, you don’t want to say, “One causes the other to happen.” Rather, you should say, “The relationship exists [with a direction and strength attached to it.]”
Or consider …
“It’s possible to run a valid experiment. There were minor validity threats. The experiment had a sufficient sample size, yet didn’t end up with a statistically significant difference in results, so you failed to reject the null hypothesis.”
Now, if you didn’t understand that last sentence completely, it’s time you talk to an analyst.
Following these pieces of advice will help you save a little money and make things run a little smoother, but most importantly, they help where it matters most – by opening up a dialogue.
Dialogue drives communication that leads to more time invested in data to discover ways to better serve your customers.