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4 tips for team collaboration, inspired by data analytics

This week, we interviewed Roberto Coronel, an expert in data analytics and Director of Technology at Del Valle University, to gauge tips that anyone can use, in any industry.


What most irritates Coronel is when processes are inefficient, leading teams to create work for themselves. We all want to stop creating work for ourselves, right? So we picked his brain to give you 4 tips to apply data analytics and business intelligence theory to any project at work.


Before we start, what is data analytics?

A data analytics system consists of data gathering, data storage, knowledge management, and analysis. After all this, you can make an informed decision about problem-solving rather than relying on anecdotal advice or a hunch.


And that definition brings us to 1st tip:

1. Diagnose the problem with data collection.


If you’re trying to build a strategy to deal with a perceived team problem, don’t react to estimates, or one occasion of a problem. Before reacting at all, or even taking the time to formulate a plan:

  • Gather data

  • Write it down

  • And ask yourself: Is this really a pattern?

This works when you’re managing up and horizontally too. If you want to go to a team lead to ask for help, the first thing they’ll ask is when and how. You’ll need examples.


I'd like to exemplify this with a story from Coronel's old team: One team member wasn’t doing a great job. Let's call him Joey. Coronel thought this might be a problem after an incident in which Joey couldn’t explain one of his projects back to the team lead. So, how could Coronel know his own leadership wasn’t at fault?

He gathered data:

  • Joey had asked for this responsibility.

  • Joey had been involved in the definition of the project, verbally.

  • Joey had been introduced to the right stakeholders.

  • Joey had also been involved in other projects that used the same technical skills.

Joey was also an internal hire to his team, which meant he had a former boss that Coronel could talk to. Having spoken to the former boss, Coronel led worked out that Joey had been trained in a paternalistic structure, mostly following detailed instructions such that even “project management” implied very little initiative in Joey’s book.


Coronel spoke to Joey about why he hadn’t met any of his deadlines. The answer was, to paraphrase, “I was waiting for instruction from you.”


All this was Coronel gathering data to ensure he understood the problem before ideating a solution or creating a new process. Now you’ve gathered data, you can move on to the next tip.


2. Apply data analytics project planning at all times.

Project planning in data analytics is perfectly applicable to any project, work-related or team-collaboration related. You always need to know:

  1. What your goal is

  2. What does the software/process need to do

  3. What or who your team lacks to reach this

  4. Who else needs to be involved

Given this information, you can:

  1. Design product/solution

  2. Validation and testing stage

  3. Launch

Let’s come back to Joey, did he continue waiting for instructions?


Joey had been given the responsibility of creating a syllabus planning software, but he wasn’t quite ready for it. He was waiting for his team lead’s direct instruction.


So, Coronel set out to train Joey in the data analytics project management methodology. Here’s what they did:

  1. Together, they made a clear definition of the project.

  2. Coronel requested Joey put this into detailed documentation.

  3. They laid out a plan together ahead of time, including who he would speak to and how.

  4. Then, Joey was empowered to act on each step from product design to testing, on his own.

The plan went from complicated, to straightforward so that Joey could easily execute it on his own.


3. Identify the simplest solution.

From the perspective of someone in data analytics, there’s little rhyme or reason to the way the rest of us operate. According to Coronel, we end up implementing excessively complex processes. And how does a data analytics expert solve problems simply?


The university faced a problem at the beginning of 2020’s pandemic: no one checked their emails or knew their email address or password. This wasn’t a huge problem for a long time. But when Covid hit, classes went virtual. To log into the videoconferences, generate groups, register attendance, and participate in sessions, teachers and students needed to access using their university email addresses.

The university set about making sure everyone had their email addresses. The solution proposed was to communicate with each student. But how could they do that, without an email address?

“They were going to email their secondary addresses if we had it, call every student 1-by-1, instruct tutors to set up meetings with every student to tell them “this is your email, remember it to log into class remotely.” and ultimately, there was another problem beyond that sheer complexity...” said Coronel.


They didn’t know what the email for each student was because there was no logic, like firstname.lastname@etc. This means that the Dean’s office needed to recover every email, manage the knowledge, distribute it correctly, inform tutors on how to help, and multiple other steps.


This started to sound like a project management nightmare; none of the departments in charge could work out how to solve the comms issue without multiple steps.

The data analytics team was looped in at this point to build a cross-departmental solution. Coronel listened to this complex plan and said: “why not generate new emails?”

This would allow for one mass communication: "your email is yourstudentID@university.mx"


Why did only Coronel, their resident data analytics person, suggest this? Well, none of the teams involved, Communications, Marketing, Operations knew you could easily re-generate emails.

This is a simple solution. But it might not have been suggested if the right people weren’t in the know. Which leads to the fourth and final learning:

Don’t just share solutions but share your problems widely, someone unexpected might have a solution.


4. Share your findings transparently.

Every time you face a complication, don’t be protective of your knowledge. Ask yourself, who can learn from this, and who might be able to advise on this?


Once the university knew the students all had their emails, which was something so simple, the data analytics team talked widely about the simple solution. This actually led to another simple solution that wouldn’t have been possible before, or without other teams: A single sign-on solution.

All these small solutions lead to a company-wide advantage. For this reason, collaborating and putting all your cards on the table is my last learning from data analytics that applies to team collaboration.


In summary: 1. Diagnose the problem with data collection.

2. Apply data analytics project planning at all times.

3. Identify the simplest solution.

4. Share your findings transparently.


Would you add any tips? Which of these can apply to your workplace?


Read more workplace applications of science-based theory: Process is about people.


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