The Human Interfaces of Data - Jordan

Jordan Morrow of Bodhi Data

I saw Jordan speak at Sanjeev Mohan’s pre-Big Data London event, that he hosted with Ravit Jain. I remember thinking during his talk that he would be a great guest for this series, and we got to talk briefly at the reception afterwards (I think it was a reception… there were canapés 🤷‍♂️).

So here we are - had I not attended the event, this post wouldn’t have happened. The value in community and meeting other data folks in person is immense.

Jordan is a passionate speaker and author on data literacy - he has even written a trilogy of books that you will see being sold at data conferences. Data literacy is something we’ve briefly touched on in other posts in the series, but it’s good to take a closer look at it in this post.

This will be the last post in this series, perhaps not forever but at least for a good few months. In the coming weeks, I’ll try to analyse the topics covered in a wrap-up post for the series so far.

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Give us a bit of an introduction about yourself:

Education

I have my undergraduate degree in Economics from the University of Utah.  I have my MBA from Westminster College (now Westminster University).

How you came to data in your career

I started using data and/or information in my first job, at the end of my undergraduate degree at the University of Utah.  I started at American Express.  I eventually moved into a more official data role at Amex and had my first ideas, you could say, around creating data literacy.  I then was hired by Qlik to do data literacy, even though we didn’t call it that at first.

[David Jayatillake - I've seen you speak passionately about data literacy, which is how we came to meet. What does it mean to you?

I've seen people define it in various ways, from the features of a data catalog to data evangelism inside large corporates. I imagine that you may define it differently.

Jordan Morrow - Data literacy, to me, is the ability to read, work with, analyze, and communicate with data.  Everyone already has data literacy abilities, they just may not know it.  Can we empower people to utilize data more effectively in their careers?  That’s what we need to help people do.]

Where you have worked in data and what roles you have held

I have held various roles in data and analytics:

• Business Intelligence Manager (if I can call it that) - Amex

• Analytics Curriculum Manager (a precursor to Global Head of Data Literacy) - Qlik

• Global Head of Data Literacy - Qlik

• Director of Data Skills - Pluralsight

• VP and Head of Data and Analytics - BrainStorm

• Owner and Founder - Bodhi Data

[David Jayatillake - You came to data literacy pretty early in your time in data - why was that? Why was it so important to you? If you haven't already, can you speak to what it means to scale data in an org?

Jordan Morrow - I love to teach and I am not sure why it was so important, but maybe that is what led me to want to grow and drive it.  One reason I came to it was, while at American Express in the BI Manager role, I was in charge of training.  I started a learning plan to teach basic statistics to people.  This, you could say, was my first pre-cursor to data literacy, and the plan was denied by my EVP.  But, I probably just kept growing in my thoughts and mind around teaching analytics/statistics to people, and Qlik gave me a chance to do it.

Scaling data appropriately in an organization is important.  Not everyone needs to be a data scientist, but they need to develop skills with data.  With that in mind, data literacy should empower people where they are in their roles, to utilize data in an effective manner.  If they move to new roles, their data literacy can be re-evaluated.  But, if organizations want to get the most out of their data with data democratization, then learning needs to be done appropriately and not as a one-size-fits-all.  Scaling the learning appropriately matters.  Give data to people in an appropriate manner.  And create the culture to utilize data effectively.]

If you’ve moved on from a data practitioner role - when, where and why

When I went into data literacy at Qlik, I was basically out as a practitioner until I was VP and Head of Data and Analytics for BrainStorm, Inc. in Utah.  I am more on the data literacy and strategy side than on a practitioner side, now. 

[David Jayatillake - From seeing you speak, this seems like a natural move for you. Is that just my perception, though? Are there things you miss about being a practitioner? Do you see yourself returning to being a practitioner at some point?

Jordan Morrow - For me, this is where my skill-set should be.  I work more around the strategy, getting value out of data you may say, and data literacy.  I do love mathematics and statistics is cool, so I wouldn’t mind working more with these, but it has to come at the right spot and time.  I don’t know if I will ever return as a full practitioner, but maybe will get my hands on data more often.  In reality, my strength and abilities put me more in the leadership side and not the practitioner side.]

Human Interfaces

Ones that existed

I had human interactions and interfaces directly in my role as a Business Intelligence Manager.  I trained end users on the data.  As Global Head of Data Literacy, I had to help create the field and spoke in the world, which enabled me to interact with many people.

[David Jayatillake - Did you find having this role and purpose changed people's attitudes towards working with you vs having a more typical practitioner role, like BI manager?

Were you able to more effectively get them to work with you and adopt data? Why was this?

Jordan Morrow - This role definitely changed how people worked with me.  It expanded the network I had.  When I was a practitioner, I had a smaller group of people I worked with.  Being Global Head of Data Literacy, I was invited or had the chance to speak to organizations around the world, maybe in person or virtually.  Being in a product agnostic role as Global Head of Data Literacy, I was able to converse with various companies and people.  Through data literacy, I hope I helped them to work more effectively with data.  I hope I helped people be more comfortable with data.]

Ones that worked well

The ones that worked well were those that were open to data literacy and to conversation.  Data literacy afforded me the opportunity to talk on a topic that, a lot of times, the person instigating the discussion was interested in.  This enabled me to meet people and have good interactions with them. The ones that worked well, including data literacy, had buy-in from someone.

Ones that didn’t work well

That didn’t work well - when they didn’t work well, it was more because the people didn’t understand data well enough and so an accountability was held where there was a lack of understanding. This was interesting, as there was an expectation in place, but lack of understanding hurt the process.

[David Jayatillake - Can you explain this a bit more deeply?

Jordan Morrow - What I mean by this is, the leaders wanted data but didn’t have a good enough grasp of the data and what it takes to work.  So, the data team was held to a standard that wasn’t the right standard.  Thereby causing issues.  Instead, a better understanding from leadership and a good investment would have helped, plus understanding the work needed to get data projects completed would have helped with timelines for the team.]

If you compare the times that did work well and didn’t, can you pinpoint the circumstances that were the cause of the difference

I think the thing I can pinpoint is lack of understanding and the need to get buy-in more effectively.

Ones that didn’t exist, but should have

This is the buy-in side of things.  With buy-in, you can get more done with a good data literacy program.

[David Jayatillake - Do you have a process or framework to solicit buy-in and then not only keep it, but enhance it over time.

Jordan Morrow - With buy-in, you need leadership and everyone else to buy in with data work; at least leadership and those impacted by the data work.  I don’t necessarily have a framework that is for every organization, as organizations are different, but a key to getting buy-in is solid understanding and proper communication.  Communication is a key to data and analytical work.  The right communication to leadership and/or end-users for the buy-in matters.  More, and not less, communication may be better in most situations.  Now, it needs to be effective and not just telling people what to do.  You need conversations and to open doors for questions.]

Why should the interface have existed? 

They needed to understand the overall picture of data better in one case, another they needed to understand what it takes to get data work done, including proper investment.  You can’t just hire a couple people, not funding sufficiently, and think that will work.

What were the consequences of it not existing?

In one case, there wasn’t a data literacy program, at least when I chatted with them. In the other, the data didn’t work the way it should have done.

What are you doing now in your current role, that helps make human interfaces in data better  -

As owner and founder of Bodhi Data, I speak and/or advise on things.  I hope to help humans become more data literate and, hopefully, that will help organizations succeed more with data.

Tooling - Hopefully, with data literacy, individuals are able to utilize tooling better in their organization.

Process - Overall, with proper change management and data culture work, organizations can capitalize on data and make better data-driven decisions.

People - This goes without saying with data literacy on the people side.  We want individuals to be able to use data more effectively and we want better dialogue among the work; communicating with data better.

With proper data literacy, data culture, and change management, I hope that organizations can seize data more effectively in the organization. We want people to be more confident and comfortable with data.