At the Intersection of Data Literacy & Design Thinking

/

Design thinking is an approach made famous by IDEO and Stanford’s d.school. The premise is everyone is creative and that the human should be at the center of design. It aims to provide a framework to design things that are desirable, feasible, and viable. The design thinking approach is a six-step process of framing a question, gathering inspiration, generating ideas, making ideas tangible, testing to learn, and sharing the story. Going through this process can be linear but often isn’t.

Now ask are data fluency and design thinking similar? The answer is definitively yes! Design thinking is a mentality of framing problems, generating ideas, testing ideas and telling stories. Data fluency stresses a similar process. Further, when determining desirable, feasible, and viable you certainly need to understand what the data indicates. Additionally, data fluency is always focused on the pain of people whether internal or external customers just like design thinking has the human in the center and their pain and needs.

The other thing design thinking and data fluency have in common is they are both geared at democratizing out their practices to everyone. Design thinking aims to put design in the hands of everyone while data fluency aims to put data science in the hands of everyone. This of course brings about fear by some in respective professions but it really brings about opportunity for all. This practice democratization solidifies importance and adoption. There will always be a place for those specially skilled in the respective design and data science arts, but it is time for basic practices and understandings of both to be adopted by all.

If you are a data person but looking to  learn about design thinking and human-centered design pioneered at IDEO. Or, if up to the challenge take the 90-minute virtual design thinking crash course at d.school. In a future post we will go into how design thinking practices can be used in data fluency when we discuss the Data Value Cycle.

 
 

What is this self-service BI thing?

/

What is this self-service BI thing?

Episode 001

What in the world is Self-service BI and why should anyone care about it? Is it just for analyst-types?

What in the world is Self-service BI and why should anyone care about it? Is it just for analyst-types?

One of our favorite topics to kick off this podcast! Matt has been building self-service analytics initiatives in Fortune 500 companies for 7 years now, so he’s excited to talk about it with you!

The foundation of self-service analytics is in allowing non-technical users throughout an organization to access and use data to identify insights, communicate results, and drive decision making.

Why would an organization choose to go this route? What are all those analysts getting paid to do, anyways? Well, in the age of information, there’s far more data than any single analyst team will be able to mine, process, and communicate. You want your technical people working on the really challenging problems like sourcing new data, cleaning data, and more advanced statistics & machine learning.

What this does is it frees up the business to move at THEIR speed. If they need an answer now, then you’ve given them the tools to get that answer, rather than being put in a queue. You want the data as close to the decision-making as possible, and as fast as you can. Democratizing the data can help you make that happen.

Make sure to subscribe to more episodes with your favorite podcast catcher!

Thanks and Happy Listening!

 
 

Name it and they will come

/

Name it and they will come

Episode 000

We are on a mission to help high-performing individuals become champions for a more data-driven approach in their organizations

We are on a mission to help high-performing individuals become champions for a more data-driven approach in their organizations

Welcome to the Data Able Podcast!

Dave Mathias and Matt Jesser are proud to bring you a brand new podcast all about data. our goal is to help people like you to champion data in your organization and truly transform yourself into a data-informed culture.

How are we going to do this?

We think podcasts are a great way to provide high-quality information in a tight and compact way. So we’re going to be delivering weekly, quick-hit episodes on topics that your organization needs to be thinking about. These 10-minute episodes are easy listens, and will arm you with ideas and talking-points that you can use to drive the data-culture in your organization.

We’ll also deliver longer-form interviews with amazing people with a similar passion for data, who are doing work just like you… helping change your organization with data.

Our first (zero-eth?) episode is a light-hearted look at how we came up with the name for our podcast, and what we’re looking to achieve.

We look forward to starting this podcasting journey with you! Until next week!

Thanks and Happy Listening!

 
 

Why it's critical that leaders become data literate

/

Nearly every organization is undergoing a digital transformation and as part of this data literacy or data fluency plays a pivotal role. Data like any language is effective when others around you understand it and make decisions based on its meaning. But, data fluent doesn't mean being a data scientist. Instead it means "the ability to understand and use data effectively to inform decisions" according to Mandinach and Gummer. [1] One addition to this definition would be ability to communicate with data.

Leaders with data fluency whether team leaders, department directors, or senior executives benefit. These data fluent leaders ask questions like those below but more importantly are able to make data informed decisions.

  • What are key metrics that help me understand my customer's experience?

  • Am I hiring, rewarding, promoting and training my team members to be data fluent?

  • What data can I share with others to empower them to make the organization better?

  • Am I being a good data steward and ensuring proper data privacy and ethics are being utilized?

  • How can I use data to make our operations more efficient and effective?

  • Am I communicating with data appropriately to show the value our organization

  • What new data could I seek out or capture to bring more organizational value?

  • What percentage of employees have access to self service business intelligence and analytics and have been trained on it?

  • What metrics do we track to measure our employee experience?

  • What percentage of data we capture are we using to inform decisions?

  • I understand my NPS is in the top quartile, but what is driving this metric and what other metrics should I be monitoring to understand my customer satisfaction?

  • How are we developing new products and services based upon data from our customers?

Data fluent leaders are able to help their organizations have a data driven or data informed culture. Doing so will not only lead to more fulfilling environment and to great success.

Are you a leader interested in helping your organization be data fluent? Reach out and let’s discuss if we can help.

 

[1] McAuley, D., Rahemtulla, H., Goulding, J., & Souch, C. (2014). How Open Data, data literacy and Linked Data will revolutionise higher education. Retrieved from: http://pearsonblueskies.com/ 2011/how-open-data-data-literacy-and-linked-data-will-revolutionise-higher-education/

 
 

How business and tech partners can better work together

/

The Twin Cities Data Fluency Group had its second meeting in May. This month involved an engaging discussion on “How the business can better work with analytics and tech partners.” Tricia Duncan and myself (Dave Mathias) moderated three great panelists – Nate Hallquist from Syngenta, Serena Roberts from Capella, and Jack Vishneski from ThreeBridge and consulting with Cargill.

There was a lively discussion on several fronts, but key takeaways were as follows:

  • Building relationships is key. Most information work takes teams and that means working with people. The more you build relationships the better chance to succeed as Nate mentioned.

  • Bring everything back to problem being solved. Data and analytics only serve a purpose if they solve problems. As Jack succinctly mentioned it is all about solving problems and bringing conversations back to those problems will help ensure success.

  • Trust is key. As Serena mentioned being a trusted advisor as an analyst and business partner alike is a must. Serena has the unique experience playing both roles in sales and sales enablement and building trust with both these hats has been essential to her success.

  • Rapid prototyping should be norm. Rapid prototyping is a must for dashboards and both to help ensure customer satisfaction and efficiency. These rapid prototypes can be done in a dashboard tool if a similar dataset available but just as nice it can be hand drawn on a whiteboard or paper.

In addition to these takeaways, there was a good discussion on the role of self-service business intelligence (BI) and how much autonomy the business should have and how much of it stays in the analyst, data science, or technology hands. There was mixed feeling here both on panel and in audience. Some companies have shown more success than others in distributing data fluency and technology into the business. However, there was agreement that tools are making it more able for end users to do more challenging problems.

One metaphor that seemed to resonate is treating self-service BI as a grocery store and not a treasure chest can help. As Nate described this the analyst, technology, or data science groups ensure that often used data has been made available with appropriate cleaning, integrity, and trust to business users. However, organizations need to ensure end users have proper training, tools, and help available so they can focus on conversations and insights while reducing the risk of invalid data models or technical debt.

There was a lot of overall agreement that data fluency is critical for organizations broadly and the language of data will be more easily picked up by some than others. But, to have a data-driven or data-informed culture at an organization requires your people to be data fluent.

This is a short summary of the great discussion that occurred, and all are welcome to attend the next TC Data Fluency MeetUp will be in July (date TBD). If you are an analyst or data scientist, then this is a great opportunity to bring one or more of your business partners to help further your relationship.

Thank you to Nate, Serena, Jack, and everyone that attended, and Tricia, Nate, and I hope to see you in July.