The post discusses the complex relationship between legality and ethics in data usage. It highlights various factors that influence this dynamic, such as changing global attitudes towards data privacy, security concerns, and evolving legal standards.
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The future of self-service analytics is bright, but could things like AI help analysts do their jobs even better?
Self service analytics isn’t going away anytime soon. In fact, we think it’s only become more prevalent!
Why is this? Well for starters, there’s more data than ever before! And our BI, Analytics and Data Science teams just simply can’t (and shouldn’t) keep up with the demand. This is a great problem to have, but will require shifts in the traditional analyst paradigm.
Leaders are finally starting see the value in their data, and in order to get them what they need, we need to move faster, getting the RIGHT data, in the RIGHT hands, at the RIGHT time.
So what about Artificial Intelligence? Is it going to eliminate the need for analysts, data science, etc.? The short answer is no… but it IS going to require that the consumers of this information are capable of a baseline understanding of how data works, how math works, and how to communicate what’s being created.
We hope you enjoy this episode. Until next week!
This month the Twin Cities hosted Startup Week. One of the many great sessions was about the history of data visualization, delivered by Matt Dubay on behalf of the Twin Cities Data Visualization Group (TC Data Viz) (of which I’m an organizer). There was an amazing level of engagement and energy in the room and for the topic. As a side-note, I encourage you to check out the TC Data Viz group if you haven’t already. It’s a place where all are welcome - beginner or advanced - technical or business users – and our tools are agnostic, whether open source or proprietary software. We provide a fun and creative space to share ways to display data in our businesses and communities.
One of the main themes discussed during the session was around data literacy. One astute person noted that IT and self-service BI cannot drive the destiny of data literacy within organizations. Instead, it needs to be something solved by the business users themselves. Only then will self-service BI truly succeed. I couldn't agree more! Data fluency is an upcoming challenge that business leaders, managers, and individuals need to make time for, if they’re going to create data-savvy organizations.
In related news, the September 2018 McKinsey Quarterly published an article entitled "Why data culture matters". The whole article is great and highly recommended, but one key insight I want to touch on was that “Data culture is decision culture". The takeaway here is that organizations shouldn’t "… approach data analysis as a cool 'science experiment' or an exercise in amassing data for data's sake. The fundamental objective in collecting, analyzing, and deploying data is to make better decisions". One other thing I want to touch on is their call for the “democratization of data" and its importance in a data culture. From the article: “… get data in front of people and they get excited. But building cool experiments or imposing tools top-down doesn't cut it. To create a competitive advantage, stimulate demand for data from the grass roots."
Certainly, executive buy-in is important for resource allocation and overarching strategy, but executives don't make most decisions. Organizations succeed by the many decisions each employee, contractor, and customer make each day. Empowering and encouraging those stakeholders to get excited about data whether it is educational opportunities, competitions, data-for-good initiatives, or other ways to help invigorate and empower data culture at the grassroots level is essential.
So a little homework this week:
Executive: Identify a way to empower and encourage your organization to support a grassroots-level data culture. What change can you support and encourage at the grassroots level so that everyone not only wants data but needs data to survive?
Managers: Identify a way you can empower and encourage your team to support a grassroots-level data culture. What new decisions can you or your team harness new or existing data to make better decisions than you had before?
Experienced contributors: Identify how you can better use data to make better decisions and even demand data that had not been used before to make decisions? Further, how you can you help support newer contributors in this effort?
New contributors: Provide a fresh insight on how your organization can better encourage using data in roles. Your fresh perspective has a distinct advantage of seeing what could be as your are not encumbered by what is or was.
Now go and do your part changing the data culture at your organization!
We help high-performing individuals become champions for a more data-driven approach in their organization. We believe that data science is only part of the equation.
Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every front-line specialist in your organization.
Data literacy is a fast-growing topic. But what is it? And why should organizations care?
So what is this new concept we’re hearing a lot about these days? Data Fluency or Data Literacy is getting a lot of buzz right now. What is it and why should i care?
We describe it as: Framing problems, applying data, making data-informed decisions, and being able to communicate with data (through storytelling, or data visualization).
Another key aspect of a good data user that isn’t taught in textbooks or online coding classes? Empathy. The ability of the data user to understand the needs of her audience, craft the right narrative and deliver the right answer to the right person at the right time. You may even consider putting empathy in your job descriptions going forward!
So is data literacy only for analysts and data scientists? Absolutely not! We think product teams can benefit from using data. We think HR teams, Finance teams, Operations teams, Sales teams and Marketing teams can all benefit from data literacy. It’s a tool in your toolbelt to help you become a better marketer, finance leader, or product owner.
We would be remiss if we didn’t share a few of our favorite resources for further study. First, there’s Cole Nussbaumer’s fantastic blog Storytelling with Data. We also love Kate Strachnyi’s Story by Data blog. And check out Jane Crofts’ company, Data to the People who are building data literacy assessments for organizations all over the world!
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.
Great products, insights, and experiences require a human-centered and data-informed approach. Let me, Dave Mathias, help you and your team through my fractional CPO, hands-on consulting, coaching/training, and speaking services.
Make sure to check out all the great thought leadership around product and analytics excellence shared at gobeyondthedata.com.
