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I was reading the recent blog from journalist and consultant Bernard Marr on 'Beyond Big Data: Why data diversity is a crucial success factor’ – and it really made me think about which data sets many businesses are often missing that could give them even greater, workable insights?

Would love to hear about any current examples of or plans to use different types of data sets that you have?

And don’t miss the original blog for more background on the importance of variety in data: https://thedataliteracyproject.org/posts/beyond-big-data-why-data-diversity-is-a-crucial-success-factor

Thanks for sharing that article. Nice read! Especially for a Liverpool fan like me :)

Thanks for sharing, @eelcolambers! This is also a great example of the implications and importance of data in industries outside the standard tech, education, health, etc. With data, organizations across the board are better equipped to make sound decisions that will yield long-term results. Not sure if anyone else saw this article: https://www.dailymail.co.uk/sport/football/article-7095041/Data-experts-Klopps-charisma-turn-Liverpool-kings-Europe.html, but the Liverpool Football Club (owned by the same parent company as the Major League Baseball team Boston Red Sox) owe their recent 2019 UEFA Champions League win (in part 😊) to a team of data analysts, leveraging data insights to help the team gain a winning edge. 

I agree 100% with the earlier comments about needing to be clear on the benefits to the business - but I also believe the best way to seek the investment of others is to demonstrate that you’re also investing in yourself. It doesn’t have to be a monetary investment: think about enrolling in some free, online training modules - or subscribing to data literacy podcasts and newsletters.

Show your manager that you’re serious, that you can apply some of the learnings you’ve picked up through your own development - look for opportunities to learn “on the job” from someone who is more familiar or more comfortable with data - your enthusiasm and commitment to your own personal development will go a long way!

Hi all,

Just wanted to share this nice synonym I discovered when rewatching the movie Moneyball. For those who don't know the movie yet: Moneyball is about the gamechanging insight to address baseball from a statistical and analytical perspective rather than opinions from "experts/trainers" and making decisions on who to buy based on gut feeling.

In this specific scene I think it is nice to see that there is just a misunderstanding of data analytics and the approach to buy players based on data analytics and statistics, rather than that the players are young or popular.

This could have easily been a team meeting where one person brings in a dashboard and others are questioning his insights because they don't understand the data and the insight go against their gut-feeeling

I am looking for the same thing mentioned here - the ability to gauge the data literacy health / score for an organization. I like the Data Literacy framework so looking to see if there is a way to utilize the individual testing here to correlate with an overall score for an org / team.

Thank you,

Kevin Conley

Hi everyone,

@Spencer_Sobczak there are two resources I'd like to point you to: 1. if you're a Gartner client you can access their Data Literacy: Organisational Assessment which is included in their "Information as a Second Language" research. This is very much a maturity assessment tool. 2. I'd encourage you to also have a look at our work through Data To The People - we offer an organisational assessment (which is measured at an individual level and then aggregated to your organisational attributes) - you can read more here: www.datatothepeople.org

Please let me know if I can be of any assistance to you in this area.

Thanks

Jane

We are at the start of our enterprise journey towards data literacy. As part of our planning stage we are looking at how we might track individuals journeys through the learning tracks. This is to formulate part of our measurement of progress / success of our Data Literacy program.

From what I can see there is no way of being able to get an enterprise view of progress through the free learning material on the Qlik Continuous Classroom. How are people managing and reporting against this at an enterprise level?

Interested in your experiences and recommendations.

Thanks!

This is a very, very interesting and important question. As you can imagine, there aren't KPIs that directly measure a score of data literacy. There are some proxies organizations can use:

• Number of courses taken
• Number of individuals who have enrolled and completed the data literacy program
• App creation, clean-up, and usage

Another way for organizations to measure it can come through the assessment Qlik has put on the Data Literacy Project and watching the movement/changes in those who took the course: did they have a large group from data dreamer to data knight?

Now, that said, I do see direct measurement on the horizon. This is naturally a next step in the journey and progress of data literacy programs and initiatives around the world. The ability to measure and utilize data literacy programs, with tangible movement and work, is key.

When looking at data literacy inside of a company's culture, I think it's important to ask questions like: How well does this organization support curiosity? Do we make time for people to learn new skills? Are we okay if we try something and it fails?

Because you can have BI skills, you can understand that data is a critical part of the equation, you can even speak data, but if the human side is missing I don't believe you'll have the success you really want.

To me, so much about data literacy is being willing to ask questions and knowing that data can help you find the answers. It's just starting there. Even simple questions like is priority A or priority B more important can change mindsets. For example, in product development we are starting to analyze things like revenue opportunity, revenue loss, user satisfaction, user efficiency, people cost to build, and technology cost to build for each new feature we choose to develop. By analyzing real data combined with deep business understanding, we can start to make our case for our priorities. In order to do this, we have to pause, take a breath and give ourselves time to be curious, to find the sources of data that will help us, and to take a chance and get it wrong. But we are definitely shifting the conversation.