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Data Literacy isn’t the Silver Bullet to Analytics Success

Why devote time and resources to instructing individuals who lack curiosity or motivation? Whilst this challenging idea applies to much of how we spend our working life, it also applies to something that you and your colleagues reading this likely find yourself doing on a daily basis: working with data.

Despite the fact that data is quite literally all around us, many people still don’t look at it, either because they don’t have the necessary skills, or because they’re just not that interested. Most people know that data is everywhere and that it’s necessary,  but it seems that data as we know it simply isn’t compelling enough to get people to take notice.

Another key reason why people don’t engage with data is a lack of data literacy. Data literacy is the ability to understand and use data effectively, and it involves skills such as data visualization, statistical analysis, and data management. Without these skills, it can be difficult for people to make sense of data and extract meaningful insights from it. 

Blockers from interacting with data

In summary, there’s plenty of reasons why someone may not engage with data, and they range from lack of interest to lack of understanding, among other things. We’ve rounded up a few of the biggest blockers when it comes to interacting with data, so let’s break them down further:

Lack of time

People may not have the time or resources to devote to analyzing data, especially if it’s not directly related to their job responsibilities or personal interests. 

This may be the most realistic and challenging issue to overcome, but this is where data-driven alerting/notifications can be explored. Notifications, which are a means of providing an interface between a Data Cloud and third-party messaging service, like email, are currently in preview with Snowflake. With Snowflake building alerting into their product, more builders can provide solutions internally and externally to combat issues related to lower engagement with data, due to lack of time available. Alerting can aid in making sure your attention is only required when an alert condition is met – no more data fomo!

Fear of the unknown

Some people may be intimidated by data and may be hesitant to dive into it because they’re unsure what they will find and don’t know how to interpret it.

This is where advanced analytics can help support decision-making, with capabilities like snowpark to run dynamic models in real-time on top of your filtered set of data. Take clustering for example, I can re-run my python models simply using an Astrato UI – you can see more of that here.

Limited access to data

In some cases, people may not have access to the data they need to analyze, either because it’s not available to them, or because they don’t have the necessary permissions or technical skills to retrieve it. 

With traditional/legacy BI systems, we often look at only a portion of the data that we believe is needed, and that data is typically aggregated. Direct query capabilities, like those provided as an option by Power BI and Tableau, or natively in Astrato, ensure that all data – not just a subset of it – is fairly democratized to support decision-making, allowing users to see further into the data.

Personal biases

People may be influenced by their own personal biases and may be unwilling to consider data that contradicts their preconceived beliefs or opinions. An increase in machine learning can reduce personal bias, but may also introduce the issue of ML model biases.


People may not look at data because they have heard misinformation about it or because they are not sure how to verify the accuracy of the data they are looking at. This becomes especially true when working with multiple systems that share datasets – inconsistencies arise and it can be difficult to understand which data source to trust.

Lack of interest

Not everyone is passionate about data. Some people may simply not be interested in data or may not see the value in analyzing it. Improving data storytelling skills can help improve interest, as can using a guided analytics flow, akin to what Astrato provides with actions

Data storytelling

And finally, the last hurdle in the race to successful analytics is data storytelling. This is the age-old process of using data to tell a story or communicate a message. It involves selecting and presenting data in a way that is clear, engaging, and easy to understand. It is often used to persuade or influence others.

One way to ensure that your data tells a compelling story is to approach your data as though it really is a story: maybe it has a hero and a villain, or a clear beginning, middle, and end. And we should never underestimate the power of patterns! Our desire for order and predictability drives us to seek out patterns in the world around us. When we encounter conflicting ideas or information, our brains are naturally drawn to try and reconcile them. Contradictions can be a powerful tool in storytelling because they create tension and intrigue, compelling us to keep exploring and learn how the opposing forces will be reconciled. By leading with contradictions, we can engage our audience and keep them invested in the narrative.

Create narratives with your data

Of course, data literacy is important, but without the ability to craft a compelling narrative, the data can often feel dry and unengaging. By using storytelling techniques, we can bring the data to life and make it more meaningful and relevant to our audience. Ultimately, this leads to a greater understanding and appreciation of the information, and a greater ability to use it to make decisions with confidence, backed by data.

What this comes down to is a skill that has pained most of us at some point or several points . . . constantly even. That skill is communication. Simple frameworks like the well-known 3-point rule (allegedly used within the British Royal Family to craft their speeches) can be useful guides: 1) Does this need to be said by me on a dashboard now 2) Does this need to be said by me on this dashboard? 3) Does this need to be said by me on this dashboard now?

Enjoy analytics success

If you take one thing away from this, keep in mind that people are naturally inclined to be drawn in by how you communicate, not what you communicate. Keep it interesting and keep it alive, and remember data isn’t the new oil, it is the new water. It’s a shapeless substance; one that you look into to see a somewhat distorted reflection of your environment. Ultimately, data is a reflection – however imperfect – of the person working with it, which means that helping people understand how to work with data, and getting them engaged with it, is of critical importance. 

The reality is that you can teach people to work with data, but you can’t impose motivation or interest in data.But you can educate and inspire people to become more familiar with data, and that’s the first step in the right direction