What's Scarier Than Halloween? Data Illiteracy.

Halloween is a holiday when we can all indulge in the darker, creepier side of life, and celebrate everything scary - turning fear into fun, be it a little spooky, bonding exercise with other trick or treaters. Because Halloween is time to turn paralyzing bone chillers into fun festivities - we take this opportunity to confront this data illiteracy terror. 

A recent Gartner survey of chief data officers found that poor data literacy is one of the top three barriers in building strong data and analytics teams.  But have no fear - learn what steps you and your team can take to use data so that everyone has time to do their most valuable activities and turn data from a seemingly ghastly trick into a fantastic treat. 

The Data Illiteracy Terror and Data-Phobia

The recent rise of big data and data science has opened many doors to make better-informed decisions and establish data culture. Leading companies continue to identify culture – people, process, organization, change management – as the biggest impediment to becoming data-driven organizations at 92.2%.

But to those without formal training or time, data remains as elusive and intimidating as ever and no one can hide. Big data is at the door, and it’s data-phobia that’s keeping your team from taking full advantage. Here is what the experts have to stay about the state of data literacy: 

“Until recently people could easily ignore data in their daily work. The company’s ‘gearheads’ and ‘quants’ were isolated in specialist departments, tech handled the mundane stuff, and managers could brush off the benefits of improved data quality with the attitude, ‘We’re doing just fine. Why bother?’ But now that’s changing. The headline result of my most recent “scan” of the data space is that fear has replaced apathy as the number one enemy of data … You can become a credible leader and dispel the fear of data in your team as well as your own fear by increasing your abilities and inspiring the entire department to embrace data.” Thomas C. Redman in Harvard Business Review
“We expect that, by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, acknowledging their extreme deficiency.” - Alan D. Duncan, Vice President at Gartner

What Is Data Illiteracy?

Data illiteracy is lacking the ability to read, communicate, and work with data.

1. Reading Data - understanding, interpreting, critiquing, and analyzing data.

2. Communicating Data - confidently speaking about and representing data accurately.

3. Working with Data - using data analysis tools to collect, transform, and analyze data.

Because the appropriate degree of data literacy required to make an impact can vary depending on the role - the details behind this definition can vary across organizations based on their expectations and need, but there are basics everyone needs to know, otherwise there can be severe consequences.

Getting Familiar With Dangers of Data Illiteracy

Unfortunately, it's easy to fall victim to the misuse of data or be a perpetrator of misinformation. The misuse, abuse, mismanaging, or wrongful deployment of information can easily happen if you are not confident in your data literacy skills or tools. 

Data Distortion.

Context is key to data literacy. Isolated pieces of information out of context skew conclusions. Regurgitating charts and other data without an understanding of the source or context leads to costly errors. You must be able to dive into details or risk biased data taken out of context. You have to trust your data sources and the way data is handled.  Bad data is costly for organizations- in the construction industry alone it’s estimated that decisions made with bad data cost $88.69 billion dollars. 

Not talking apples to apples.

Data must be an apples to apples comparison. If data is measured differently - without taking the various differentiating factors into consideration then data will be skewed. Data must be transformed with care so that it can be blended effectively in analysis.

Misunderstanding sample size.

In general, a larger data set with uniform data collection processes can provide a more reliable analysis. This includes limiting the types of data to what is most important for the analysis. Collecting a great variety of different variables can confuse and complicate the findings, making it difficult to assess useful data as evidence of action or truth.

Not understanding basic statistics.

Uncertainty is inevitable, but statistics enable us to use a limited sample to make intelligent and accurate conclusions about a greater population. But just because there is a correlation of data doesn’t mean that there was causation or a cause-and-effect relationship between the variables. It's important to know the basic statistics and what makes an insight actionable.

Ethics & Responsibility.

The ethics behind data must be considered. A key ethical issue in data is the manipulation of data to support only a particular position instead of exploring data for the whole truth. Often the same data seems to result in differing opinions or is dismissed because it does not support a certain context or point of view.

Data should approximate truth, editorialized or misrepresented data can be disastrous - it's a responsibility to handle with care.

Many stay away from data analysis because they are terrified of being unconsciously incompetent at communicating with data and presenting a bias. Rightfully so - there is no way to draw a reasoned conclusion if you do not understand the proper use of data and this ignorance can create issues that have a profound impact on organizations and societal communities.

Why Data Literacy Efforts Fail

Sometimes data illiteracy boils down to the paralysis and lack of time to dive into data and use for data decision making.  Autodesk's Harnessing The Data Advantage in Construction Report sheds light on the lack of buy-in to data - respondents felt that a third of all poor decisions made were due to bad data and 41% of respondents indicated that they saw limited, or no benefit to implementing data strategy.

Data literacy ≠ code- Not everyone wants to code.

Most data training programs look the same, and understanding tools for data analysis is the focus. Data literacy takes more than a 1–2 week-long intensive training covering software Excel, SQL, Python, and visualization tools using demo data.

The objective of data literacy sessions is for people to query the data sources themselves and get the info they need. There may be no genuine buy-in because demo data doesn’t help concepts stick, and many see coding as a hindrance to their daily tasks.

Attendees become out of practice if they don’t often code and instead focus on their most valuable tasks rather than data. After all there are other obstacles like data accessibility changes, structure changes, and new tools or multiple tools that need to be used. There is a burden of data governance, maintenance, and all in all, its a full-time job that requires due diligence and takes them away from their most valuable tasks. 

When forced to learn code, or create a dashboard once, teammates end up in the same place they were a few months earlier, except everyone’s a little more cynical and afraid of data.

How to Turn The Fear of Data Around

Let your team be vulnerable and enable them to practice, practice, practice. It goes hand-in-hand with courage. It means having the will to expose your weaknesses to whatever you might face, which is pretty scary in itself. However, much of the research that is around understanding fear has found that those more willing to be vulnerable are also less fearful.

The purpose of data analysis and creating a data culture is to make data useful.  Here are some steps you can take to make data less intimidating, and help bridge the data literacy gap for your team and start empowering them as data citizens- users of data who understand how to treat it appropriately- by using Toric to create a central location for data and eliminating the code barrier. 

Don't make data feel like data - ditch the code.

Empower your team with data at their level of use. Get rid of the manual coding and conventional data pipelines. Instead, make data useful and interactive by making data apps and interactive dataflows the norm. 

Data Apps 

Data apps are a super stealthy easy into helping your team interact with views of data - either to capture new data, retrieve a result, or to analyze a dataset that continuously changes. See our example of our Halloween Candy Database Analysis.

Learn more about data apps- read our blog “What is a Data App, and Why Your Whole Team Will Love them” 

Interactive Dataflows 

Example of the interactive dataflow view from the embodied energy report

Dataflows enable a birds eye view of your data. You can connect to data sources by dragging and dropping them into the dataflow, or by connecting with an integration. From there you can visualize all of the transformations and connections between data sets and interact with the data directly. Learn how they work in our blog, “Creating Your First No-Code Dataflow.” 

Trust the data by getting full transparency.

Look behind the sheet - data apps enable stakeholders to easily inspect the raw data and transformations as needed because everything is connected. All the data in the data app reports and it’s transformation can be traced down.  All data is in sync and you can dive into the details of any visualization or analysis that sets off red flags. 

Use data professionals more effectively.

One way to use your data professionals is to leverage pre-existing data apps, and use your team to build a library of reusable data apps. In conventional data pipelines, manual coding is required and the process is not reusable or repeatable for new analysis. With Toric, you can enlist your experts to use their skills more effectively by creating a library where data citizens and plug in their own data and have the analysis build-out for them. 

Get To The Treat! 

Start using data and utilizing your team’s talent to do their most valuable work.  Start using smart data apps and create a data safety net by eliminating code and making the entire data analysis process transparent. Don't be scared - start with a pre-built data app or speak to our team to learn more. 

Do Something Scary, and Have a Happy and Healthy Halloween!

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