Why We Should Be Excited About Data Literacy

Allen Hillery

Hello! My name is Allen Hillery and I’m happy to be teaming up with Matt and Dave to get you excited about Data Literacy. I’m a data champion who has worked with business and data teams throughout my career playing the role of ambassador and coaching them on how to better leverage data. I’ve had the opportunity to work in companies with varied data maturities ranging from reactive to more thoughtful on executing results. Like most of you, I aspire to work in a truly data informed organization where everyone is literate to understand the context of their data they’re analyzing and the value it brings internally and externally. 

So my question to you is - How comfortable are you with data? Does the thought of getting your hands dirty with data excite you or make you want to cringe? According to Forbes, there are 2.5 quintillion bytes of data created daily. If you think about it, data is a major part of our lives.  Each one of us, generates data as we move from google searches to shopping with a club card at the supermarket, not to mention data created by Internet of things. In the office, are you the go to dashboard expert or maybe you’re resident data whisperer who massages insights out of your analytics teams? 

Being data literate means you have the ability to read, understand, create and communicate data as information. We are on the precipice of an exciting time, as we have superfluous data available to analyze.  This data can present information that provides better customer experiences and enables your team to identify which segment would be best served by your products. While the amount of data being created can sound daunting, the evolution of the tools and infrastructure to help us navigate this landscape is intriguing! 

People aren’t going to go to BI, BI has to go to to the people.
— Nick Caldwell

Tech executive, Nick Caldwell said, “People aren’t going to go to BI, BI has to go to to the people. This is already happening in a big way.” The staggering amount of data that has been made available to us has hit a tipping point where data analysts have to enable non technical business partners to develop insights on their own. This trend has caused a shift towards more intuitive self-serve tools.  At the same time, the proliferation of opportunities to learn query language are seemingly ubiquitous.  

In addition to trends pivoting our work cultures to being more data informed, the growth and learning opportunities that will come from leveraging both data and data literacy have me really psyched!  Companies are beginning to realize the importance of investing in their employees’ data literacy. AirBnB is a shining example of investing in data literacy through the creation of their data university. This effort was made with the belief that every employee should be empowered to make data informed decisions. It took roughly two years to launch but one of the amazing results is a reported 50% increase in active use of their internal data platforms. Another benefit is that it frees up data teams to concentrate on more complex tasks. 

AirBnB Data University

Sharing success stories, like AirBnB illustrate the importance of empowering employees and customers with data. Think of all the apps and services you use right now. You’re leveraging data when you are booking that next AirBnB, searching Yelp for food recommendations and hailing your lyft to get around. BI is coming for you and you’re more acquainted with data than you realize. So maybe you’re the resident data wrangler on your business team who realizes that data is not as aloof or mysterious as you once thought? Maybe your knowledge of the business combined with your new found data sleuthing skills has put you on a direct path to being a data champion lobbying for more training? Then you’re at the right place! We’re here to reassure you that you don’t have to be a data scientist to be data literate! You just have to be open to getting your hands a little dirty with understanding how to leverage data!

About the Author


Allen Hillery

Adjunct Professor at Columbia University,
Writer and Editor at Nightingale, a Publication

Allen serves as part time faculty at Columbia University’s Applied Analytics program. He has extensive experience in developing and executing data analysis and integrating results into marketing programs and executive presentations. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.

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Do your metrics have a positive ROI?

Metrics are important but not everything needs to be measured in a data-driven world. It is easy to add a metric without really understanding the upstream and downstream consequences. That is why it is important that your metrics have a positive return on investment (ROI).

ROI is a simple calculation where the benefits are divided by the cost and if the benefits are greater than the cost you have a positive ROI. This might sound obvious but most of the time metrics are implemented in silos and upstream and downstream impacts are not understood and accounted for.

So what are the potential benefits and costs of metrics:

Potential benefits:

  • Enhanced clarity by leadership resulting in enhanced decision making outcomes

  • Changed behavior that results in more revenue generating activities or reduced cost generating activities

  • Benefit of not having to maintain other metrics and related costs if a metric is replacing one or more metrics

  • Reduced friction between areas with metrics that align teams or departments with organizational objectives

Potential costs:

  • Time spent in calculating metrics by individuals

  • Storage and processing costs in creating metrics

  • Time spent rolling out new metrics

  • Time spent communicating out and reviewing metrics

  • Changed behavior that results in less revenue generating activities or increased cost generating activities

  • Enhanced friction between areas with metrics that misalign teams or departments

Each metric that is implemented should have a clearly positive ROI. The purpose of calculating ROI is not just to come up with a precise measure though. We think calculating ROI related to new metrics is most valuable because it provides a process for deliberate thought around implementing new metrics and maintaining old metrics.


Just as importantly as determining the ROI on new metrics you should make sure the ROI on existing metrics still is positive. While metrics are not something that should be changing all the time, they should be reviewed and updated in a deliberate fashion.

Hopefully this post helps you think more about your metrics and their ROI. Much of being a data informed organization is simply the critical data thinking that goes about aligned to desired organizational mission.

This is the first in a series of posts we have planned around metrics. We think metrics should be front and center in an organization that values data because metrics are something that people are already familiar with and use regularly. More importantly, we think good metrics that are well communicated can unite and accelerate an organization.

Looking to implement metrics & KPIs in your organization? Check out our latest workshop “Designing Metrics & KPIs That Work” in Minneapolis, MN on October 15th and in Chicago, IL on October 22nd!

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I Started Learning Data... Now What? [Guest Article]


I came across a recent new resource, the Data School by Chartio. They’re building lots of free materials on how to learn the basics of data analysis. If you’re still working on integrating data into your day-to-day work, Data School will be a nice resource and reference guide for you!

Below is a guest post from Matthew David on Data Literacy and the Data School


Education in the data space is largely focused on helping individuals get jobs in data. They give you just enough information to get you past an interview. This typically involves entry level SQL knowledge, basics of analysis, and some proficiency with a Business Intelligence tool.

The Data School was created to pick up after people get jobs and start running into multiple people querying for data and conducting analysis. New problems emerge.

  • Analysis is affected by biases

  • Dashboards that a lot of effort went into go unused

  • People second guess your results

  • People want to be taught SQL

  • Data is messy and oddly represented

While these may not be asked in your interview these are challenges that every company who uses data faces. Solving these problems increases trust in an organization and leads to more informed decision making.

The Data School is a community driven library of free web books covering the data challenges companies face as they have more and more employees using data.

So far we have written:

Learn SQL - An interactive SQL tutorial to learn the syntax

How to Teach people SQL - Gifs and visuals to help people build intuition for how SQL works

How to Design a Dashboard - How to apply design thinking principles to the process of building dashboards

Misrepresenting Data - How people accidentally present false or misleading results

SQL Optimization - Common techniques for improving query speed and database performance

We are currently writing on Data Governance, Data Modeling, and the Fundamentals of Analysis. We write these books both internally and with the help of many data professionals. 

Check out our site:

Join our community: Slack

About the Author

matthew david.jpg

Matthew David

Product Lead - Data School at Chartio

I am a Product Manager focused on making it easier to make decision based on data. With a lifelong passion for data, I’m focused on building great content for analysts who are getting started in their data career.

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What kind of games do you play?

This might be a question you answer with chess, basketball, Call of Duty, Overwatch, tennis, poker, or Dungeons & Dragons. But, now if I ask you “what type of game is your current change or transformation effort at your organization?” What is your answer? Most people I have asked that question are not sure how to respond. We look at games as something we do for fun and leisure and sometimes will incorporate into a learning activity but we generally don’t consider games serious activity.


There is the concept of finite and infinite games that James Carse detailed in his book “Finite and Infinite Games: A Vision of Life as Play and Possibility.” Carse’s main concept is that there are both: a) finite games; and b) infinite games. Finite games have fixed boundaries and rules and that there is someone that wins which results in the game ending. On the other hand, infinite games that have no fixed boundaries or rules and the purpose is to continue the game indefinitely with no ending.

One type of game is not better than the other. They both serve a purpose. But, it does matter what type of game is intended and played by those playing.


Friction can arise if you are playing a finite game in an infinite-game way or vice versa. There are also “advantages” and “disadvantages” you will have if you are playing a finite game and others you are playing with are playing an infinite game.

Getting back to my question above, most change or transformation efforts in organizations are treated as a finite game. That is there is a start and an end and there are rules laid out with an objective goal in mind. Accordingly, an organization’s players, namely employees and other stakeholders, look to play a finite game and win.

While treating change efforts as a finite game clearly helps organizations plan and invest, does this approach hinder sustaining change? My proposition is yes. This is because sustaining changes like digital, data literacy, customer-centric, and product-oriented transformations have no finish. They need to be treated as a game that continues and without winners and losers but rather how an organization and its stakeholders benefit from playing the game.


Maybe you buy into this concept and your next question is “how do I do it?” Here are some general things to take into account as you design your transformation game.

  • Establish leadership support for an infinite game approach.

  • Communicate and incentivize people involved in leading the transformation to design and support an infinite game.

  • Establish and support councils and communities that will help drive transformation and sustain it.

  • Establish metrics that incentivize and infinite game.

Thinking of your large transformations as an infinite game will help ensure they sustain and have maximum benefit. Good luck on your transformation game and hope it is well played.

No matter what happens keep playing and have fun at what you do.

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7 steps for getting started with data as a business person

Getting started in data can be overwhelming in today’s world. The hype machine of Big Data and Data Science makes it feel like you need to learn 3 different coding languages, 4 BI tools, and have a PhD level skill set in machine learning and statistics.

Case in point. A couple weeks ago, I was in Chicago doing a Data Visualization and Storytelling workshop. After the day-long training, an attendee asked if I could chat. She had been tasked with building some executive dashboards for her small organization and fell in love with data and driving more meaningful analytics. So she started doing some research about moving into the field. But after reviewing several job descriptions, she felt completely lost and pretty discouraged. There was no way she would qualify for those kinds of jobs without going back to school and spending countless dollars and hours.

I assured her there were LOTS of jobs out there for her skill sets, which included Excel, business context, communication, project management, and translating needs into requirements. But, those aren’t obvious to a person just getting started.

If you’re a business person and want to get deeper with using data here are my seven recommendations to get started:

Ignore the hype

There is a real need for great data scientists in this industry. But you’re not going to be one of those. At least not in the next couple years. The good news is that for every 1 data science job, there are 10 business analyst jobs that are just as critical. People who can translate the business. Who can project manage and communicate effectively. Who can drive change management and adoption of data-driven approaches. Those are the jobs for you.

Identify and focus on your unique talents

Focus on the skills you DO have, not the ones you don’t. Perhaps you have an accounting background. Maybe you are great at training people or speaking? Do you like listening and empathizing with your peers and leaders? Find what makes you, uniquely you. I bet your organization will benefit from combining whatever that is with data.

Raise your hand for data-related projects

You DO need some understanding of how data works, and you need to prove it. I once hired a person with virtually no professional experience over a person with 6 years of data 7 analytics experience. Why? Because the no-experience person during a summer internship had raised her hand to do some reporting, found Tableau, tried their free trial and got the whole department to start using it. Organizations want people like that.

Pick a tool

There are a mind-boggling number of data and analytics tools out there. Nobody can learn all of those tools. Pick one and get started. Most of them behave similarly enough that once you learn one really well, you can pick up most of the others well enough. Remember, you don’t need to be everything to everyone. Get good at one thing and build to the rest.

Take a class or workshop

You don’t need to go back to school to be a great analyst. But you can easily pick up some of the basics through cost-effective online or in-person programs. Udemy, LinkedIn Learning, Udacity, and Coursera all offer good self-paced programs for reasonable prices (stay away from the “data science” programs to start). Want a more guided and personal format that’s still cost effective? Check out our programs from Beyond the Data, which are tailored to business professionals like yourself.

Consume lots of blogs & podcasts

There are so many great free resources out there from leaders. Just start reading and listening. You’ll pick up all kinds of useful information on how to become great at using data. Blogs like Storytelling with Data, Flowing Data, and Visualizing Data. Podcasts like Data Skeptic, Data Stories, and Present Beyond Measure. We’re also pretty partial to our own podcast: Data Able.

Join a community and learn from leaders

Getting connected to other people like yourself is THE best way to jump start your new direction toward a data career. You’ll meet influential leaders in your local area who know the right people and can help you navigate your local market. I guarantee they will be willing to coach and mentor you along the way. That person from Chicago? She started networking and within a week had eight (8) different leaders reaching out to HER about their open jobs!

I can hear it now… “That’s all fine and good, but the job description says I need 3 years of SQL and 5 years of Python!” Yep. HR put those requirements on the job description. One of three things will happen if you apply:

  • The hiring manager actually needs those specific skills and you won’t get the job. You probably don’t want a job that technical anyways.

  • The hiring manager is willing to overlook some or all of those technical skills because teaching technical skills is way easier than teaching people/business/soft skills.

  • The hiring manager just copied and pasted the job description from a different role and doesn’t really care if you have it or not… they were just trying to weed people out.

My point is don’t let yourself be stopped by silly things like “x years of z tool.” You have unique talents that you can bring to the table. Fuse those talents with data, and you’ll be a data rockstar in no time.

Good luck and happy analyzing!

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