Over the last decade, organizations have invested heavily in search engine marketing, Predictive analytics for performance management and data visualization tools. These tools help users get information from huge amounts of data, but they may be more compelling if they present themselves as "self-service" or are available to marketers who do not 'may not have acquired advanced quantitative skills .
Those who are neither scientists nor data analysts able to understand the results of these powerful engines? Many research suggests the opposite.
Mynewsdesk surveyed 1,050 marketers, public relations professionals, and business owners. (Disclosure: my company was involved in designing the survey.) The results were revealing.
Only 18% of respondents reported having a high level of data literacy. (The average of the marketers was only 19% – not significantly.) Keep in mind that the definition of data proficiency involved the evaluation of data submissions, and not on their expertise: "Data literacy is the ability to extract meaning and ideas from data. A person familiar with the data is comfortable interpreting data graphs, analyzing and criticizing data submissions, and recognizing when data is used to mislead. "
When asked what prevented them from becoming more data-centric, respondents cited time (and this is always the obstacle). Lack of time comes first, followed by lack of skills and budget. And it is important to read between the lines what is not said. Most of the answer options did not even deserve half the votes, which is equivalent to a big "meh". We can safely say that becoming more data-driven is simply not a priority for many.
This study shows that while the availability and power of analytics tools continues to grow, teams deploying them may lack the skills to interpret rich data reports and visualizations. Not only are companies unable to take full advantage of their data investments, they are sometimes misled by misinterpretation of results.
Gartner predicted this growing gap – an explosion of tools analytical tools and a shortage of people able to use them effectively – in early 2018. Carlie J. Idoine , research director at Gartner, wrote "While data and analytics managers simply provide access to data and tools, self-service initiatives often do not work well. This is explained by the fact that the experience and skills of professional users vary widely from one organization to another … Training, Support and Processes Integration is needed to help most self-service users produce meaningful results. "
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What you need to know about data literacy
What can you do with your brand to solve the problem? First, let's describe what I mean by data literacy in the context of general marketing. I bring together marketing knowledge in three categories:
Understand basic statistics
Know how to query the results
Visualize data for transformation
If you are not confident in your math skills, I bet your challenge is statistics. As quantitative marketers, we are not often called upon to use university-level mathematics, except when we are asked to compile basic statistics. (May God help me if someone uses my computational skills.) Understanding statistics means understanding the concepts and understanding how to differentiate between research and the statistics used at best incorrectly. or at worst what to cheat.
As important as it is to understand basic statistics (and how some people use statistics to escape the truth), it is also essential to learn how to query the results – to dig holes in the statistics. data. Not all your dashboards and sophisticated summary reports are 100% complete.
You have to question what you see. Does it make sense? Is there a hidden factor that influences performance? What else could explain the change? What should you follow? Is something too good to be true?
Learn how to patiently and methodically question those beautiful reports produced by the technology in which you have invested. For example, a common mistake I see is when companies publish  research twelve-month performance and ignore the fact that the number of respondents is different each year, which can dangerously distort the results.
Finally, take the time to hone your data visualization skills. I wrote a bit about it last year. Understanding how to visualize data – to influence internal decision-making and to present information to an external audience – is an essential skill for marketing.
Earlier this year, I attended a conference at which a senior executive of a large biotechnology company announced something along the lines of (paraphrasing): "We can not hire Data scientists quickly enough to replace those that already exist. poached by competitors. We are training people from the inside. The company set up a training program where employees interested in data science could start a career at the expense of their employer. This is the demand for data skills in 2019.
Airbnb does the same thing with its Data University . The mission is to "give each employee the power to make decisions based on data ". The faculty has 55 volunteers who teach 20 courses a year. These courses correspond to level 100 university courses and are adapted to the specific needs of the teams. Since the beginning of the program in 2016, 6,000 employees have taken 400 courses – and most have enrolled in more than one course.
Jamie Stober, data specialist at Airbnb, explains the power of Data University: "After training, employees of these teams built their own dashboards and developed localized solutions from data, data that (the science would never have had the bandwidth to create.) Program participants felt empowered to explore their own data and use data tools to begin measuring their work , which has increased their impact and scale. "
The benefits of the program not only benefit those who are learning and using their new skills, it also frees up time for employees in demand. As Jamie writes: "When business partners can answer their own questions with the help of SQL queries and basic SQL dashboards, IT professionals have plenty of time to work on projects at the same time. impact, critical to the strategy and direction of their partner teams. "
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Data Education If You Can not Attend a University of Data
Of course, not all organizations can create a data university. What is the solution for small budgets and the first less ambitious?
I highly recommend Naked Statistics as a starting point. Charles Wheelan uses examples of presentation of news and business data – good and bad – to teach a range of concepts and tricks. This is an excellent book for beginners, but it will also entertain data enthusiasts.
Another classic is How to Lie with Darrell Huff's Statistics . (Published in 1993, he inspired the writing of Naked Statistics).
To learn more about data visualization, I recommend The Wall Street Journal Information Graphics Guide . It's an excellent introduction to do's and don'ts with clear and simple visual design.
If you are interested in anything other than the basics, Scott Berinato (publisher of Harvard Business Review) has published an excellent guide called Good Charts .
I have also strongly recommended: Telling Stories with Data: A Guide to Visualizing Data for Professionals by Cole Nussbaumer Knaflic.
For those who have the zeal to go further, ask yourself if learning to use Table (paid version) would help you. Tableau gives wings to your spreadsheet or your SQL data. You are no longer locked into static data presentations. Instead, you can attend meetings with interactive dashboards that can change on the fly … filter, sort, and even edit visualizations in real time. The integration of this tool allows you to spend more time analyzing data and extracting information, and less time handling colors and borders. (To learn more about how Tableau works for organizations, read this case study REI . (Note: I have no commercial interest in Tableau except for the pure fandom .)
If you do not do anything else to improve your data skills, you should commit to never confuse correlation and cause-and-effect relationship. This is the most common error in the data I see in marketers (and who will judge you). The mere fact that two factors are correlated (eg, cheese consumption and tangled sheets) does not mean that one influences or causes the other.
I share my computer skills and I encourage you to develop your skills with deepest humility. I've made some colossal mistakes. Once upon a time, I had worked as an accountant and just say that a tense Tuesday in March 2004, I had given an unconscious employee a windfall of pay per accident while I was working on payroll. (She must have made it, unfortunately.) I am perfectly capable of doing silly math, but by expanding my computer skills, I have expanded my marketing horizons and I encourage you to do the same.
The author [Clare McDermott] will speak of new errors to be avoided in the Content Marketing World data in September. Register today to learn from her and from over 100 other experts. Use code BLOG100 to save $ 100.
Cover image of Joseph Kalinowski / Content Marketing Institute
Note: All the tools included in our blog posts are suggested by the authors, not by the CMI editorial team. No post can provide all the relevant tools in the space. Do not hesitate to include additional tools in the comments (from your company or those you used).