3 minute read
Modern marketing is powered by data. Data drives your segmentation strategy and unlocks your personalization project, and these data-enabled outcomes are only a sampling of what is quickly becoming table stakes in today’s competitive environment. A fundamental step in becoming a data driven organization is data literacy. It’s impossible to overstate the importance of fostering data literacy within your team and organization as a whole.
Data literacy not only propels marketing performance forward, but it fosters informed decision-making, and prevents costly mistakes and potential ethical issues from arising. Let’s take a look at how data literacy will empower your team to ask the right questions and avoid common misunderstandings or misrepresentations of the data.
Statistics are powerful tools, and are often used by data-literate individuals to provide concrete evidence to support their arguments and to represent data in meaningful ways to the rest of the organization. However, those members of your team who may not be as comfortable or familiar with statistics may risk creating misleading representations of the data. An individual may think they understand the data or how it’s relevant to the rest of the organization, yet inadvertently represent the data in a biased or misleading way. This creates a potential domino scenario in which organizations are making critical decisions based on faulty or misinterpreted data.
For example, consider someone using data from one department where the definition of a customer differs slightly from that of another department. The marketing department may define a customer as anyone who has purchased in the last year, while your sales department may view a customer as anyone who has ever made a purchase from your organization. If both of these departments ask for insights based on customer data, the data developed from each of those samples are going to be different. Receiving and working off of the wrong data will most likely create a disadvantageous—or potentially even disastrous—result.
This is just one of the many reasons why having a common language is so important for organizations. Without commonly used definitions, one group could potentially mislead other departments if they’re not discussing data using the same dictionary, as it were.
This confusion also leads to arguments or unproductive conversations. Being able to ask the right questions, such as, “What do you mean by ‘customer’ in this instance?” is a valuable part of being data-literate.
You can avoid these problems by creating data-literate teams and ensuring the use of consistent definitions throughout the entire company. Having more people around the table who are comfortable and confident using data means there are more people who can dig deeper when things are unclear and notice these issues before they manifest into a problem. A data-literate team works to support one another in this way by collaborating and holding one another accountable.
Ethical issues also arise from intentional cherry-picking of data. Cherry-picking is when someone selects certain segments of data from a larger set to support their argument, even though their cherry-picked data may not be representative of the whole. Such cherry-picking may be intentional, may be the result of unconscious bias, or may simply stem from a lack of data literacy.
For example, an employee conducted a campaign that received overall poor response rates. That employee might be feeling motivated to try and find a certain cell or group of customers within the campaign that had a higher response rate to demonstrate that the campaign was somewhat effective. The overall effect of doing so may result in a perception that the overall campaign was a success. If there are other data-literate team members present, they may notice this cherry-picking and ask questions about how the data was selected, as well as how the results fit into the larger campaign. By having these conversations, organizations can avoid poor decisions that may have been made based on misleading data.
Another example is the representation of data in charts. Results can be easily misinterpreted when scales are manipulated or groups incorrectly compared, such as when the wrong type of chart is applied to data that doesn’t truly reflect what the data reveals.
In the end, statistics are tools that require vigilance in order to make sure they are represented in the proper context. Having an involved team ask clarifying questions about the charts is a great way to avoid intentional misrepresentation.
Data transparency is the result of creating a collective understanding of data analysis throughout your organization. When you build data-literate teams they will help one another avoid errors and bias, and they will hold each other accountable for providing accurate information.
Developing an informed team is the most important thing you can do to avoid misunderstandings and potential ethical dilemmas. You will find that data literacy is most effective when more and more members of your team are better informed. You will then be able to create and enable a more open and collaborative work environment where learning is encouraged through mutual communication, support and respect.
For more information about how to develop data literacy in your organization, talk to Trendline today!
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