This is a guest post by Piyanka Jain, President & CEO of Aryng, a data science consulting, training, and advisory company.
Are you a data professional burning the midnight oil to make sure your organization has easy access to a single source of truth (SSOT)? Are you frustrated to see that, despite ingesting petabytes of data and building hundreds of new dashboards every quarter, your leaders and managers still make gut-driven decisions? Do you wonder what the point of integrating data is if your organization doesn’t know or care how to use it?
Suppose the Chief Data Officer (CDO), at the behest of the CEO, asks you to lead the charge on developing a data-driven culture. The goal is for every level of the organization to optimize their decision-making process. Your first instinct might be to meet with your L&D lead to procure training for every employee to learn to use dashboards.
This is not the right approach. Learning to use dashboards is necessary but not sufficient to the cause of building a data-driven culture.
There are 4 Ds of data culture that must work in tandem to support a well-functioning, data-driven organization. They are:
- Data maturity – Does an organization have a reliable process for integrating data?
- Data literacy – Does an organization have analysts and data scientists who understand how to navigate and interpret data sets? Do other people in the organization refer to data to make decisions?
- Data-driven leadership – Does an organization have a data-literate executive team?
- Decision-making process – Does an organization require decisions to be validated by data?
In order to achieve these 4 Ds, there is a five-step process I recommend for building a data-driven culture. They are to:
- Define data literacy and data culture goals
- Data culture assessment
- Leadership workshop and planning
- Communication and execution
- Evaluation and improvement.
I discuss these steps in detail below.
1. Define data literacy and data-driven culture goals
Every company handles different data sets created by different kinds of operations and has different reasons to be data-driven. Similarly, employees of a company have different jobs that make different kinds of decisions and thus require different levels of data literacy.
A data scientist likely needs advanced statistical literacy as well as the coding chops to build models to predict customer churn, retention, response, etc. A marketing manager, by contrast, may need to know simple analysis techniques to answer questions about campaign performance, drivers of conversions, and so forth.
Typically, every company will have 5-8 distinct data literacy personas depending on their respective roles. You will have to determine what these personas are and how the members of your team fit into this personas. Then, you will have to assign reasonable goals to each of those personas.
2. Data culture assessment
Once you have determined your data literacy personas and their respective data literacy goals, you will have to assess the present state of your organization’s data culture. Use the 4 Ds for guidance.
Concretely, this consists of an enterprise-wide survey and one-on-one interviews with the leadership. At Aryng, we have translated these 4 Ds into a number of dimensions that are measured against practical demonstrations and company-wide perceptions. You might evaluate these metrics on a scale of 1-10, and arrive at a table like what we have below:
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