Five Steps to Building a Data-Driven Culture

A data-driven culture isn’t just about dashboards. Here's how you can take your organization’s data literacy to the next level.

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:

  1. Data maturity – Does an organization have a reliable process for integrating data?

  2. 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?

  3. Data-driven leadership – Does an organization have a data-literate executive team?

  4. 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:

  1. Define data literacy and data culture goals

  2. Data culture assessment

  3. Leadership workshop and planning

  4. Communication and execution

  5. 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:

 

 

3. Leadership workshop and planning

Once you have the results of your data culture assessment, read them out at an executive workshop where you discuss the data culture quotient and the narrative around it. Use the results of the assessment to determine some important KPIs. You might have identified the following shortcomings:

  1. Low data maturity – You must take urgent steps to fix the back end infrastructure issues to enable easy access to a single source of truth (SSOT). The SSOT, with proper security and governance, forms the backbone of any data-driven organization.

  2. Poor data literacy – You may need the help of expert data science mentors. Designate an initial cohort of champions to be upskilled, then use recordings of these instructional sessions, as well as the initial cohort of champions themselves, to instruct subsequent cohorts.

  3. Lack of data-driven leadership – Lay out a plan for executive coaching and upskilling as soon as possible. Without buy-in from the top, you will struggle to build a data-driven culture.

  4. Decision-making process is not data-driven – Revisit how your organization makes decisions. Start from scratch if needed.

Coordinate with the leaders of your organization to identify data literacy projects with the potential to drive the highest impact. 

4. Communication and execution

At this stage, you have finalized your plans but must communicate them to the rest of your organization. Since creating a data-driven culture is a change management process, make sure you have a cohesive message and communication strategy in place so that you get buy-in from the rest of the organization. Over time, this will solidify data literacy as a part of your organization’s DNA.

5. Evaluation and improvement

You can’t manage what isn’t measured. Ensure that your training efforts are in line with the data literacy goals you defined in Step 1, and assign KPIs that can be regularly checked as assessed.

Conclusion

The 4 Ds of data literacy and the five steps I have outlined above should give you a start on building a data-driven culture at your organization.

If you want to learn more about establishing a data-driven culture and promoting data literacy, get in touch with us at Aryng.

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