Data Culture and Data Literacy Are Key to CDO Success

Last Published: Aug 05, 2021 |
Dan Everett
Dan Everett

Vice President of Product and Solution Marketing

data culture and data literacy

In 10 Ways CDOs Can Succeed in Forging a Data-Driven Organization, Gartner Inc. states, “Culture and data literacy are the top two roadblocks for data and analytics leaders.”[1] It also states, “One cannot change the behaviours and belief systems of an enterprise without changing the conversations within it.”[2] So how can chief data officers (CDOs) change the conversations they have with stakeholders?

It’s critical to first understand line-of-business owners’ perspectives, issues, goals, and initiatives, so you know how to map your data strategy to their business-value opportunities. And it’s important that you speak in their language and about their issues if you want to get commitment for and participation in executing the data strategy.

Focus On the Why

To drive the behavioural changes required to improve data culture and data literacy, focus your conversations on why people should change. An important part of the CDO’s job is to translate the “why?” Why is this important to the business? Why is this important to me? Why is collaboration between various people and organizational functions critical to success?

One method to help with why is to map the value chain of data. Let’s look at a simple value chain of data to support profitable revenue growth through cross and up sell.

First, everyone involved needs to understand that data activities like cataloging and cleansing help ensure complete and accurate data, which impacts the accuracy of analytics activities like propensity-to-buy modeling. The accuracy of the insights generated from machine learning algorithms, such as which customer segments are most likely to buy specific products, in turn impacts the response rates of personalized marketing offers. And the response rates to marketing activities in turn impacts cross-sell and upsell revenue generation and marketing campaign return on investment. A shared vision and understanding across organizational functions will help improve data literacy and culture.

What’s In It for Me?

Once everyone has a shared vision and an understanding of why that vision is important to the business—and why collaboration is crucial—it’s time to get specific about the value for individuals and teams. Clearly articulating “What’s in it for me?” is the strongest motivation for behavioral change. For example, having conversations with the following teams, regarding the value end-to-end transparency will deliver to them:

  • Executive Sponsor: Prioritize investments and activities to accelerate business outcomes. In addition, it will help you ensure teams are aligned on the correct tasks and activities to drive business value. And it will provide visibility into the status of execution and help you know where to focus when things get off track.
  • Finance Team: Better monitor and measure leads, conversion rates, and campaign costs. This in turn will help you more accurately forecast bookings, revenue, and profitability. In addition, over the long term it will provide visibility into product profitability, as well as customer lifetime value.
  • Marketing Team: Build better customer profiles that increase response rates to marketing campaigns. In addition, it will help you better understand how to deliver personalized offers into touchpoints that span business processes and interaction channels across customer journeys. And it will provide visibility into consent policies and opt-in status to ensure compliant engagement.
  • Analytics Team: Reduce the amount of time spent searching for and preparing data. This in turn will enable you to spend more time analyzing the use case to identify the problem characteristics and accurately design data science solutions. In addition, it will help increase the operationalization of models by providing a better understanding of where algorithms should be inserted into business process flows to optimize value, as well as where data quality rules should be embedded into data flows to ensure input data meets the minimum standard for accurate AI output.
  • Architecture Team: Prioritize what data to move into your cloud data lake or warehouse and what data cleansing and enrichment to apply during migration. In addition, it will provide visibility into privacy and protection policies, so you understand masking, encryption, and cross-border transfer requirements. And it will help you more accurately predict workload requirements so you can reduce costs through better cloud capacity planning.
  • Governance Team: Understand the data and data attributes that have the biggest impact on business outcomes so you can focus governance activities on the things that matter most. In addition, it will help you monitor changes in quality as data flows through business processes and applications, so you identify problem areas and remediate quickly. And it provides visibility into subject matter experts that can help with the definition of policies, rules, business glossary terms, and metrics.

With a shared vision and understanding of “why,” you’ll have a higher probability of getting the participation and engagement required for successfully executing the data strategy—for example, getting time from subject matter experts in finance, marketing, analytics, architecture, and governance teams to participate in a data governance council. It can also spur cross-functional commitment for establishing a baseline measurement of metrics so you can monitor, measure, and demonstrate the success of the data strategy. Additional examples include resources to help with the stewardship of different domains of data and support from the executive sponsor to ensure organizational alignment and resolve difference between teams and stakeholders.

This is just one example of how to improve data culture and data literacy. If you have best practices for getting buy-in and engagement from stakeholders, please comment and share them.

To hear a firsthand experience of creating a data-driven culture and improving data literacy, watch this short customer video on Spearheading Data Transformation.

[1] Gartner, 10 Ways CDOs Can Succeed in Forging a Data-Driven Organization, Mike Rollings, Alan D. Duncan, Valerie Logan, 22 May 2019
[2] Gartner, 8.

First Published: Mar 18, 2020