Digital transformation requires a commitment to enterprisewide data literacy if your organization is going to thrive in this changing business climate.
The chief data officer (CDO) has taken on far greater digital responsibility than her predecessor has. She spearheaded the digital transformation in the past decade, laying pipes to bring disparate data sources to a centralized data lake. She migrated on-premises databases and technology stacks to cloud systems in the past five years. Finally, data from all the different sources — such as billing, marketing, customer support, web hosting, platform, and others — are flowing into a common data lake.
Data Democratization Has Just Begun
Unlike product managers from two decades ago, today’s product manager wants to look at the user flow data on the website and design changes to UX flow to improve revenue. He doesn’t have the luxury of a dedicated analyst supporting him for every question he has about his product. The marketing manager has direct hands-on access to the CRM system. He is pulling targeted customers for the next campaign and needs to have a lifetime value score for each of the customers to target the highest value customers effectively.
To resolve the customer concerns quickly, customer support agents need access to what happened when the customer accessed the website two days ago. He doesn’t have the luxury of the SLA of one-week resolution time of yesteryears; the customer expects resolution during the call.
The CDO needs a proper plan to enable appropriate access to the right kind of data to the right person, with the right security level. Barring that, the business’s numerous stakeholders will start standing up their individual mini data marts to serve their needs. If that happens, the CDO’s past five years of centralizing data sources will amount to nothing.
What is needed is a proper data access strategy and governance for the entire enterprise. As long as the CDO office doesn’t boil the ocean and try to solve the problem for every data point collected for every use case, this is a problem the enterprise can solve within a year. Most cloud technology stacks come pre-primed for this problem with a well-defined access template attached to data elements by roles and responsibilities. When implemented well, each metric’s lineage is traceable; definitions are clear and singular. Whether the product manager pulls that data via data visualization software or a marketing analyst pulls data using an interactive query service, it returns the same results. Data democratization is complete.
Need for Data Literacy
With data access comes the supreme need for data literacy. How should the product manager turn the website click data available by sessions (it’s a mountain of data) into a meaningful UX plan to increase revenue? Unlike what many may believe, the web clickstream data isn’t going to start broadcasting insights!
The product manager needs some data skills to turn those data points into insights. They need data literacy.
They need to learn how to ask intelligent questions to data, layout a hypothesis-driven plan for those questions, pull the relevant data, and analyze it using a simple business analytics methodology to create an upgraded UX plan. They may need incremental skills in A/B testing to experiment on their various hypotheses about what drives incremental engagement and revenue.
In our work, we use different data literacy persona to personify a cluster of data literacy skills. The most commonly used data literacy persona on the business side is known as the citizen analyst or digital citizen analyst.
On the other hand, the customer support agent might need skills to effectively think and act with data and quickly pull the customer history for optimal call routing and resolution. His manager would probably need to coordinate efforts across different agents and prioritize small but powerful wins rather than reacting to data and making changes that don’t stick. The manager needs critical thinking. These managerial skills would be defined as a data-driven manager.
In essence, every individual in the organization who is accessing data, which is pretty much 100 percent of the people these days, has his or her own unique data literacy need. Our research shows two important things: first, data literacy is rapidly changing roles and responsibilities, and second each organization’s unique data literacy need can still be captured with six-to-eight data literacy personas, thus creating a scalable plan for upskilling all.
Our CDO’s digital responsibility extends beyond creating and managing a centralized data lake. The CDO partners with leadership to develop data literacy at all levels. It is the leadership’s responsibility to define goals that deliver business impact, not training for the sake of training.
Thankfully, that is not too Herculean a task as long as these five steps are followed.
1. Define data literacy goals and objectives by different persona. Map every employee to one of the personas so you know the endpoint.
2. Assess the current level of data literacy for every employee so you know the starting point. Always attach real projects at the end of the learning plan so employees can practice and put their data literacy skills to use on a real problem or opportunity. Set priorities and make your Data Literacy planning easier.
3. Create stratified learning solutions by persona. Have clear learning paths that allow everyone to improve their skills regardless of where they start. Continuously grow the organization’s data literacy capability.
4. Communicate widely, create cohorts and start people down the path.
5. Measure success, course correct, and evolve execution as needed. Use results from the assessment to set and communicate measurable goals tied to business goals and inspire everyone to engage.
A Final Thought
Data is the new currency and data literacy is the new language of business. Within the next two-to-three years, every CDO needs to create a scalable plan for upgrading the data literacy for their entire organization.
Fuente: Piyanka Jain