3 Best Practices to Transform to a Data-Driven Operating Model
Put the customer at the core by bringing business and IT together.
by Lisa Sheth
posted on 08-21-2019
Companies have widely embraced putting the customer at the heart of their organization as a lever of growth and satisfaction — yet execution has proven tough. According to research from the CMO Council, only 14% of marketers feel that their company has achieved customer-centricity. Although challenging, truly operating with a customer focus is not impossible.
In 2016, we saw an opportunity at Adobe to better align our business with our customers’ needs. Enter our data-driven operating model, or DDOM: a way of working that centers the business around the customer journey and drives the business toward strategic objectives with informed insights. Eric Cox, our VP, digital media GTM strategy and operations, describes DDOM this way: “From individual contributors to the C-suite, any decision that impacted the overall customer experience had to be made with insights, and not purely intuition or educated guesswork.”
DDOM has been remarkably successful in driving efficiencies and value in our Creative Cloud business. Still, attaining the data-driven insights necessary was a considerable feat. Collaboration between business and IT was crucial to our success. This means that even in workstreams overseen by IT, such as data integration, the business still serves as an important contributor. From our experience, we learned that every company should consider a few areas of collaboration. Here are three best practices that will help make your data integration and DDOM transformation as impactful as possible:
1. Use a top-down, bottom-up approach to make your data integration reasonable
For many companies, data is scattered throughout the organization, siloed among teams, and overwhelming in its scale. A collaborative “top-down, bottom-up” approach can help you achieve balance. Starting from the “top,” our business looked at our customer journey stages to determine the most important business questions and which KPIs could lead us to answers. The business side has the closest view of the customer journey, so they spearheaded this effort.
IT then identified the data assets generated across the customer journey that fed into the business KPIs. This “bottom-up” element required IT to map out these data assets and their sources, and document the level of effort required to integrate each source. The IT organization gained a sense of which quick wins to pursue and which sources would require more resources and effort. That information would prove useful in developing a road map for data integration.
With the business identifying priorities and IT identifying effort, we gained a starting point for building a single source of truth.
2. Design accessible and adoptable experiences around your business personas
A single source of truth will have minimal impact unless sufficiently adopted, however. Our IT organization understood this, and our CIO felt strongly about empowering everyone in the business, regardless of function or technical prowess, to explore the data firsthand. With this in mind, our IT organization took a “customer-centric” approach of developing different reporting experiences for different personas, with the business as their customer.
IT interviewed several business stakeholders to better understand the needs of these personas and collaborated with product designers to address those use cases in the reporting experiences. This process resulted in a number of different tools — from a centralized dashboard that everyone in the business can use to track Adobe’s performance to specialized reporting instruments for data scientists with more complex questions. By tailoring these experiences to the business audience, IT made it easy and intuitive for everyone in the business to delve into the data.
3. Provide an accountability structure to preserve your newly unified data
Once DDOM has been introduced to the wider business, the growing number of users may potentially reintroduce the issue of differing interpretations. To combat this, a data governance strategy should emphasize cross-organizational accountability.
At Adobe, each DDOM KPI is assigned a VP sponsor, a business steward, and a technical steward. The VP sponsor serves as the “face” of his or her KPI and is held responsible for reaching KPI targets. The business steward manages the KPI’s definition and usage, and the technical steward manages the transformation and accuracy of the data feeding into the KPI. With these cross-organizational owners, the business covers any questions about what a KPI means, and IT covers any questions about the data behind the KPI. This eliminates confusion or conflicting interpretations of the data.
Work together to succeed together
DDOM ultimately aims to eliminate the silos that hinder an organization, whether data silos, functional silos, or others. Similarly, business and IT cannot work in isolation if they hope to achieve a successful transformation. Alex Amado, VP of experience marketing at Adobe, stresses how key this collaboration was for Adobe: “We had long been a data-driven marketing organization, but it was only through this cross-organizational partnership with IT and the development of our DDOM that we were really able to move from data to actionable customer insights. Ultimately, this has enabled us to deliver better customer experiences at every stage of their journey.”
Business and IT must have equal seats at the table. Otherwise the foundation of DDOM can fall apart. By identifying KPIs and associated data assets, developing a centralized dashboard as well as personalized reporting experiences, and assigning a cross-organizational team to sponsor each KPI, you can accelerate your transformation — and center your company more completely on your customers.
Special thanks to the contributions made by Adobe colleagues Aini Sun, senior digital strategy analyst, Kasey Haas, digital strategy engagement manager, and Nathalie Tadena, digital strategy associate.
Topics: Digital Transformation, Analytics