Recently, Microsoft has been hosting events for executives from more than 70 of our data and AI enterprise customers in Europe, the Middle East and Africa (EMEA) to provide a forum for participants to share their learnings and pain points, exchange best practices and build support networks. In short, we wanted to grow a collaborative community to take our data and AI journeys together. Unlocking the power of data, building an enterprise-wide data platform, data governance, data culture, data-led customer insights and data transformation have been some of the themes covered in these events.
As a fairly recent addition to the C-suite of leaders, CDOs have an extraordinary opportunity to utilize their organizations’ enterprise data assets, enable data-driven decision-making and implement digital transformation at scale — all of which are highly needed, especially in the post-pandemic period. Furthermore, CDOs are in a highly strategic, visionary role to unlock the power of their organizations’ data for AI-infused applications and long-term autonomous enterprise efforts.
But what keeps chief data officers (CDOs) awake at night? If they are based in Europe or Asia, it’s probably the late-night calls with the West Coast. All kidding aside, in speaking with hundreds of CDOs during my tenure at Microsoft and Accenture, I have found that they struggle with data and AI hygiene factors in their organizations and data ecosystems. These include data quality, data literacy and culture, analytics strategy, data governance and privacy and compliance challenges. The results of a data integrity trends survey of 300-plus CDOs support my observations:
• Data quality represents a barrier to data integration projects for 82% of the respondents.
• Their teams spend 40% of their time on data cleaning, integration and preparation.
• Only 35% state their staff will trust a data-driven insight in conflict with their intuitions.
• The majority of the survey participants (88%) report that they have difficulties with data integration projects due to a lack of staff with relevant skills.
It is also worth noting that challenges and opportunities vary by industry and geography. For instance, tech and logistics companies are leading the way in establishing analytics programs and generating insights for their stakeholders. In contrast, more than 50% of retail CDOs report disappointing or mixed results with similar efforts. Geographically, EMEA enterprises spend the most time (42%) on data cleaning, integration and preparation. We have also observed these differences in our customer and partner engagements, as well as heard them at the virtual roundtables we hosted as single-country and single-industry-focused events.
So, what can CDOs do to overcome these challenges and turn them into opportunities for their organizations? As with anything else in business, I would recommend starting with people and culture. Recently, I learned that one of our customers hired a CDCO, or chief data culture officer. I could not agree more. Yes, the people side of the equation will require slowing down to speed up later, but it is worth it. It will require a gradual journey of inclusion, onboarding, training, growth mindset and compassion.
In parallel, CDOs should be radically clear and transparent with their business strategies. A simple data and AI road map (one page or slide) with three (evolving) horizons showing prioritized industry scenarios and use cases — serving as tangible, measurable examples of how data and AI can drive business outcomes tied to the overarching strategy and industry reality — will help mobilize stakeholders at all levels.
Executing this vision, however, is a massive undertaking that will require CDOs to recruit internal (and, sometimes, external) partners, sponsors and champions as change agents and amplifiers for the mission. Once this social fabric is woven and starts to propagate across the organization, teams will lay out the operational milestones and programmatic motions, such as automating data management processes, integrating data silos, augmenting company data with third-party datasets and embracing low-code/no-code self-serve environments.
For us at Microsoft, the journey has had four milestones:
1. Form a diverse and inclusive multidisciplinary, cross-business team.
2. Move toward data modernization to enable a consolidated cloud-based enterprise data lake (EDL).
3. Use a unified data governance approach to discover and classify data across multi-cloud and hybrid estates and apply data governance policies automatically.
4. Scale data management with data standards and compliance programs.
A unified data mapping, management and governance solution that puts these steps under one umbrella is genuinely the key to success. Automated data discovery wherever it may reside (on-premise, multi-cloud or SaaS), sensitive data cataloging and an end-to-end view of the enterprise data estate, in parallel with minimizing compliance risk, will help CDOs get their well-deserved sleep back. I’m looking forward to continuing my partnerships (and roundtables) with CDOs to support each other during our respective data and AI journeys. It’s an exciting future ahead!