This year, the already tenuous role of the Chief Data, Analytics and AI Officer (CDO/CDAO/CDAIO) has become even more precarious. Many companies are experiencing a disengagement and realignment of their responsibilities as leaders in enterprise data and AI.
These roles are still relatively new. CDO work was set up at a major bank in response to the 2008-2009 financial crisis and has since expanded into industries as diverse as pharmaceuticals, healthcare, consumer goods, entertainment and federal government. Survey data shows that between 2012 and 2023, companies that appointed a CDO increased from just 12.0% to 82.6%, with responsibilities expanding over time to include analytics (CDAO) and AI (CDAIO). can now be used. However, only 35.5% of his major companies report that the role is successful and well established, and only 35.5% of companies report that the CDAIO role is well understood internally. Only 40.5%. Clearly something isn’t working.
For some data and analytics leaders, 2023 felt like a return to the early somber days when they failed to fill their roles. Financial turmoil and the explosion of generative AI have forced them to focus on defensive risks and regulatory tasks instead of growth-focused forward-looking efforts. , customer acquisition and creation of new products and services. Corporate leadership is calling on her CDAIO to introduce GenAI’s potentially transformative capabilities while avoiding harm. This is a heavy-handed balancing act with technology that presents great risks and opportunities.
With more companies wanting and needing CDAIO, the role is as difficult as it has ever been and often fails. Here are five steps companies can take to solve this problem.
What’s wrong with CDAIO jobs?
As co-authors, we have first-hand witnessed and participated in the rise and evolution of the CDAIO role. Randy has over 20 years of experience advising leading companies on their use of data and analytics. Allison has been an Industry CDO for five years and currently advises CDAIOs and companies on how to deliver business value. We both agree that this role can feel impossible, but the current iteration contains the foundation for a better, more effective version of the job. I also believe
First-generation chief data officers were often hired by large companies in regulated industries such as healthcare and finance. Initially, the role was understood as a defensive role focused on control and risk rather than a business role, even though both functions use the same data and analytical skills. The transaction data that banks use to detect fraud patterns is also used to uncover existing or potential needs of their customers, but businesses invest in the former and not the latter. As the focus shifts to commercializing data, companies increasingly view this as a technical and people issue rather than a business issue. They have invested heavily in technology and people, built a data infrastructure and a team of data engineers and data scientists, but they are not sufficiently focused on the importance of business relationships and the most important business challenges. did not.
As a result, companies were not getting what they wanted from their data programs. 91.9% of companies report achieving some measurable value from their investments in data and analytics, but only 23.9% say they have built a data-driven organization, and only 23.9% say they have built data. Only 20.6% of companies reported. culture. CDAIO was put in charge of large-scale projects that required huge investments and touched all corners of the company, often failing to deliver tangible returns. Even if you do exactly what is asked of you, it can be difficult to claim success.
We believe that two main factors have caused this situation. It’s the wrong focus and lack of trust.
The role’s focus should have been on business outcomes, not technology or infrastructure issues. That means identifying the problem you’re trying to solve for your customers, prioritizing the use cases with the highest business benefit, and taking advantage of cross-pollination capabilities whether or not your goals are achievable. commercial, risk management, or both. “The hardest part of the job is knowing what problem you’re trying to solve for your customers,” says Kathy Kozilkoff, principal decision scientist at Google and a pioneer in the field of decision intelligence. says.
Lack of trust is a similar factor. Business leaders must believe that their investments in data, analytics, and AI are delivering business benefits—that their investments are being put to good use. When business value is not clearly delivered, that trust erodes and business leaders are reluctant to invest further. CDAIO has built a governance infrastructure of people, policies, processes and management models in an effort to unify ownership of data trust across organizations, especially in large enterprises. These efforts are complex, often unpopular, and their benefits difficult to quantify. Since virtually every problem in the digital economy can be described as a data problem, it is difficult to achieve victory without agreed metrics against which progress can be measured.
How to fix
Progress in the new era of innovation has a beginning and an end that can be difficult to measure. It’s fair to argue that data and business strategy are out of alignment, that governance efforts are too clunky to be broadly adopted and measured, and that discipline is incomplete rather than senior management and board priorities. . The advent of generative AI has magnified these issues, creating new trust, quality, and ethics issues that are making headlines and attracting the attention of executives and boards.
Companies can and should fix how they manage data, analytics and AI, and set CDAIO’s role for success. This requirement will grow even further, especially when 83.9% of businesses plan to increase their investments in data, analytics and AI next year. Here are some concrete recommendations companies can take today to fix his CDAIO role and generate business value from their investments in data, analytics and AI.
Make data your business.
CDAIO has long promoted the importance of data literacy, but has been inconsistent in implementing strong governance, policies, standards and other practices. The areas where the data is most mature and disciplined are typically finance and compliance functions. Success in these areas, reinforced by executive and board engagement, serves as a model for the company.
Philipp Lambach, Chief Artificial Intelligence Officer at Schneider Electric, a global leader in energy management and digital automation, explains how Schneider has built a culture that is everyone’s business. I will tell you.
To get serious about data management, you need a dedicated organization. To support this goal, we decided to carve out data from IT and focus it on enterprise-wide governance, business, and performance issues. So he decided to create two roles: Chief Data Officer and Chief AI Officer. A key part of any data-driven initiative is having a single source of truth within the company and making high-quality data easily accessible to all decision makers across the company.
Make business leaders advocates for data projects.
Business leaders must become advocates and advocates for their investments in data and analytics. Successful data leaders are critical partners to their business leaders and come to trust them as right-hand lieutenants who provide critical data and decision points that drive successful business outcomes. CDAIO, no matter how well-intentioned, should not try to impose an agenda (“Data and AI are great, we should do more”). Find business her leaders who are ready to become a trusted partner through results that drive data and AI within their business areas and build trust.
Review all data and AI investments to ensure funds are being used appropriately.
Distinguish between “nice to have” investments and “need to” investments. Only continue with those that are currently delivering measurable business value to your organization or that can demonstrate a rapid path to value in the short term. Businesses must refocus their investments on the capabilities they need and need to grow and compete. Data analytics and AI leadership requires time, attention, clear and effective communication, and storytelling skills to articulate needs, establish realistic expectations, and elicit buy-in.
Shift to an ecosystem mindset.
To get the most out of data and AI, it’s important to foster partnerships and collaborations with vendors, universities, and other partners. Schneider’s Rambach added:
The new nature of competition isn’t really about technology. AI technology is advancing too fast for that. What matters is the value you provide to your customers. And whatever value you provide can be enhanced through partnerships. We open our IoT platform for third-party innovation, enabling partners to develop new applications using our software development kits and innovate to improve building efficiency and sustainability. will do so.
Please proceed with caution.
Generative AI presents a game-changing opportunity, but Rambach reiterates the importance of understanding the risks and proceeding cautiously, as in the following model.
…in particular, by allowing more users to easily and quickly access large amounts of diverse data, it exposes enterprises to new kinds of vulnerabilities. Now is the time to establish data governance and cybersecurity measures to use these new capabilities responsibly. Companies and users should always approach generative AI with caution and focus on confidentiality to ensure that users do not upload sensitive information to publicly accessible AI chatbot platforms. The secure/private version of LLM should be preferred.
Many CDAIOs lead corporate committees made up of risk, finance, technology, cybersecurity, legal/ethics, privacy, human resources, and business unit leaders. These teams need higher status and responsibility. Companies also need to add data, analytics and AI expertise to their boards. Only 23.8% said the industry is fully committed to the ethics of data and AI. AI and data privacy, governance, and ethics issues pose a threat to enterprises if not managed responsibly and effectively.
With more companies getting serious about CDAIO capabilities, now is the time for data and AI leaders to step forward and show how they contribute to the business value of their companies. Companies with a clear vision of how to create business value from their data and AI investments will be the companies most likely to survive the next decade and beyond.
