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The role of the chief data officer (CDO) has evolved more over the last decade than any of the C-suite. A position once laser-focused on regulatory compliance is today one of the most strategic enterprise decision-makers. As companies plan for a rebound from the pandemic, the CDO holds the key to accelerating analytics and artificial intelligence (AI) investments. And while firms consider slashing IT budgets, their ability to compete – or even survive – depends on their AI capabilities. The investment in strong leadership of AI innovation at the C-level is one that can’t be put off or slowed down.
CDOs’ Time to Shine
Last year we saw what can happen when investments in Data Quality and democratization aren’t made. For example, last spring the world’s premier health agency, The Centers for Disease Control and Prevention, set out to hire a CDO as the agency faced multiple challenges related to COVID-19 case reporting. The CDO’s job is to make sure Data Quality, among other data issues, is proactively addressed. But it’s abundantly clear that many organizations have not invested enough in their data and technology – and that there’s a void to fill in data-driven leadership.
In private industry, data leaders are gaining rapid traction as important decision makers. While a 2019 O’Reilly report named culture as the main bottleneck holding back enterprise AI adoption, the tide is turning. The role of the CDO has expanded into empowering broad and consistent use of data for every decision, from new product innovations to supply chain shifts. And a recent IDC study found 59% of CDOs already report to a business leader like the CEO .
Additionally, 80% of CDOs’ top KPIs are tied to business goals, such as operational efficiency, innovation and revenue, and customer satisfaction and success. This is good news: It means the culture is changing. More CEOs know data isn’t a means to an end; it’s a platform for innovation. And as the pace of innovation accelerates, we’ve reached a point where it needs AI to scale.
AI Needs Data, and Data Needs AI
AI is trained on data, but for AI to move from shiny novelty to truly valuable technology, it needs to be fed an enormous amount of information. The data used to train AI models must be high quality and bias-free, or else the output will be useless. But with the overall volume of data center traffic expected to reach 20.6 zettabytes by next year , data simply can’t be managed via a linear, human approach.
Traditional approaches to handling data often leave information in silos, meaning only one group may have access or be able to contribute to a dataset, leading to incredible inefficiencies in AI development. It also leaves significant room for human error, which can cause all sorts of problems: low-quality recommendations for your customers, failed processes, and inaccurate forecasts that send supply chains into chaos. AI doesn’t just need data; data needs AI.
AI can automate and simplify tasks related to data and other technical work. Machine learning (ML) methods can take over repetitive tasks, freeing developers to work on high-value projects, such as deep learning efforts. As AI improves data understanding and identifies privacy and quality anomalies, it augments developers, analysts, stewards, and business users, speeding up tasks through automation and augmentation with recommendations and next-best actions. In other words, AI can accelerate end-to-end processes across your entire data environment.
Bringing It to Life
The McKinsey Global Institute estimates AI will add $13 trillion to the global economy over the next decade . Yet data shows that only 8% of firms engage in practices that support widespread adoption . Most firms apply AI to a single business process – and this is where they fall short.
As the champion of all things data, the CDO has the ability to drive digital and cloud transformation initiatives, and in turn, speed up AI investments. Without a data-focused leader, data teams are pulled in a million directions, whether it’s helping sales answer a question or building products and dashboards for the finance team. The CDO can set data teams free by championing innovation priorities that will ultimately democratize data for everyone in the enterprise.
For example, by deploying AI-powered DataOps (data operations) – an automated, process-oriented methodology, to improve the quality and reduce the cycle time of data analytics – the CDO can eliminate roadblocks to making data accessible to more users and cross-business unit collaboration. This is a game changer enabling data teams – including data engineers, data scientists, and data analysts – to be more iterative and obtain data that’s in usable shape for AI and ML, faster.
By speeding up the process of finding, cleansing, and integrating the massive volumes of structured and unstructured data from all corners of the enterprise into fully governed and consumable datasets, companies gain a secret weapon: the ability to leverage AI for everything. However, this is only possible at true enterprise scale when the knowledge and skills of your data and IT teams are augmented by AI and ML.
The CDO is also responsible for driving enterprise-wide upskilling as the business needs more employees who are data literate. According to Deloitte, while 81% of leaders expect the use of AI to increase over the next three years, more than half (54%) say they don’t have the right skills in-house. For AI to truly be an innovation platform, it can’t only be usable by a small group. By spearheading the development of employee data skills, the CDO further frees up its most technical talent to focus on AI, ensures the company’s leaders know how to capitalize on AI, and trains non-technical workers on how to use it.
I’ve seen firsthand the significant impact of an AI-focused enterprise. For example, in life sciences, it often takes over a decade to bring new drugs to market due to the process of lengthy clinical trials and regulatory approvals. Even with rapid scientific advances, the pace of innovation still moves at a snail’s pace. Data scientists are working on predictive models to help discover and deliver revolutionary therapies faster that significantly improve patient outcomes. To a life sciences company this can be worth billions. For thousands of patients, it can be the difference between life and death. From a business perspective, massive amounts of many types of data are required to quickly discover a range of potential new therapies and rapidly recruit and retain patients in clinical studies. In these situations, the CDO must hold the reins.
The Race to the Future Is a Race for Data
Over the next three years, we will create more data to fuel AI than we did over the past 30 years . AI will be the driver of the economy of the future. As AI adoption increases, the potential for its misuse by leaders who don’t fully understand what’s behind its decision making will grow. But businesses that are early AI adopters with data leaders at the C-level will win the AI race, while those who wait to invest in AI will be ultimately left behind.
 IDC, “Chief Data Officers: The New Business Leaders,” August 19, 2020
 Cisco Global Cloud Index, November 19, 2018
 McKinsey Global Institute, “Notes from the AI frontier: Modeling the impact of AI on the world economy,” September 4, 2018
 McKinsey, “Breaking away: The secrets to scaling analytics,” May 22, 2018
 IDC, “Global DataSphere,” May 8, 2020