managemnet company strategy managemanet 8 Strategies for Chief Data Officers to Create — and Demonstrate — Value

8 Strategies for Chief Data Officers to Create — and Demonstrate — Value

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The role of chief data officer (CDO) was only established in 2002, but it has evolved significantly since then. In one recent survey of large companies, 83% reported having a CDO. This is not surprising: Data and methods to understand it (analytics and AI) are very important in contemporary organizations. However, what raises eyebrows is that the job of a CDO is not very clear. Sixty-two percent of the CDOs surveyed in the research we describe below reported that the role of the CDO is poorly understood, and that incumbents often meet with diffuse expectations and short terms. There is a clear need for CDOs to focus on adding tangible value to their organizations.

Part of the problem is that traditional data management methods are unlikely to provide tangible value on their own. Many non-technical executives do not fully understand the CDO’s job and struggle to recognize when it is being done well. CDOs are often asked to focus on preventing data problems (defense-oriented initiatives) and such data management projects as improving data architectures, data management, and data quality. But data is never perfect, meaning executives are often frustrated by the state of their organization’s data. While improvements in data management can be difficult to identify or measure, major problems such as hacks, breaches, lost or inaccessible data, or poor quality are easier to identify than improvements. .

So how can CDOs demonstrate that they create value? The main ways that data can add value to companies is by enabling them to understand and predict business performance and customer behavior, and embed it into products and services – all initiatives aimed at violation. CDOs, then, should help companies achieve value through better data use and consumption.

That is the main focus of a newbie research project sponsored by Amazon Web Services contributed by all three authors. It included a large-scale survey of 250 CDOs who attended the MIT Chief Data Officer/Information Quality Symposium, as well as in-depth interviews with 25 prominent incumbents in that role. Of the CDOs surveyed, 41% said they define success by achieving business goals – more than those who measure success in terms of managing change or shifting culture (19 %), technical achievements (5%), avoiding serious data problems (2%), or a similar combination of these factors (32%).

Based on the insights of this research, we describe several steps of value creation below. We’ll start with some methods that apply to each type of organization, and then describe some that depend on the maturity of the company’s analytical and data management using CDO.

How CDOs Create Value

Take responsibility for analytics and AI.

These initiatives are seen as delivering the most value: 35% of CDOs surveyed believe focusing on a small set of key analytics or AI projects to deliver the most that amount. Most CDOs (64%) also spend their time enabling new business initiatives based on data, analytics, or AI. That makes them – officially or unofficially – chief data and analytics officers, which is a fast-growing variant of the CDO title. Many CDOs commented in interviews that the combination of managing supply and demand data is effective in delivering value.

At lower maturity levels, focus on a few key projects that have value to stakeholders.

If your organization is early in its data and analytical journey, select a few analytics and AI use cases to develop based on consultations with key stakeholders. Ensure that some projects are successfully deployed. And don’t boil the ocean – modernize the data environment as specific analytics applications or AI use cases are developed. Then business leaders can see the connection between data modernization and the business value it can create.

Focus on data products.

Data products are combinations of data and analytics/AI to achieve a specific result for a customer or employee. An example might be a new simulation model to determine whether wealth management customers will survive their savings, or an attrition model to predict employee departures. Adopt an analytics-based data product focus, which covers all activities from the idea to the deployment and continuous maintenance, is a good way to ensure the creation of value. Product focus ensures that data scientists, data engineers, and other members of a data product team don’t just create algorithms, but work together to deploy entire business-critical applications. Thirty-nine percent reported that they have “adopted a data product management orientation with product managers.” This is a new concept, so many adopt a data product study is surprising.

Manav Misra, the chief analytics and data officer at Regions Bank, ensures that each of the data products developed by his team is successfully deployed and the company’s value is carefully measured. For each data product they have quarterly steering committee meetings, where the business team — the leaders of the business or functional unit that promotes the development of the data product — reports, and Misra’s team attends the meeting.

Measure and document results.

Carefully measuring the results and value of key projects, sometimes in collaboration with the financial organization, helps CDOs demonstrate and communicate value. Sebastian Klapdor, CDO of printing and design services company Vista, is also a strong advocate of data products, and ensures that all of Vista’s data products are impactful by evaluating them every quarter with a sign-off of any monetary benefits from the financial organization. In just a few years his CDO organization has documented $90 million in additional revenue – an impressive number for a company with $1.5 billion in 2021 revenue. Some CDOs also create online dashboards to illustrate their organization’s achievements and value in relation to data and data-driven business results.

Build relationships with peers and business leaders who get it.

Successful CDOs find business leaders – and business segments – who already value data to a considerable degree, and who can be partners in delivering data-driven value. Data, analytics, and AI initiatives require significant change not only in technical areas, but also in processes, culture, skills, and customer/supplier relationships. They cannot be successful without the strong support of senior executives. CDOs require close and trust-based relationships with senior executives.

Strategies for Advanced Companies

Other valuation methods depend on the company’s prowess with analytics, AI, and their data management fundamentals.

More sophisticated companies may focus on data management.

Data management is a top priority for CDO activity in our survey, but it is a difficult way to achieve value. This involves changing behavior and asking data users to perform data management activities that are not part of their defined jobs. Because of the difficulty of effective data management, only those CDOs who have established value through other means want to make it a priority. Some CDOs attempt to establish “governance by design,” where data systems and structures enforce the proper use of data through data architectures and reusable data properties. However, it is still early days for this approach, and it also requires a high level of sophistication in data management.

Advanced companies must work to create a data-driven culture, even if it is difficult to demonstrate value immediately.

A large (69%) percentage of CDOs spend a large portion of their time on data-driven culture initiatives, and it’s clear why: 55% perceive a lack of a data-driven culture as a key challenge to achieve business goals. Culture initiatives often include data literacy programs and attempts to instill data-driven decisions throughout the organization. However, these cultural activities also involve behavioral change and may be slow in coming. Therefore, CDOs must undertake culture change in a scalable manner if they are not already bringing significant value through other means.

Build analytics and data infrastructure if your organization is sophisticated.

Some CDOs of relatively advanced analytics and AI companies emphasize that completing key projects alone is not enough. They feel that CDOs will ultimately need to build an infrastructure to facilitate the use of data, analytics, and AI throughout the company.

Todd James, who leads data and AI for 84.51°, the data science subsidiary of The Kroger Co., said: “One set of strategic use cases is not enough. That creates a set of point solutions. You need to be able to scale by having a set of reusable analysis capabilities…We’re trying to create a composable [built from modular components] set of analytics and AI applications accessible through APIs.” Similarly, a leading enterprise data bank and machine learning is heavily focused on scale and infrastructure development for in machine learning. He noted in an interview: “With ML, we’re on our way to platforms that everyone can take advantage of, with both standardization and automation. We want to remove arbitrary uniqueness, and remove temporary ML platforms. ” The bank also built a feature store: a repository of available variables for ML models.

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There is little doubt that organizations need chief data officers and that the job is here to stay as long as its occupants add value. Others clearly do. The job may have short average terms, but 30% of CDOs in our survey have held their jobs for more than six years. When CDOs adopt these and related approaches to creating tangible value from data, analytics, and AI, they can be instrumental in transforming their organizations to become more digital and data-driven competitors. As Bill Groves, a veteran CDO who has held roles at Walmart, Honeywell, and Dun & Bradstreet, says, “It [the CDO function] not a service organization; it is a transformational organization.”

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