Creating an office of the chief data officer is the first step in developing a data-driven culture and maximum business value.
We’ve come a long way from the first website, which was published on August 6, 1991. The Internet has over 1.94 billion websites. Over seven billion search queries a day are conducted worldwide, and over 15% of those are entered into a search box for the first time. Data is transforming how we do business and, more importantly, how we make business decisions. However, 51.8% of the traffic is solely from machine bots; the remaining 48.2% is from human traffic.
From this ongoing surge of data has emerged the chief data officer role—and, more recently, to support that role, the office of the chief data officer.
Establishing the right structure can have a positive impact on organizational transformations to drive a data-driven culture. Let’s address four questions that clarify the value of the office of the CDO:
- What’s its purpose?
- What are the primary office functions?
- What resources and skills are required?
- What are the major duties of the office?
The CDO is an executive responsible for enabling and championing value creation for the organization through the use of data assets internally and externally. This includes governance, planning, definition, capture, usage of, and access to data and information. Generally, the CDO has accountability in three areas: data management, analytics and technology.
- Data management captures the care protection and governance of data from establishing a strategy for designing the implementation policies for governance.
- Analytics includes any capabilities required to analyze data to transform it into useful insights.
- Technology covers the data architecture, infrastructure, and services for the ingestion, movement, monitoring, and storage of data.
The CDO is accountable for capturing high-quality and timely data and leveraging data assets to stakeholders. To fulfill this mission, we need to understand the purpose of this role. The role of the office of the CDO is simple: create value from data. To frame the context of the role, we’ll dive into its functions.
There are 100 ways to build a good data office, but there are only a handful of ways to build a great team. The office of the CDO needs to envision, prototype, evangelize, implement, and support existing and new data platforms. There are two broad paths that organizations can take here.
The resource makeup of the office of the CDO varies greatly based on employees and annual revenue, so that this approach can take a number of forms. However, some common themes are observed. The variability is that one company might need one of a particular resource and another might need 100. Use your judgment to scale the primary functions based on your business demand.
Next, we’ll cover the following primary roles and the skills required:
- Chief data officer
- Data scientist
- Data modeler
- Data architect
- Data analyst
- Front-end designer/developer
- Database administrator
- Portfolio manager
- Project manager
- Business relationship manager
Chief data officers provide leadership on maximizing the value of data assets enterprise-wide. This role is responsible for leading the transformational change to position the organization so it’s data-driven. Driving the use of the right data at the right time, creating a data-driven culture, and leading analytics are vital. However, the most important aspect of the role is establishing and fostering organizational buy-in for the office of the CDO function as well as the future role data will have in the organization. Few leaders will argue that data is transforming business decisions and that business models are changing; the challenge is that those same leaders might not believe that your office of the CDO is the right team to do that. This is why establishing collaborations and building trust outside of IT is essential.
Data scientists help to identify opportunities to improve organizational outcomes by utilizing data, developing predictive models, and sharing stories that present new insights. There are seven major areas of significance to data scientists: data collection (web scraping, HTML, CSS), data ingestion (SQL APIs, JSON, XML), data cleansing (multiple data types), data visualizations (D3, Tableau, Spotfire), basic analysis (R, Python), data mining (variance analysis, measuring bias, feature normalization, feature selection, feature extraction, clustering analysis, association analysis) and predictive modeling (data modeler+, graph analysis, bootstrap or bagging modeling, ensemble models, Bayesian analysis, neural networks, deep learning). An effective data scientist can apply sample and survey methods, determine statistical significance, conduct outlier analysis and make data-driven decisions to identify new data-science opportunities previously undiscovered.
Data modelers use a variety of data types to build and design predictive models. To understand sampling methods and measure statistical significance, data modelers need to have much of the experience of data scientists. For example, data visualization, basic analysis, data mining and predicting models are key skills for this role.
Data architects develop linkages between systems. They need to have experience with multi-architectures and implementing complex database policies and standards. This background allows them to develop complete solutions to validate, clean-up and map data. Ensuring end-to-end data quality requires integrating data from unrelated sources. Having internal knowledge of the organization’s domains is a crucial element.
Data analysts facilitate data collection and aid in data cleansing with primitive analysis skills. Often this role is the initial drafter of organizational policies, standards, and procedures before more experienced resources assume ownership. These resources likely are familiar with R, Excel, and SQL at a high level but hit limits quickly when applying this to SQL APIs, JSON or XML applications.
Database administrators specialize in software to store and organize data. Usually, this role includes capacity planning, installations, configuration, database design, data migration, performance monitoring of data, security, backup and recovery, and basic troubleshooting. This role is hands-on regarding data and, as a result, needs to be carefully managed with segregation of duties.
Portfolio managers focus on value realization from products, services, interactions, assets, and capabilities. This includes making investment decisions to balance objectives, asset allocation, and risk for optimal performance. This role aligns strategy with the bottom line to optimize delivery orchestration across the data portfolio of investments, projects, programs or activities.
Project managers lead data-related project initiatives and provide contract support to align with corporate policies. These resources work with multidisciplinary teams like legal, cloud, finance, operations and various business functions to lead projects and get them over the finish line.
Business relationship managers stimulate, surface, and shape business demand to define the full business value envisioned. This involves building credibility for the office of the CDO, establishing partnerships outside of IT to increase awareness of existing capabilities in house, and introducing new data capabilities that have force-multiplier effects for business partners.
Likely there are dozens of resources that could be pulled into a CDO team to align to organizational needs. The foremost that comes to mind are subject-matter data experts that have specific and deep domain knowledge of how your business operates.
Now that you know the critical roles to establish the office of the CDO, spend your time finding the best resources to staff your office. These resources are in high demand, so you must assume it will take longer than planned to recruit the team.
The primary responsibilities of the office of the CDO used to be focused around data governance, data quality, and compliance drivers. Today, the focus of this office is to enable a data-driven culture and maximum business value.
To exploit data to achieve a competitive advantage and establish the office as a strategic advisor, the responsibilities need to be communicated across the organization.
Leading change and championing a data-driven culture can be enabled with the following defined responsibilities:
- Envision, design, and communicate a collaborative, enterprise-wide data strategy.
- Establish a governance structure for managing data assets using a repeatable process and standardized frameworks.
- Define, implement, and manage organizational data principles, data policies, data standards, and data guidelines.
- Decrease the cost of collecting, managing, and sharing data while increasing the value.
- Enable data-as-a-service for enterprise-wide adoption using a data-service strategy.
- Develop data-quality measures and practices to improve organizational trust in data.
- Manage the data portfolio to coordinate the investment prioritization of enterprise-wide data initiatives.
- Identify opportunities for the organization to more fully leverage data for a strategic advantage.
- Champion organizational change management for a data-driven culture.
- Advance how enterprise-wide data assets are managed to provide deeper insights.
- Establish policies and programs for data stewardship and custodianship for stakeholder engagement.