Building effective teams to increase data consumption at scale

As they look to increase data consumption Chief Data Officers (CDOs) and other data leaders rely on the people in their teams and the structures they put in place to enable them to share the right data, in the right formats, with the right business users. At the same time they need to ensure consistency and strong data governance, providing a single version of the truth to the business.

However, often data and analytics (D&A) teams have grown up centrally, with an IT background that means they are staffed by technical experts focused on handling and understanding data. While this ensures consistency it means that business needs and strengths can be overlooked, preventing agility and meaning that the impact of data is not maximized. How can companies balance central control with ensuring that business needs are met?

In a new report How D&A Leaders Should Organize Their Data and Analytics Teams

Gartner argues that CDOs need to take a hybrid approach in terms of both structures and skills in order to ensure governance, enable innovation and connect business users directly with the data they need. This blog explores the options set out by Gartner and explains the importance of data product marketplaces to successful data and analytics organizations.

There have traditionally been two main approaches to managing data and making it available to the business:

• A global, centralized hub team that leads and controls data sharing, ensuring consistency
• Decentralized regional/domain (department or office)-specific spoke teams that bring context and business expertise to data

Based on its experience, Gartner recommends two further approaches that organizations should consider:

• Local, decentralized analytics teams, based in specific locations (such as a factory or store) who use data and analytics to meet the operational needs of local users
• Agile Next teams, labs focused on innovation through data, such as AI and new data services

All of these approaches bring specific advantages – simply applying one of the four will not maximize data consumption. Instead, what is needed is a hybrid approach that combines elements of all of them, blending them to meet specific needs. The exact balance will very much depend on the organization’s requirements. For example, companies at the start of their data sharing journey are likely to adopt a defensive approach that focuses on putting in place the right data platforms, and therefore lean towards central hubs. More advanced organizations looking to innovate will have a stronger Next team/lab component in their structures.

Essentially, CDOs need to put in place a mix of centralized hubs and local spokes to deliver success. Achieving this starts by understanding where the business is now, what its immediate needs are, and how this is likely to change moving forward. 

Creating this conceptual model provides a strategy for what activities should be carried out centrally, and which are best delivered by regional or local teams. From this CDOs can build a physical structure that ensures the right expertise, resources and technical or business knowledge is in the right location. All of this has to be mindful of the needs of stakeholders in order to deliver data effectively to maximize its consumption and value. 

One method that is increasingly being deployed to deliver the right mix of consistency and freedom is a franchise model. Just as in a fast-food restaurant chain, spokes operate independently to meet local needs, but within frameworks and guidelines set out and enforced by a central team. This ensures consistency and governance, avoids reinvention and overlap, and enables flexibility and agility.

While the structure of every business is different, based on talking to CDOs across the globe Gartner has synthesized six best practices that help guide how data teams are organized:

1. Data governance policies and processes and master data management (MDM) impact the entire organization, and should therefore be centralized to ensure consistency and regulatory compliance. Data stewardship, which relates to specific data assets, should be decentralized, meaning those closest to this data are responsible for managing it.
   
2. Building a data culture through data literacy and change management programs should be developed centrally, but with the input and participation of the entire business to ensure engagement and adoption.
3. Business intelligence has traditionally been centralized, making data teams bottlenecks between data insights and the business. Instead, self-service analytics should be rolled out to decentralize the process and connect users directly to the data and tools they need in their working lives.
4. Data scientists should be deployed as part of both central and regional/local teams. This helps them support local needs and build a company-wide data culture. At the same time they should collaborate closely to share best practices, learn new skills and drive forward wider data consumption.
5. Technical infrastructure has normally been handled at a central level, often in conjunction with IT teams. This provides robustness and control. However, as companies increasingly roll out data products, responsibilities for these should be decentralized to those closest to the data itself and the business requirements it helps meet.
6. Data architecture and overall strategy is also best handled through a central approach, headed by the CDO.

Putting in place an intuitive, self-service data product marketplace connects business users to the data they need. Importantly, it also supports CDOs in optimizing their data team structures in six key ways:

Providing a central collaborative space

Data product marketplaces bring together local data product owners and central data governance and administration teams in a single place. They can collaborate around creating, sharing and promoting data products, including monitoring their uptake and use cases.

Enforcing governance and best practices

All data assets published on the data product marketplace are checked to ensure they meet governance, data quality, and security standards. By applying processors, data assets are standardized around corporate guidelines, both around technical formats (such as dates) and reference data, as well as business terms through business glossaries. As a data product marketplace provides a single version of the truth, the organization benefits from consistency and auditable data processes.

Enabling seamless data sharing by all

Data product owners from across the business can easily create and share data assets through the data marketplace in a straightforward way, making them available to users. Access can be controlled to sensitive information if required to ensure security and confidentiality while maximizing reuse at a local, domain and/or global level.

Delivering data for AI innovation

The Next labs identified by Gartner are essential to innovating with data and underpinning AI. Data product marketplaces make data assets, especially data products, available in machine-readable formats, enabling them to be seamlessly used by AI models and agents. Built-in data contracts set out how data can and can’t be used, minimizing risk and maximizing innovation.

Showcasing data reuses

Data from one part of the business can be used in completely new ways in other departments or teams. Data product marketplaces underpin and encourage this sharing, both by making data available in understandable, usable formats and also allowing users to share their reuses with the wider organization. This spurs new ideas and uses, drives collaboration, and enables innovation.

Building a company-wide data culture

Collaboration and communication are essential to building and spreading a corporate data culture. By making data easily accessible to all users and data teams, data product marketplaces build trust in data and encourage everyone to experiment, share ideas and provide feedback. This helps create the skills and culture required to scale data consumption within the organization.

As organizations look to increase data sharing and use, they need to have the right structures in place, balancing centralization and governance with local, business requirements. An effective data product marketplace supports these structures, helping organizations optimize data sharing, embrace innovation and drive data democratization.

Want to learn more about structuring your data teams? We’ve worked on over 3,000 data marketplace projects across the globe – talk to our experts to find out how we can help you deliver on your objectives.