A modern data strategy is critical to the successful execution of an analytics program.
David Dadoun, Director of Data at Bombardier Recreational Products (BRP), described the dos and don’ts of building a modern data strategy in a September 21 session at Real Business Intelligence, a virtual conference held by Dresner Advisory Services,
Dadoun joined BRP, a Canadian manufacturer of snowmobiles, personal watercraft and other recreational vehicles, just six months ago.
When he was hired, he was responsible for overhauling the company’s analytics operations. Before taking steps to modernize BRP’s data strategy, however, he held meetings with nearly 80 different data employees in local and international offices to learn as much as possible about BRP’s existing analytics operations. and the changes it might make.
He then adapted BRP’s data strategy based on what he discovered. Any organization must tailor its data strategy to its individual needs, he stressed.
But there are general best practices, starting with a cloud-based data platform. On top of that, however, it implements what it’s called a domain-federated data policy.
Domain federation is a strategy that centralizes the architecture, governance, and security of an organization’s data operations, but creates flexibility beyond this centralization by establishing domain teams. These teams then develop data assets that allow knowledge workers across multiple departments to access and use the data to drive the decision-making process.
“In domain federation we are looking to remove bottlenecks, increase our ability to deliver value to all data products, these products are interoperable and the fact that these products are interoperable creates a network effect. which increases the value of data, ”Dadoun said.
Typically, organizations have developed centralized, decentralized, or federated data strategies, according to Dadoun.
David DadounChief Data Officer, Bombardier Recreational Products
A centralized data strategy puts a single data team in charge of all data-related matters, which means that whether an analytics project is for the human resources department or the finance department, among others, l The data team undertakes the project and delivers a finished project. produced when it’s done.
The advantages of a centralized strategy include ensuring that best practices will be applied and a common data architecture for all projects. The downsides, however, include the loss of commercial proximity to the project, bureaucracy, and an inflexible structure.
A decentralized data strategy, on the other hand, gives each department its own data team, with those teams reporting to department heads rather than a data or analytics manager. The advantages are proximity to the business – data teams and data consumers work closely together – and flexibility, but the disadvantages include the lack of common standards and the development of data silos.
Finally, a federated data strategy is a hybrid of centralized and decentralized, with departments retaining their own data teams but working under the direction of a data oversight team with a senior-level presence.
A federated data strategy makes the most of centralized and decentralized data strategies, including standardized governance metrics and other best practices and proximity to the consumer side of the business.
“Typically what we see over time is that organizations move from a highly decentralized approach when they start with their data initiatives to trying out the centralized model and then moving to a federated approach,” said Dadoun. “But there’s still something missing, and that’s the fact that companies still think monolithically.”
This monolithic thinking refers to a single data warehouse or data lake, a single reporting platform, and single teams creating all the reports and feeding the data platform, he continued.
“It’s not the best approach,” Dadoun said. “It can be better if we start to take a domain-driven approach. “
Data domains are groupings of specific data elements that can meet the needs of multiple departments or business teams, and the domain federation builds a data strategy around domains rather than the needs of individual departments, according to Dadoun.
The fundamental building blocks of domain federation are the data domains themselves, a cloud-based data platform, a data catalog that makes data easily discoverable, and data interoperability which includes strong data governance and common standards and architecture.
Like a federated model, establishing these foundational blocks is the responsibility of organizational data managers.
But unlike a federated model, domain teams working under the direction of organizational leaders are rather than data teams dedicated to the needs of individual departments. They take care of ingesting the data, transforming it and creating the reports and dashboards that guide the decision-making process.
Knowledge workers can then access products created by domain teams and use them to perform ad hoc queries, build their own models, and perform their own analysis.
the the result is a network effect, and the ability for end users to create their own data ecosystems using cleansed and governed data.
“Ultimately, we want to permeate knowledge and use of data, and [knowledge workers] will meet these domains and access data which is naturally interoperable between different domain teams, ”said Dadoun.
None of this, he added, can be accomplished without a modern data platform.
“You can’t accomplish this with your grandfather’s data warehouse,” Dadoun said. “This new platform needs to be a platform for data and not just a warehouse versus a lake. We need domain-driven architecture. We do it because we want to stimulate innovation. It’s different from the way IT has handled data in the past. “