How should organizations structure their analytics?
Your organization consists of one core team of analysts and data scientists. The work they conduct is the driving force for how analytics will be run throughout the rest of the organization. Each position is unique in the responsibilities they carry out for reaching the overall goals of the team. Together, they tackle a variety of projects across all departments as needed.
Each department/business unit delivers its own projects and functions, with opportunities for analytics expertise within each. Data analysis is not limited to the responsibility of a single data team. Instead, it is done by those in other positions who have a desire to build their analytical skill-set or by data-focused individuals assigned to that specific department.
So how do you choose what’s most effective?
When it comes to knowing whether centralization or decentralization analytics is a more suitable fit for your organization, consider where the two structures sit across these few valuable components.
Centralized Analytics: With a centralized analytical model, how methods and processes are executed can have stronger oversight from the core data analytics team outsourcing their efforts to the entire organization. They can optimize on each skillset their team holds and utilize them in a way that is most cost-effective and efficient. There does come the risk, however, that certain analytical operations are not fit for every department and what may have been intended to work for all becomes a sunk cost when one team must implement something more applicable.
Decentralized Analytics: When analytics expertise and resources are spread across all departments of an organization, there is a higher chance for redundancy in efforts. Although operations are more tailored to the needs of each department and KPIs can be measured and budgeted accordingly, there is less transparency on what other departments are doing where resources could be more streamlined.
Centralized Analytics: Due to challenges of delay when information has to be passed throughout management of a core analytics team, decisions can be slowed down with a centralized structure. On the other hand, there is a benefit of not having resources siloed that allows greater collaboration in decision-making given the perspectives across all departments.
Decentralized Analytics: With each department deploying its own analytics, decisions can be made that are more responsive and relative to the changing conditions of that business unit. Leaders that carry the analytical skills have more sovereignty in decisions when they are working closer to the consumer and hand-in-hand with other team members to see which priorities need to be met.
Centralized Analytics: Progress is crucial for every organization. Centralized analytics opens the door for increased communication and knowledge sharing among all departments, as well as growth within the data team. Instead of one individual carrying out multiple responsibilities for their relative department, members of the centralized team can develop their own career path by mastering the value they bring towards strengthening the analytics of the organization.
Decentralized Analytics: A decentralized approach also affects employee growth. If there is one individual responsible to deliver on analytics for their department, they may feel more empowered to work harder to do so and prove they are a vital resource. Decentralizing analytics also creates more career options for organizations to capitalize on. Someone that holds a multitude of business and analytical skillsets can be seen as more versatile and adaptable in their position. One limitation, however, is that these talent resources cannot be deployed as easily when individuals are designated to their own unit.
Centralized Analytics: In terms of attaining goals and reaching performance targets, a centralized structure allows for the data scientists to work towards developing best practices that can be used by every department. They can ensure there are consistent metrics that every facet of the organization can use so there is comparable measures of success that reflect the state of the business as a whole. Unfortunately, that means that there may not always be enough time and resources to hone in on the goals of each department.
Decentralized Analytics: With each department having analytic functions working towards their main objectives, there is less of a disconnect between data science and the business department. Each one guides and extracts value from the other to build upon previous capabilities and make visions more achievable. Where there is experimentation and learning, there is development and transformation, but not necessarily for the organization as a whole if there are not common metrics across the board.
Balance is Key
You may already begin to determine whether a centralized or decentralized system is more suitable for the current state and size of your organization. If you’re a smaller business, a centralized structure may seem to align better given the above factors.
Take into consideration, however, the possibility of combining both. This can take many forms, but ultimately has the same end in sight – to capture the benefits that each structure provides. For instance, it may be more strategic to have a centralized team for areas such as data engineering and deploying solutions that data scientists, analysts, and others with that expertise can best manage. Organizations can then decentralize the remaining facets, such as data visualization, analysis, and real-time reporting that affects the work each department must deliver on.
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