When it comes to analytics solutions, centralization versus decentralization is one constant tension that’s plagued data architects for years now. Both options offer their own sets of advantages and disadvantages, as well. Centralized data design means building a data tool set controlled by a single IT department that serves external business units. This provides organizations with control, uniformity, simplification, and security. Decentralized data allows business units to be the owners of their data needs. This gives companies more flexibility, speed, and unique system designs to meet users’ needs.
It’s no wonder why discussions involving cross-departmental data often involve a forceful and adamant pull between these two valid approaches to one of the most valuable assets a business can hold. When all is said and done, the issue is almost always rooted in trust.
Data teams want to ensure that the use and management of the data align with each department’s core goals. When shared, that guarantee goes out the window. Not all departments have the same policies or workflows to ensure a secure, standardized, and efficient data set. If a team were to apply aggregations to model its business goals, this downstream transformation effort could introduce meaningful logic errors. These errors might result in business variances that will erode the trust in the data altogether.
For a company, it’s often more cost-effective and secure to centralize data reconciliation and unification components to a centralized team and then share an aggregated solution — as opposed to building analytics for each department. This is where data virtualization has emerged as a solution to support multiple workflows without duplicating underlying source data. As with any technology solution, however, there are trade-offs. But there are technological solutions that can help blend the pros and cons of the centralized-decentralized dichotomy.
An open data-sharing protocol has many benefits. It allows business units to build custom-to-need analytics that can inform decisions. Easier access to data also helps departments develop strategies, fine-tune processes, improve products and services, and so on. Besides, sharing data helps foster collaboration and communication between departments, allowing them to work more effectively together. Open data protocols simply help teams better understand how to use data and arrive at insights in a collaborative manner.
A shared data model isn’t without pitfalls, but most, if not all, can be avoided. While many are based on data use cases, some general considerations exist. For one, shared data models require strong governance. Who is responsible for data? What types of data transformations are taking place? This allows each data user to have a system in place to understand how they can consume the data and how to communicate with other stakeholders. Data teams must work with other departments to develop clear data-sharing guidelines and protocols. This can help establish expectations and ensure everyone is on the same page.
Communication across departments is also essential. It can help foster trust and efficiency to align goals or complement initiatives. Again, data teams must work with other departments to build trust and enable communication. This might involve sharing data in small increments, providing training on data analysis, or involving other departments in data-related decisions.
Beyond that, it’s crucial to assess the risks and benefits of shared data models. Once these concerns are identified and documented, you can understand the potential impacts of data sharing on the organization.
Being “data-first” means ensuring data is considered and developed with every product or business workflow. Organizations gain an increased understanding of their user bases, enabling them to target their marketing and optimize their operations more effectively. Organizations with data-first cultures also make more informed decisions and gain a better understanding of their markets. They’re in much better positions to price competitively, build more robust automation, serve their customers, and, ultimately, outperform competitors.
Building such a culture often starts with the following:
Data literacy will serve as the starting point for any organization to build a data-first culture. Even the best digital tools won’t work if team members don’t understand how to access, adjust, or utilize output insights. Setting up a data literacy framework can certainly help, as it provides a more structured system for educating and training employees on the value of data. It also helps establish parameters for making informed, data-driven decisions. For any data literacy framework to be truly comprehensive, it should involve activities that expose participants to the purpose of data, its management, its use, and how it relates to achieving an objective.
Improving data accessibility takes more than enabling decentralized data sharing. Not every business unit requires access to all data at all times. Instead, think about how data is structured and shared. Accessibility to accurate and properly integrated data will better ensure that users can focus on analysis, insights, and automation rather than engineering, integration, and design.
Once good systems have been designed and teams understand how to consume data, it’s essential to establish a process for departments to share their data insights and successes with other teams. This fosters a feedback loop that encourages data-driven practices and supports even more analytical decision-making.
When an organization doesn’t value data or understand its application, it misses opportunities to improve business results. Once the above strategies are enacted, it’s only a matter of time before employees’ mindsets change. They’ll begin to embrace that data-first approach and further enable more data-driven decisions to drive business beyond what was ever thought possible.
By Josh Miramant
