Insights-Driven Infrastructure - Using Business Goals to Drive Data Architecture
As more companies are increasingly moving data architecture to cloud-based platforms, they have many decisions to make around what data is stored, how much of it is stored, and how it becomes accessible to the business. Those who are developing the supporting architecture must balance cost and data security concerns while ensuring that the data needed to propel the business is available to the people and systems that rely on it most. For large scale data architectures, it is important to design in a way that not only supports present-day business operations but the future goals of the company as well in order to minimize re-work. That starts with a clear understanding of the goals that the business is looking to achieve in the foreseeable future and an understanding of how the data infrastructure can support those goals. Here are some key considerations in designing solutions with business goals in mind.
Understanding how the business needs can be accomplished often have a direct impact on underlying data architecture and processes
How will data impact our ability to meet or exceed our business goals?
High level business goals such as:“increase revenue by 20 percent”, “reduce costs by 10 percent”, or “improve customer satisfaction by 8 percent”, may not seem like they have dependence on data but a little digging into how these goals will be achieved will likely tie back to a company’s capabilities around data. For example, a company may find that they can improve customer satisfaction with more real-time updates related to customer orders. Those real-time updates may then be linked to updating data stores more frequently and further automating data pipelines to get that data into the customer’s hands. Understanding how those business needs can be accomplished often have a direct impact on underlying data architecture and processes.
How does our architecture align with the long term vision?
When it comes to data architecture, it is important to look beyond just the goals for the coming year. For example, if we enabled real-time data updates for customers but reduce historical data storage to save costs, have we just hampered our ability to build future forecasting capabilities off of historical data? As we make trade-offs around how data is stored and accessed, it is important to understand how those trade-offs may handicap us in the future. In this example, even though forecasting may not be on this year’s roadmap, we need to anticipate longer term repercussions in direct discussions with business partners.
How do we balance system flexibility and cost?
It is impossible to anticipate every business case for how company data may be used. Given that, it is important to maintain flexibility whenever possible. This means not only trying to store sufficient historical data but also storing key data in a manner that makes combining across various data stores as easy as possible. The value of data can often be enhanced by combining it with the right assets that may currently sit in a different part of the organization.
Data assets often serve as the basis for how companies can build innovative new products, generate insights, and drive business results. The data in itself however will not serve those purposes without proper data architecture. Architecture decisions around what data is stored, how it’s stored, how much is stored and the level of accessibility will dictate the level to which a business can grow and achieve its goals. The purposeful alignment of these decisions to goals is what will enable and drive the business forward.