No business is too small for big data.
In fact, any business that doesn’t take advantage of advanced data analytics risks losing pace with their competitors who – very likely – are utilizing these capabilities to drive operations, sales, and innovation.
This article demonstrates the importance of data architecture for SMBs that use data to guide their decision-making processes.
In its most general terms, data architecture is any framework of models, policies, rules and standards an organization uses to manage data and its flow through the organization. Its purpose is to capture, store, interpret and deliver usable data to people who need it. Put differently, data architecture is the framework that converts raw data into useable information.
As a framework, data architectures of all kinds follow general principles that govern the usage, management and integration of that data for the businesses. For example, a good data architecture is one that drives consistency and visibility across all tools and vocabularies, allowing people working on the same project to collaborate effectively. It should also flag and correct data errors, including data duplication, as quickly as possible. Finally, a data architecture should provide the user – both your customers and your workers – with the right interfaces (UI) to consume the data effectively. All of this coalesces as a seamless, integrated experience for all parties involved.
You may be tempted to think: “That’s all well and good for enterprise organizations with hundreds (or thousands) of employees collaborating on a single project. But why should I, a small business owner, care about data architecture?”
Supposing you are someone who still needs to be convinced of the importance (for all businesses – no matter the size) of utilizing the available data, consider the following study conducted by Oxford Economics and NTT Data (2021):
So, if you’re still questioning the relevance of data management for your business, you’re certainly in the minority of SMBs. In fact, SMBs are rising to the challenge of staying competitive with their larger counterparts by quickly moving to plan and implement their data architectures. Another survey, conducted in 2018 by Dresner Advisory Services and reported by Forbes, found that organizations with “100 employees or fewer had the highest adoption rate of business intelligence (BI) tools, including data models driven by advanced analytics.”
The first question to ask is, “What are my business goals?”
After all, with innumerable possibilities for collecting, processing and analyzing information, you’ll want to begin with a clear sense of what you want to achieve before establishing guidelines. Otherwise, you’ll almost certainly get lost in an ocean of unstructured data that equates to high storage costs and unnecessary complexity that will prevent you from zeroing in on the right data to help guide your business’s decision-making processes.
Once you’ve defined your business goals, the next step is to convert those goals into measurable KPIs (key performance indicators); otherwise, you’ll have no way of matching the available data sets to your business goals. Put differently, you need to have a thorough grasp of the metrics that will allow your business to identify points to be optimized and to provide a means of tracking that optimization over time. For example, let’s say you are trying to forecast future revenue – this would rely, in part, on a process called “predictive analytics.” In order to predict the amount of revenue that will be generated, say, in next quarter, you’ll need to start by collecting/analyzing data from previous time periods. These metrics need to be determined in advance, such that forecasting can be made possible.
To learn more about how data architecture can be used to help grow your business and to find a data-architecture solution that’s right for you, reach out to one of our experts today!