Any savvy 21st-century business knows the importance of data-driven systems and analytics.
Data analysis drives business intelligence and strategy, but that same data also lays the building blocks required for your systems that focus on security, transaction management, and more.
But all too often, decision makers become overwhelmed by their own data. They find it’s unwieldy, scattered, and expensive to maintain.
If this sounds familiar, you’re not alone.
In an age when 7.5 septillion gigabytes of data are generated globally every day, it’s not entirely surprising that a huge percentage of the data that companies collect goes unused. Estimates range from 55% to 90%, but regardless, the untapped potential is clear [1,2].
The more types and volume of data you have, the higher the potential for powerful results. But it’s also harder to keep organized and efficient.
So, how do you begin to sift through the noise so you can put your data to work to unlock both new products and big-picture insights?
The first step is taking a critical look at your data architecture.
What is data architecture?
Simply put, data architecture provides your foundation to perform analysis, deliver an interactive customer experience, and make data-driven decisions both now and in the future.
Data architecture is a unique blueprint that defines your processes and workflows for data collection, storage, and extraction. It clearly outlines the types of information you collect and use, how it’s maintained, and how you’ll process it efficiently and accurately.
Most importantly, data architecture is business-driven.
Good data architecture begins by carefully considering your unique business goals and configuring tech in service of the business — not the other way around.
To take it a step further, high-quality data infrastructure should be a business priority in and of itself. Data informs decisions and powers daily operations. Designed poorly, it leaves your business vulnerable to governance issues and security breaches.
Well-designed data architecture:
- Unites your data into a single source of truth. Data is vital for operating a modern business. But when it’s scattered through a disorganized infrastructure, you lose the ability to govern it.
Because you use your data in multiple ways, unifying it can be one of the greatest challenges you face.
- Is based on business goals. What kind of data do you have? What questions do you want to answer? What are some ideal use-cases for your as-yet-untapped data?
Your data architecture should enable you to make the most of what you have and deliver the kind of insights you need.
- Has consistent, well-defined workflows and standards. Incomplete and inconsistent data is often unusable — 66% of businesses with unused data cite this as an analytical showstopper . To reap high-quality insights, you need to clearly outline every step of how data should be collected and stored, and document its intended uses.
- Is efficient and eliminates redundancy. Over time, businesses tend to accumulate multiple data warehouses and pipelines. What’s more, they’ll often have several tech systems that do the exact same thing . Good data architecture minimizes the components you need — saving you money and headaches.
- Is flexible and adaptable. Your data comes from multiple sources, and does multiple jobs. Some of these are instant services. Others are long-term analyses that inform your goals.
You want an architecture that allows you to check all these boxes. Its components should communicate well and easily allow refinement as the available technology evolves.
Why does data architecture matter?
Data architecture matters just as much as data-driven business practices — that’s to say, a lot.
Data-driven businesses consistently out-perform rivals. For instance, one study found that profit, sales, growth, and ROI of retail companies that used customer analytics were more than twice as high as those that did not .
What did these customer analytics “champions” have in common?
They took “a truly integrative approach, seeing analytics as a strategic rather than purely IT issue.”
This, as you’ll recall, is the central requirement of good data architecture.
Successful data-driven companies talk about their data in terms of overarching business strategy instead of siloing it off to their IT department.
Then, they leverage it to power daily operations and drive decisions that matter in their particular use-case.
This requires data that’s complete, current, orderly, and accessible for multiple purposes.
For example, imagine an insurance company. With the data they’ve collected, the company can identify customers on the verge of cancellation. But to make an impact, they need to do this frequently and quickly — otherwise, by the time they reach out, the at-risk customers will already be gone.
More generally, your ever-evolving business landscape demands quick, well-informed decisions. And if it takes a long time or a small army of developers to probe your data, you’ll end up resorting to guesswork and luck.
That hypothetical insurance company should optimize their data architecture so it’s easy to scan for at-risk customers regularly. They should also take into account moment-to-moment monitoring tasks, such as identifying attacks on their system.
The right data architecture lets you seamlessly manage your business and tech strategies. It lets you scale up business operations at high speeds. And in a quick-paced world, it allows you to pivot and stay on the cutting edge.
How to design data architecture that’s right for you
There’s a huge variety of tech available to businesses that want to make better use of their data.
It’s tempting to run toward every well-known solution your competitors are using without considering your larger goals and plan. This is how businesses too often end up trapped with complicated, redundant systems that don’t communicate well with each other.
Remember: you get to design your own data architecture, so make sure it works for your business
To start off, ask a series of questions:
- What goals does your business hope to meet — both long term and day-to-day?
- What technology options are available, and what is the best fit to meet those goals?
- What type of operational or maintenance burden will you be taking on if you choose to add those new technologies?
- How do the components need to connect?
In light of these considerations, you’ll design your data architecture in three layers, each building on the previous:
- Conceptual model. This is a high-level, business-oriented model that includes the essential entities and how they connect.
- Logical model. This more technical model defines how the data is organized and tagged from a logical, database management standpoint.
- Physical model This model lays out the nuts and bolts of the actual tech infrastructure you need to support your data architecture.
This is a complex task that requires expertise. In fact, “data architect” is an entire career path unto itself. So, adding a specialist (or a team of them) to your staff is one option, but it’s not always necessary or feasible.
Estuary is building the technical tools to make data architecture more approachable for your developer or data science team — regardless of their usual specialty. We help you consolidate your data pipelines into a single, agile foundation that can help you answer big questions, process events in real-time, and ultimately, meet your business goals.