In the digital age, businesses rely on high-quality, easily accessible data to guide all manner of decisions and encourage growth. However, as a business grows, the way the organization interacts with its data can change, making processes less efficient and impairing progress toward business goals.
Businesses need to think critically about their data architecture to stay competitive. If you are concerned about the ability of your current data architecture to keep up with your business’s needs, here are a few steps for building a more scalable data architecture for the future.
1. Engage Early with All Stakeholders
Data architectures exist to serve those who use them, so before you take any steps to build or reconfigure your data architecture, you need to understand exactly how your data is being used. As early in the process as possible, you should begin engaging with stakeholders like IT teams, business and data analysts, executives, administrators, and any other group within your organization that regularly interacts with data. Get to know their data practices and goals, which will provide insight into the requirements for your new data architecture, ensuring you have a deep well of information to draw from. By considering the needs of all stakeholders, you can create a data architecture that supports your entire organization.
2. Understand the Current and Future Data Landscape
Various details about your data will determine your business’s best data models, storage solutions, and architecture strategies. When engaging with stakeholders and within your own research, you should find out where the organization’s data comes from, what types of data it collects, and how data is formatted and used.
For example, if your organization has leveraged the applications of IoT, it will have to navigate real-time data streams — which can lead to valuable insights when properly managed. With the details of your data landscape in hand, you can create data maps that demonstrate how data flows around your organization so you can identify inefficiencies to target with your new, scalable architecture.
After communicating with stakeholders and researching your organization’s current data landscape, you can determine exactly what your data architecture will need now and into the future. Some requirements you will need to precisely define the volume of data your architecture will handle, how fast data needs to move through your organization, and how secure the data needs to be. All this data about your data will guide you toward better decisions in designing and building your data architecture.
3. Choose Optimal Data Models and Databases
Now begins the arduous task of creating a data architecture that can scale with your organization. The exact construction of your data architecture will depend largely upon the needs you outlined during the previous step, but some solutions are more advantageous for businesses looking to expand.
For example, NoSQL databases generally offer more flexibility than traditional relational databases by organizing data in a variety of different formats so that more data can be stored at faster rates — though at the expense of consistency. Generally, NoSQL databases are the go-to choice for businesses focused on scalability. However, increasing numbers of modern data architectures are adopting NewSQL databases, which strive to bridge the performance of NoSQL and the integrity of relational databases.
4. Balance Horizontal and Vertical Scaling
While there is plenty of healthy debate regarding the merits of horizontal scaling versus vertical scaling, the truth is that the best database architectures use both. Horizontal scaling, or using multiple servers to distribute data and processes, allows an organization to have many nodes within a system so the system can dedicate resources to specific data tasks. Horizontal scaling strategies allow businesses to continue adding nodes as their business grows, allowing them to meet data needs without completely rebuilding their infrastructure every few years.
Meanwhile, vertical scaling involves increasing the capacity of an existing resource, allowing it to draw more power to tackle more intense challenges. This often entails upgrading the physical machines within your data architecture, which can make it more cost-effective because it allows you to continue using equipment you already own. Unfortunately, the potential for vertical scaling is limited, so you will eventually need to scale out as well as up.
5. Secure Data Architecture Against Threats
Data is extremely valuable, which makes it an attractive target for cybercriminals. Therefore, you need to make sure that every element of your data architecture is properly protected. It’s hard to underestimate the importance of cybersecurity awareness. You should be aware of every attack surface or all the locations in and around your business network where a cybercriminal might be able to access your data. Then, you can reduce the potential attack surface and keep your data safe. You should also make sure your stakeholders understand the importance of cybersecurity tools and behaviors.
It isn’t an understatement to say that data is everything to businesses in the digital age. Before you launch a grand strategy for business expansion, you need to be sure that your data architecture will support your growth so you can achieve the success your business deserves.
