In a world dominated by data, a well-executed data architecture is key for ensuring your business remains healthy and growing. Without it, you’ll fail to unlock the value of your data, run the risk of wasting money, and lose out to your competitors who have more mature data strategies.
So, creating a beneficial data architecture is important. But it can be a daunting topic. Without the right experience and know-how, it’s difficult to set up a data architecture that aligns with your business strategy.
To help make your data architecture a resounding success, we’ve compiled our knowledge and best practices to provide you with the best information on data architecture. Reading this is the first step we recommend to every business looking to build or improve their data architecture.
Data architecture is a framework of rules, policies, models and standards which dictate how your organization uses, stores, manages and integrates its data.
It dictates how your organization handles all data, whilst aligning with business, application and technology architectures to achieve company-wide objectives.
Data architecture is important because, without defined processes and strategies, your business will at best fail to unlock the true value of data. At worst, you’ll leave yourself open to the many dangers that strike organizations that mishandle their data. These include:
It’s also important to consider that, globally, we’re producing more data than ever. According to IBM, every day the amount of data worldwide increases by 2.5 quintillion bytes. And as the volume, velocity and variety of data grows, the need for more effective data architecture will also increase. This means that your organization must be able to handle more data or risk falling behind.
But, putting aside the security fears and the onrushing data tidal wave, there’s plenty to get excited about as well. Below are just some of the ways that effective data architecture can help your organization:
Now you know what you’re aiming for, let’s take a look at the core principles which contribute to an effective data architecture.
There are a few core principles to consider that will affect the design and success of your data architecture. These include:
Only when you understand your organization's position and needs can you effectively create a data architecture solution. By following these core data architecture principles, you can then build a data architecture that unlocks the true value of your data.Blog: 4 Data Architecture Principles That Will Accelerate Your Data Strategy
An important pillar of your data architecture is establishing what data you want to store and where you want to store it. There are various options to consider and each has different pros and cons.
Let’s cover the big three: data warehouses, data lakes, and data vaults.
Learn more: For more on different types of data storage, including data warehouses, lakes and vaults, take a look at our post Data Warehouses, Lakes, Hubs and Vaults Explained
This is where you analyze data from a wide range of operational sources. It’s considered a core component of business intelligence because it’s here that data from disparate sources comes together for analysis and reporting, and to unlock value for the business.
Data warehouses optimize for simplicity, ease of use, and speed of access for the end-user. KPIs, for example, must be easily accessible to non-developers who want visibility and insight at a glance.
A data lake is a collection of data stored in its ‘natural’ form. It’s a catch-all area for any enterprise data and is typically built from a variety of different sources, such as, analytics, reporting, or machine learning. It’s optimized for quantity and is a home for data to sit untouched before its cleaned, interpreted, and transformed.
Data vaults are for long-term data storage and the creation of a single source of truth.
Accuracy and overall data quality are therefore essential qualities for data vaults. Data vaults are commonly used for audits, as finalized sets of sensitive data are safely stored here.
These factors make data vaults less agile, but perfect for long-term projects as they help provide alignment for both specific projects and the overall organization.White Paper: Your Guide to Enterprise Data Architecture
Your data pipeline architecture is responsible for bringing together data from different sources and making it strategically valuable for your business.
To ensure your data pipeline architecture is providing the value you require, you need to address the following:
As your business grows and your requirements change, fixing and modernizing your data pipeline is an essential part of staying ahead of the curve and driving profitable innovation.
One of the biggest barriers to an effective data architecture is the unintended creation of data silos. If you don’t create a modern data architecture that feeds data effectively through your organization, your data risks becoming hidden and unused.
An example of this is a marketing department that fails to pass on the right data to sales, and in turn the business misses out on new opportunities.
A good data architecture must manage and reduce bad data as much as possible. Poor data quality can act as a contagion and decreases data value as it spreads through the organization.Blog: Managing Bad Data: 5 Things You Need to Know
If your organization is experiencing consistently bad data, it’s important to first look at organizational issues. Consider:
Making these changes contributes to reduced data error and a more effective, high-value data architecture. But what can you do, specifically, to clean data? Below are some tips:
Typically, it’s easier to detect issues with bad data than it is to correct them. And it’s also easier to clean data at the point of entry rather than to clean it down-the-road when it’s spread through your data pipeline. Follow the tips above and you’ll ensure your data architecture helps contribute to high-quality data.
There's no easy way to create an effective data architecture for your organization. And when you're so close to it, it can be hard to be objective – is your data pipeline as streamlined as it should be? Are you using data to track the right KPIs? Are you building your data architecture in the most effective way?
And how do you choose the best data architecture for your business? It's a process that needs you to ask yourself some key questions, including how much you're realistically looking to spend; how often you might want to change the questions you're asking about the business; and what the skill level of your workforce is.Infographic: 7 Questions to Ask When Choosing Your Data Architecture
These important questions can be hard to answer when you don’t have the experience in-house, which is why working with experts can be helpful. It takes the weight off your shoulders and those of your over-burdened IT team, and it helps you get it right the first time without wasting time and money.
For more on how CloverDX can help you to tame your data and build a data architecture that delivers results, download the comprehensive Guide to Enterprise Data Architecture.