Data Architecture

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.

What is 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.

Why is Data Architecture Important?

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:

• GDPR fines
• Security threats
• Damaged reputation
• Lost business opportunities

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:

• Cut costs – by defining which data you should and shouldn’t be storing, you can reduce cloud and onsite storage costs.
• Better decision making – recognize, interpret, and put high-value data in front of decision makers, and your organization will benefit.
• Faster innovation – the right data needs to be available to everyone, because silos keep departments in the dark and prevent insight and innovation.

Now you know what you’re aiming for, let’s take a look at the core principles which contribute to an effective data architecture.

Data Architecture Principles

There are a few core principles to consider that will affect the design and success of your data architecture. These include:

• Business architecture and policies. Depending on your sector, there may be specific regulatory or professional standards to consider. For example, financial sector regulatory requirements, such as Basel II and Basel III, are stringent and play a large role in the formation of data architecture in the banking sector.
• Technology architecture. The hardware and software supporting your data architecture is also an important factor. For example, previously purchased software licensing will dictate both historic and ongoing inputs into your data pipeline and shape the overall data architecture.
• Economic reality. Some data architecture solutions provide only incremental improvements and might be valuable to organizations that are looking to refactor rather than overhaul. Others offer more compelling solutions at a lower price.
  
  For example, hiring a new data cleansing team is costly and time-consuming. But an automated data cleansing solution can tick the same boxes at a lower price and deliver faster, more profitable success.

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.

How and Where is Data Stored?

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.

Data Warehouses

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.

Data Lakes

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

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.

Fixing Flaws in Your Data Pipeline 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:

• All data sources are ‘plugged in’ to the pipeline and aren’t feeding repeated data sets
• Your pipeline is delivering insights and is sufficiently agile (so for example it doesn’t take a week to get a report)
• Scaling isn’t a problem – as the volume of data you're working with grows, it doesn't cause a bottleneck in your processes

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.

Modernizing Your Data Architecture and Eliminating Data Silos

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.

Reducing Bad Data: Why It’s Important and How To Do It

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.

If your organization is experiencing consistently bad data, it’s important to first look at organizational issues. Consider:

• Upskilling workers – human error is a big contributor to poor data quality. Train your staff effectively so they don’t introduce bad data into the system and can both recognize and correct it.
• Analyze business processes – poorly constructed processes lead to the collection and distribution of bad data. For example, if one source inputs dates as day/month/year and the other month/day/year, when brought together, there will be data consistency issues unless your data is standardized as part of your process. 

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:

• Establish which metrics you should be tracking.
• Have a clear framework and step-by-step processes for cleansing data
• Use the right tools to clean data. It’s hard to do things manually – and data preparation tools can help to clean, structure and enhance the value of your data.

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.

Data Architecture Is A Complex Beast – Tame It With Our Help

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. 

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.

Read More About Data Architecture

• Webinar: Choosing the Right Data Architecture for Your Business
• Blog: The Data Storage Evolution: From Punched Cards to Data Lakes and Beyond 
• Blog: Making Data Models Actionable with Data Integration
• Blog: How to Use Data Integration to Develop Data Architectures