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Data Architecture: Importance of Developing Streamlined Data Architecture

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Everyone uses data these days, and for Internet of Things (IoT) developers, data is vital to move forward. It helps you build the systems you need to make smart devices and real-time tracking systems work. However, without the right data architecture and the right processes, you can easily spend all of your time just organizing data. Especially in the quantities an IoT network can generate. Data can come from multiple sources and end up in more than one database. So pulling it together takes time and effort.

The key to solving this problem is proper, streamlined data architecture. But how do we get there?

What are the Benefits of Data Architecture?

In short, data architecture is your overarching strategy for the collection, storage, and use of your important data. Streamlining your data architecture comes with a significant cost in terms of up-front planning. But it brings with it a number of benefits.

  1. It reduces the amount of time spent manipulating, processing, integrating, and organizing data. Instead, you can devote your time to properly analyzing your data and using it to inform your business.
  2. It reduces the number of mistakes that are made while organizing data. Generally increasing the quality and accuracy of your data.
  3. It supports risk management by reducing compliance issues. Ensuring that data is stored and, when necessary, erased in accordance with regulatory policies. This is particularly important if you do business in jurisdictions with strict privacy laws, such as California or the EU.
  4. It allows you to analyze and manage accurate data. So it increases the value of your data and improves your ability to predict customer behavior and analyze other data that help you develop business processes.
  5. It reduces the tendency for data to be siloed within departments and allows for cross-department analysis.
  6. It supports the use of more advanced technologies, including the use of artificial intelligence and machine learning systems. These systems are very much “garbage in, garbage out,” and their effective use requires accurate, easily accessible data.

Data architecture achieves these things by ensuring that you can apply a strategy to all of your data management. Using integrated systems that work together and ensure that data is easy to access, easy to analyze, secure, and accurate. A proper data architecture plan includes addressing concerns with cybersecurity as well as working towards full data governance.

How Do You Develop Streamlined Data Architecture?

So, how can you achieve this? There are six basic steps to developing data architecture which is solid and supports your specific needs:

Business People Meeting Conference Discussion Corporate Concept
Business People Meeting Conference Discussion Corporate Concept

Audit your existing tools and systems.

The first step is to look at everything your organization already uses, including systems used by specific departments. You should talk to all of your stakeholders and find out what works well, what works poorly. Identify pain points and places where data is not getting to the people who need it. When designing an integrated system, it is important to make it something everyone in your company is happy to use.

Plan the data structure.

data warehouse is often the best way to store large quantities of data. Designed to consolidate and compare your data. If you are already using one, then make sure to document what you are capturing and look into what sources might not be included and why. Should you add them? Or are you better off just using a reporting tool? There may be a very good reason why some data might not be included, such as security or compliance concerns. If you are not, then make note of any manual joins you are doing; these are time-consuming and need to be minimized or removed.

Define your business goals.

You need a data architecture that works for you, taking into account your needs, how you analyze data, etc. Make sure that each department or unit has proper KPIs and if necessary are audited to see how they use data. Make sure that you know what metrics you need and work out how to integrate them so that data analysis is easy.

Ensure consistency in data collection.

Make sure that you continue to collect data as much as possible. So you can produce accurate year-over-year comparisons. This may mean setting rules about how the website has to work. If you do have to change data collection, for example by moving to a new e-commerce vendor, then make sure to keep things as consistent as possible. Make sure that you are not duplicating data or having different names for customers or vendors.

Select a data visualization tool.

It might be that you are already using the best data visualization tool. But if you haven’t reevaluated it in a while, chances are you aren’t. Make sure that you can create reporting data directly from the warehouse or from whatever integration tool you were using. You want a tool that will automate the reports you actually use. Don’t waste time on a dashboard that isn’t useful.

Set up your reporting and analysis.

The final step is to make your reporting and analysis (reports should be automated) work the way you want it to. Test these tools before you go live and make sure that you can see changes. Some of which can be mapped to events like a promotion, and others will need more analysis.


It’s vital to make sure from the start that you have firm in your mind exactly what you need to do with this data. For many companies, this means bringing in an expert who can help you avoid going down a false trail. An expert consultant can leverage the experience of others to help you deal with your specific problems.

Hyprcubd has the expertise to help you develop a data architecture that will support your business goals, help you store and analyze your data, and ensure that your data is kept secure. We are with you through the entire process. Including post-implementation support, and we can help you with all of your big data needs.

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