Data is the talk of the town right now, thanks to the smash hit that is AI tools. Training AI systems, however, is the last step you need to take with your data. Before you rush into trying to beef up systems, you need to get your data in order.
Enter: data fabric.
Comprehensive data fabric architecture works to seamlessly integrate all your information, manage it, and then use it in ways you only ever hoped for. There are many different components that make up a robust data fabric solution, and knowing each of these parts is how you can best strategize and prepare to collect, manage, and use your data.
What is a data fabric?
Data fabric architecture works to collect all your datasets under a single roof, often called a data silo. The goal is to have all your information in one place, organized, and sorted. Once this is done, you can then use more analytical and even AI tools more effectively. You are also better prepared for scaling up (or down), securing your data more effectively, and integrating data governance so only authorized users have access to your info.
Why is it important to have a data fabric solution in place?
There are many reasons why you need a data fabric if you operate a large-scale business, including:
- Keeps all your data together.
- Speeds up data acquisition.
- Helps make managing data easier.
- Secure your data more effectively.
- Improve your data governance and compliance.
- Make real-time analytics.
- Reduce operation costs.
- Scale your data structure easily.
What are the key components of data fabric?
Now that you know a bit more about data fabric and its benefits, it’s time to get into what makes up a data fabric solution in the first place. Knowing these components is key when it comes to structuring your data integration approach, as well as building your data fabric solution.
Data storage
The first element to a successful data fabric is to invest in a single-source solution that is capable of holding all of your data. This can be a cloud-based solution, an in-house set of servers, or a hybrid approach. So long as the solution is secure and the data is redundant (if one server fails, your data is not lost), you are ready to move on to the next key component of your data fabric system.
Data ingestion
Every solid data fabric solution needs to be able to then ingest all your datasets, from wherever they are located. This means bringing in information from your existing servers, analytical tools on social media, sales info from a different software solution, and so on.
Data processing
You then need a way to process the data as it comes in. This can be done with cloud-based or in-house solutions, but essentially you need a tool that can parse through your incoming information so that you can then sort it with metadata.
Metadata management
While your data is being processed, it needs to be sorted. It needs a full-scale metadata strategy. Metadata, otherwise known as data about data, helps analytical and even AI tools better understand what a document is, what to do with it, and how to use it with other datasets.
When it comes to adding metadata, consistency is key.
Data cleanup
One of the big benefits of pooling all your data into one source is that you can then structure it and remove duplicates. You can keep all the previous versions of a document in the same file and can remove unnecessary copies. As a result, managing your data fabric ends up costing less, while offering you more.
Data governance
Ensuring that only the right people have access to your information is critical, and so much easier to do in a data fabric. Make sure that you implement user access control, encrypt your data in transit and at rest, and also work on removing outdated software from using your data.
Data management tools
From there, it’s all about how you want to use your data. With a robust data fabric architecture in place, analytical, AI, and management tools will easily be able to work more efficiently, allowing you to extract actionable insights that can help your business stand out from the competition. After all, it’s expected that insight-driven companies will grow.
Insight-driven companies are more than 8.5x more likely than their counterparts to see 20% yearly growth in their revenue. With AI systems becoming the norm, making sure your data is ready has never been more important.
