There’s never been more data available, but that data is only as useful as it is easy to consume.
The amount of information that your organisation can use only increases, so to use it well, you need to make it accessible and understandable for people and for programmes. Of course, you also want to keep it clean, consistent, accurate, and timely in the process.
That’s where Data Fabric comes in.
What is Data Fabric?
Think of Data Fabric as an approach to data, or a data ecosystem. It’s not a product or a solution that you can buy — it’s an architecture for managing your data, which uses tools to help you govern your metadata, your master data, data integration and data delivery.
Typically the tools include:
- Data governance
- Data security and data access
- Data integration
- Data Engineering
- Data Lake and Data Warehouse
- AI, including machine learning and large language models
- Analytics, and data visualisations
Why do we need Data Fabric?
In short, because data needs to be easy to consume.
With the volume of data that we have, and the growing number of applications that use it, keeping that data readily available and easy to process means that you maximise its potential. On the cultural side, if teams can easily access data, they will feel empowered to use it.
It’s a question of accessibility, both in the sense of being able to find the data when required, and being able to use the information that’s available.
If you can’t trust the data, or it lacks context, that’s as bad as not having the data (or worse). A Data Fabric regime is built on metadata, which means your programmes and your people have the context that makes data meaningful.
If teams struggle with either accessing or trusting data, then they can’t use it, and that has a knock-on effect. They might base their decisions on inaccurate or incomplete data, or no data at all, which means those decisions are at best guesswork, and at worst commercially dangerous.
They may also miss opportunities. Well-structured, easy-to-access, easy-to-use data helps you build a picture and tell a story, even if data is not your area of expertise. If you don’t get to spot the patterns that data describes, then you miss the potential commercial potential for new offerings, initiatives, and savings.
What is the benefit of Data Fabric?
Once you have a robust Data Fabric, then you can:
- Eliminate data silos, so that the whole business has access to the same data, giving you greater unity in your decision-making and strategy
- Trust that your data is secure. Security is built into a strong Data Fabric architecture, plus knowing where it all is, means it’s easier to protect and monitor
- Realise new commercial potential. More and better data means more and better decisions
Ultimately, Data Fabric helps you monetise your Data by unlocking commercial potential and preventing losses.
Would we have to rebuild our data architecture for a Data Fabric?
Because Data Fabric is an approach, not a programme or a product, most organisations have a data fabric of some kind already. Some are more optimised than others, so how fundamental your transformation will be depends on the current state of your data ecosystem.
You may only need to make some adjustments or add some components to your data architecture. A typical Data Fabric consists of:
- Data governance
- Data engineering
- Data Lake and Data Warehouse
- Security and Access
- Analytics and visualisations
- Machine Learning, AI, and Large Language Models
You may already have a solid foundation to build on.
What could stop us from implementing a Data Fabric?
Aside from cultural concerns, there are some barriers to Data Fabric that you will need to address first.
- Poor data
If you had a library, but all of the books had become unbound, then a gust of wind had mixed up all of the pages and blown some away completely, your priority wouldn’t be to build a new bookcase to stuff the jumbled heaps of pages into. Your priority would be to put your books back together. Data quality and governance are integral to your Data Fabric implementation.
- Poor Data culture
If you are truly data-driven — meaning you have and use meaningful datasets, that information informs your decisions, and that all team members respect and value data in their roles — then you have a good foundation on which to build a Data Fabric.
There’s no point in attempting to create a proper Data Fabric if you lack the cultural basis for it. If your current architecture isn’t strong enough to build a Data Fabric on, then that’s almost certainly a sign that the underlying data culture is weak or absent. A data-driven business would have those structures in place.
- Poor metadata
The context of your data is what gives it value. Facts, figures, and numbers in isolation don’t offer any benefit or tell any story, nor are they properly searchable without the metadata to support them.
- No Master Data Management (MDM)
An absence of MDM undermines a Data Fabric completely. Data Fabric enables business-wide data access and encourages its use for all business functions. MDM ensures that there is a single version of the truth (SVT) and that anyone who looks for data finds accurate, complete, and timely information.
How can we start building a Data Fabric?
The first step is to assess the alignment between data outcomes you need, and the potential that Data Fabric offers. Our guide can help; Democratise your data to extract its full value, an Agile Solutions guide to introducing this flexible data architecture.