Once upon a time, organisations had to choose between making capital-intensive investments in data infrastructure that didn’t start delivering benefits for years and carrying experimental data projects that helped achieve rapid yet short-lived value. Today, these organisations can not only have their cake but also eat it – precisely, unlock value in months while building a sophisticated and long-lasting data platform.
In his latest blog for “towards data science”, Barr Moses sustains that for any sizable organisation, a state-of-the-art data platform is no longer a nice-to-have but a necessity. These platforms are similar to central repositories for enterprise-wide data as they will distill that data into one single source of truth and catalyse the scaling of new data science applications and analytics programs that translate data into business value. A robust data platform can help these organisations capture substantial impact from improved efficiency and uncover new growth and revenue streams. In that sense, he introduced the multiple-layer data platform, an approach to the most needed core layers that any data platform must have in one shape or another.
In this blog, we will tell more about the different layers that should compose piece by piece a modern data platform:
- Data ingestion and storage
We start with the data ingestion layer, the backbone of any data architecture. Data flows from the source component to a storage destination. With organisations moving their data landscapes to the cloud, the rise of cloud-native data lakes and data warehouses disrupted the market, allowing for better-off options for data storage.
- Data transformation
If data storage seems to be the most critical piece to a modern data stack, transformation is often neglected. Data programs tend to scatter transformations along business dashboards, visualisation tools and manual artifacts, yet organisations that manage to centralise data transformations show unquestionably the attributes of data maturity and data-driveness.
- Data Analytics
The data that you have now collected, stored and transformed brings close-to-zero benefit if it can’t be leveraged. Hence, the data analytics layer makes the data available to all data users, teaches them how to use or how to look at that data, to ultimately see what unsuspected value they can unlock for your organisation.
Here, you have the power to make your data actionable and smart.
Regardless of which path you choose, building the core layers for your data platform will give you the bullet-proof grounds to scale and create high impact value to your organisation through augmented service or product delivery and improved time-to-insight.
Still, finding the needed budget, skill sets and timelines to build your data platform from scratch is easier said than done and might provoke a power struggle between technology leadership and business management who quite often obsess over value from the start and continuously push for quick impact.
We have figured this out for you and it is called Digazu.
As a turn-key, ready-to-use and low-code data platform, Digazu will help you optimise your data programs lifetime and solve all your data engineering concerns. Our platform allows for the orchestration and automation of every data project stage. From the collection, storage, transformation to the distribution (full streaming) of their data, companies were able to act faster and capitalise on their data assets to unlock real value.