Every business that wants to compete successfully today needs to be aware of the prime role that data-driven decision-making plays in its journey to success. Studies show that customer acquisition is 23 times more for data-driven organizations than those who ignore data.
It's noteworthy that the COVID-19 pandemic accelerated digital adoption in almost every customer segment resulting in a massive influx of data that businesses can use to their advantage with the right data strategy.
To set up a successful data-driven business model, the primary need for any modern enterprise is to have a great data platform that can manage its end-to-end data needs. So, the next obvious question that leaders have is, "What goes into building a great data platform?"
Let us explore the core components or layers of a modern data platform that enterprises cannot ignore in today's context:
Storage Layer
We cannot talk about data platforms by ignoring the most fundamental necessity that any enterprise needs - a means to store their data. The storage layer holds data for as long as needed, either before computational processing or after it. The speed of retrieval and copying of data in the storage layer is critical in ensuring timely data-driven operations for the business.
The storage layer can be built as an on-premises infrastructure or leveraged as a cloud offering like Snowflakes, a cloud data warehouse solution. The cloud option will be beneficial as it offers on-demand scalability, which is essential for the highly dynamic digital workloads that enterprises deal with today.
Data modeling often happens before data is stored for further use. Enterprises leverage different data modeling techniques to organize data in a pattern that different business systems can consume over time.
Transformation Layer
While data modeling deals with the spatial organization of data, the transformation layer is what makes the data ready for analytical processing. It collects raw data from the storage, applies business logic and appropriate filters to clean the data, and provides a curated and customized dataset that can be analyzed for insight generation.
In simple terms, the transformation layer adds purpose to the stored data, which makes it easier for computational processing in the next stage.
Analytical Layer
This is the most important layer of a modern data platform. In fact, in most cases, a data platform is considered an analytical solution that provides decision-makers with insights. The analytical layer deep dives into the vast treasure trove of transformed data to learn hidden facts that the data generated is trying to convey. It does so by connecting the dots between different data sets, uncovering patterns that point to specific outcomes or trends, and much more.
Data Assurance Layer
The criticality of data in empowering businesses with the right direction of growth makes it important to ensure that there are no downtime or quality issues with the data supplied. The data pipeline needs to incorporate lessons from best practices in DevOps and quality assurance to ensure a steady and continuous stream of error-free, high-quality data flowing from different business systems into the data platform.
Automation Layer
One of the best ways to ensure that the data platform leveraged by an organization serves its objectives without bias and at a faster pace is to encourage automation of data asset management. From quality checks to interoperability between different business systems, a considerable focus should be on automating data workflows and information exchange. This will ensure that all relevant stakeholders in the organization have access to insights from the data platform seamlessly without risks.
Discoverability Layer
The discoverability layer performs a two-way function of first ensuring that the data platform gets the data it needs transparently for computational and analytical processing, and secondly, all stakeholders get insights from the data for their use.
The first part can be called the ingestion layer, which compiles data from different systems after transformation into desired formats and makes it available for use by the data platform. The second part ensures that connected processes, systems, and platforms can seamlessly unearth the required data insights for reporting and other similar initiatives.
Summing Up
By 2031, the enterprise data management market will be worth $271 billion. As more businesses realize the need to efficiently manage and learn from their data assets, there will be more emphasis on building state-of-the-art data platforms that help in the process.
Selecting the right tools, formulating the right data transformation strategy, and establishing the most profitable analytical infrastructure are all pain points that enterprises must resolve in their quest to build a sustainable data platform.
This is where Wissen can help bring about a major competitive difference. Connect with us to explore more about building the best data platform for your business and embracing digital supremacy faster.
