How Fintechs can create better data products with data mesh
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Financial technology (fintech) companies have grown significantly and disrupted the traditional financial industry in recent years by leveraging innovative technologies and data-driven approaches. Data engineering and advanced analytics have revolutionized how data is processed, stored and analyzed making it possible to uncover patterns in real-time that can help respond to changing customer preferences and their buying habits.
Being a fully digital business, Fintechs have access to large volumes of data that is key to successfully drive new product development. However, when data is trapped in silos and business teams grapple with multiple versions of the same data, businesses fail to unlock value at scale. A study from Accenture and IDC shows that only 32% of IT leaders reported realizing tangible and measurable value from data. This is where the “data mesh” concept comes into play. In this article, we will examine how fintechs can use the data mesh approach to create superior data products and obtain a competitive edge in the market.
How can data mesh help?
Fintechs today benefit from a data mesh, which promotes a domain-driven data architecture and gives business teams direct control over the quality of the data products they use. A data mesh consists of best fit platforms including data lakes, data warehouses, and data lakehouses for each business domain’s workloads and requirements reducing costly workloads on the mainframe. This architecture gives the ownership of data products to different teams rather than limiting it to the IT department leading to responsible capture and management of data to ensure the highest quality. This opens the road to AI with a new way to innovate and experiment using data-rich use cases to deliver meaningful value. The data mesh architecture underpins the foundation of a domain-oriented structure and quality-centric culture required for AI/ML use cases.
5 ways data mesh improves development of data products
1. Data democratization:
A data mesh facilitates self-service applications from various data sources to collect data such as user verification, card management, payment services, and customer support to expand the access of data beyond technical resources such as data scientists, data engineers, and developers. In fact, data scientists can easily access data from multiple sources to reduce the time spent on processing. Fintechs are moving toward becoming vendor-agnostic and no longer want to be associated with a single data platform provider. Data mesh acts as the perfect methodology to create a distributed architecture for unlimited flexibility and scalability of data. Fintechs can easily transfer data in multiple ways on a daily basis empowering various users to seamlessly work with data and create data products without overwhelming them with technical nuances.
2. Data product approach:
By improving the visibility into data, data mesh plays a crucial role in enabling a stronger data stewardship among various business units. With the required data alignment and order in place, data mesh results in a mature data architecture that acts as a catalyst for creating data products supporting a number of business operations such as transactions, portfolio management, trading, risk management, cash management, and more. Teams can successfully create high level product groups around Wholesale Credit Risk, Party and Trade and Position data as products with each of them having sub-products like KYC, under Party. A strong collaborative culture among teams promotes the development of data products to meet the specific needs of each domain by operationalizing data wherever it is located. It also builds a collective knowledge environment to keep teams informed on how data is being used across the organization and iterate on those capabilities to operationalize similar datasets to achieve different goals.
3. Data interoperability:
Data mesh eliminates the step of shifting data from one location to the other predominant in other data warehousing and ETL models avoiding duplication and facilitating faster and resource efficient processes. Data mesh maintains interoperability with data virtualization that creates a logical layer between data sources and data consumers. This layer enables users to easily access the data they require without any delay. Data mesh enables Fintechs to make the data, including data from newly integrated sources more discoverable and accessible reducing data silos and operational bottlenecks. This in turn fuels faster decision making and accelerates AI model development to achieve automation goals to provide customer value.
4. Data governance:
Fintechs can interweave their data governance strategies across the data mesh to meet legal compliance and data privacy requirements. Data sharing across domains and product owners can be protected with a centralized integration layer for transactional and analytical workloads. Different data sharing rules and access in place enable domain groups to process data smoothly while ensuring complete data security and protection. The overall data mesh architecture enables fintechs to embrace a new culture of data sharing and domain-centric data platforms that establish true ownership and governance of data. The data mesh can also further demarcate clear roles and responsibilities across different domains and users for effective governance. Fintechs can enforce security and anomaly detection programs without having to connect to numerous data systems. Rather, they can set up domain oriented data products for anomaly detection to create better outputs for fraud detection and security.
5. Data compliance:
Improved visibility, management and governance of data can offer fintechs compliance benefits as well. Compliance requirements such as GDPR require fintechs to build a complete view of their data to showcase regulators how their data is being used and by whom. Data mesh enables fintechs to create bespoke data products for compliance and regulatory needs that can create reliable reports with clear mapping of product owners and users. This also helps them to work collaboratively with regulators to adhere to compliance and tailor their products to meet new requirements. With data mesh, financial institutions can effectively provide all the answers expected of their data to regulators and governments.
Fintechs can improve visibility, management, and governance of their data with a data mesh. Above all, it helps them accelerate their data initiatives in a secure and compliant manner. Using a data mesh, fintechs can develop bespoke solutions around risk, compliance, and customer service that bring value to their business.