At CAPIOT we have been advocating leveraging the #DataFirst approach for sometime now. We often get questioned by stakeholders on how being data-first helps deliver better RoI for digital initiatives across the enterprise.
In this post, we focus on how decision-makers can calculate the return on investment (RoI) of data-first approaches. We examine two different scenarios and demonstrate the cost savings generated from data service platforms, owing to the advantages of reusability and easy maintenance.
What does it mean to be data-first?
Being data-first means enabling seamless access to enterprise data in a standardized, secure and self-service manner. This requires executive sponsorship and a culture that ensures enterprise data assets are treated as shared assets available on tap.
What is the need for being data-first?
One key factor that hurts the speed of digital initiatives is lack of access to the right data at the right velocity, and at the right time. With technology/IT teams being under mounting pressure to accelerate project delivery, even while being constrained by limited budgets, it is extremely critical that data access hurdles are made a thing of the past.
One efficient way to resolve data access hurdles that are stymieing time to market at your organization is adopting a data service platform that enables a #DataFirst approach.
What does a data-first platform do?
A data-first platform enables organizations to deliver data as a service.
In CAPIOT’s case, our Omni Data Platform is a leading data-first platform that creates centralized data repositories with cleansed and well-governed data. It uses APIs to expose data as a service (each data service API is deployed as a microservice on cloud-native architecture) that can be easily reused so clients can dramatically speed-up time to market with minimal effort, risk, and cost. Reusable APIs drive new functionalities quickly, help build new capabilities on-demand, simplify data maintenance, deliver out-of-the-box security, and promote real-time monitoring.
How do I calculate the RoI for a #DataFirst approach?
Let us consider two scenarios to understand the actual savings achieved from a data-first approach.
Scenario 1: Measuring the returns of data reusability in a #DataFirst approach
From datasets to data assets
Take the case of a bank that stores various types of reference data within different systems like CRM, loan origination, loan management, marketing, mobile/web apps, and contact center systems. Each system has its own way of defining, storing and retrieving data, with different application owners who are responsible for its management.
Say a department wants to roll out an enterprise-wide project like introducing a new credit card or payment app. This can present certain challenges when building the required technology platform. For example, identifying what dataset to use from which enterprise system (CRM, loan origination system, loan management system, etc), whether the systems have all the necessary data needed, and which system owners should the new project team communicate with to retrieve this data. The new consumer ( system) will also have to ensure that, when querying for data, the incumbent system doesn’t fail and is able to address the required SLA. Further, different systems store data in application-native formats, making any integration difficult. Finally, once all the data is retrieved, business analysts will have to verify that the dataset is complete in order to run the new project. Such issues affect the pace and quality of the project.
With a #datafirst approach, the enterprise exposes different datasets as data assets through secure and scalable APIs. This API-fication of data standardizes access across the enterprise, making these data assets seamlessly reusable by different business units for different projects like KYC, AML, Real-time offer management, etc.
The illustration below demonstrates how using APIs to create data services delivers savings. In this real example, exposing customer reference data needs 50 data APIs to be created, of which 60% are reusable.
The total savings achieved across 60 projects in 3 years amounts to USD 3.6 million!
Scenario 2: Measuring the returns of data maintainability in a #DataFirst approach
Manage data easily with one-click updates
Say, the bank’s reference dataset contains a field of ‘State/district’. In traditional data stores, it may be represented differently by different systems. For instance, ‘Bangalore’ can be ‘BLR’ or ‘BLORE’. In cases where an update is needed (like when Bangalore was changed to Bengaluru), the update has to be done manually across every single system where customer data is stored, which takes significant time and effort besides creating data inconsistencies.
In a #datafirst approach, executing this change becomes a simple one-click action. Since data is standardized and exposed via an API, the ‘district’ field – Bangalore – has a single definition. During the update, the name change is made only once (rather than across every enterprise system) in the data asset. Besides slashing effort, any system that calls on this data asset will have uniform data.
The figure below helps calculate the savings achieved when maintaining data APIs. In this example, maintaining reference data needs 50 APIs to be created, of which 60% is reusable. The time taken to maintain a single API service for the data is 120 hours at the rate of USD 50/hour.
Thus, the total savings across 60 projects in 3 years is a staggering USD 7.2 million! More importantly, these benefits do not take into account the reduced effort in maintaining data assets.
As you extend the scenario further for, whether it is defining and unifying product data, scaling data assets, or streamlining reports, becoming #DataFirst delivers tremendous returns and is the first step to becoming a digital enterprise.
About Omni Data Platform
Omni Data Platform enables organizations to deliver on the #BecomeDataFirst approach that enables democratic access to data by providing microservices-based Data as a Service solution.
Omni Data Platform lets you integrate, govern, distribute and analyze data seamlessly, opening up new opportunities to collaborate. ODP is a secure, API-first, high-throughput, cloud-ready platform that businesses can harness to unlock the real potential of data while sensing opportunities and threats and improving operational efficiencies to make the enterprise truly agile.