Organizations are turning to a new approach: Data as a Service. This strategic initiative is an investment in consolidating and organizing your enterprise data in one place, then making it available to serve new and existing digital initiatives. Data as a Service becomes a system of innovation, exposing data as a cross-enterprise asset. It unlocks data from legacy systems to drive new applications and digital systems, without the need to disrupt existing backends.
Challenges
The keys to success in the digital age are how quickly you can build innovative applications, scale them, and gain insights from the data they generate – but legacy systems hold you back.
Solution
Deliver Data as a Service within your organization to speed development, integrate data, and improve accessibility and performance.
The path to Data as a Service is to implement an Operational Data Layer (ODL). This data layer sits in front of legacy systems, enabling you to meet challenges that the existing architecture can’t handle – without the difficulty and risk of a full rip and replace. Depending on your requirements, an ODL can draw data from one or many source systems and power one or many consuming applications. An ODL can be used to serve only reads, accept writes that are then written back to source systems, or evolve into a system of record and eventually replace legacy systems entirely and simplify the enterprise architecture.
An ODL makes your enterprise data available as a service on demand, simplifying the process of building transformational new applications. It can reduce load on source systems, improve availability, unify data from multiple systems into a single real-time platform, serve as a foundation for re-architecting a monolith into microservices, and more. An Operational Data Layer becomes a system of innovation, allowing an evolutionary approach to legacy modernization.
How?
Successfully building an ODL and delivering Data as a Service requires a combination of people, process, and technology. Here’s how MongoDB can help:
People and Process
Data Layer Realization
MongoDB has developed a tried and tested approach to constructing an Operational Data Layer. The Data Layer Realization methodology helps you unlock the value of data stored in silos and legacy systems, driving rapid, iterative integration of data sources for new and consuming applications. Data Layer Realization offers the expert skills of MongoDB’s consulting engineers, but also helps develop your own in-house capabilities, building deep technical expertise and best practices.
This process for constructing an Operational Data Layer has been successfully implemented with many customers. Starting with clear definitions of project scope and identifying required producing and consuming systems is the first step to ensure success. Based on these findings, we assign data stewards for clear chains of responsibility, then begin the process of developing and deploying the Operational Data Layer with loading and merging, data access API creation, validation, and optimization. This process is iterative, repeating in order to add new access patterns and consuming apps or enrich the ODL with new data sources.
A successfully implemented ODL is a springboard for agile implementation of new business requirements. MongoDB can help drive continued innovation through a structured program that facilitates prototyping and development of new features and applications.
Technology
Why the MongoDB Intelligent Operational Data Platform?
When you choose MongoDB as the foundation for DaaS, you’re investing in the best technology for your system of innovation.
MongoDB is the best way to work with data
MongoDB lets you intelligently put data where you need it
MongoDB gives you the freedom to run anywhere
MongoDB enables data access and APIs
Consuming systems require powerful and secure access methods to the data in the ODL. MongoDB’s drivers provide access to a MongoDB-based ODL from the language of your choice. Data as a Service reaches its fullest potential when you present a common Data Access API for applications; this layer can be custom built, or MongoDB Stitch can be used to expose access methods with a built-in rules engine for fine-grained security policies.
Data as a Service should also be available for analytics. The Connector for Business Intelligence allows analysts to connect to a MongoDB ODL with their BI and visualization tools of choice, or MongoDB Charts can connect directly to the ODL for native visualization. The Connector for Apache Spark exposes MongoDB data for use by all of Spark’s libraries, enabling advanced analytics such as machine learning processes.
Benefits
Use Cases
Single View
DaaS is perfectly suited to generating a Single View of your business. When you unify your enterprise data and make it available as Data as a Service, the next step is to build an application to expose a single view of that data to those who need it. Better real-time visibility across the business, improved customer service, and insight for more intelligent cross-sell and up-sell opportunities are all within reach.
Analytics
Providing Data as a Service doesn’t just support operational applications. It can also power the the analytics that make sense of your data – faster than a traditional data warehouse. Whether you’re analyzing your unified enterprise data set for business insights, running real-time analytics to take action based on algorithms, or reviewing usage patterns to inform application roadmaps, an Operational Data Layer can serve analytical needs with the appropriate workload isolation to ensure that there is no performance impact on production workloads.
Mainframe Offload
Mainframes and other legacy systems aren’t suited for modern applications. Rigidity, downtime requirements, and high costs mean that you’re held back from innovating for the business. By implementing an Operational Data Layer in front of your legacy systems, you can build new apps faster, deliver great performance with high availability, meet new regulatory demands, and make it drastically easier to serve mainframe data to new digital channels – all while reducing MIPS and hardware upgrade costs.
And More
Building a mobile application to reach your customers any place, any time? Putting machine learning to work on your enterprise data? Building recommendation engines, adding social components to your UI, or personalizing content in real time? These applications, and any others you need to build, benefit from being able to access Data as a Service. What innovation could you power with all of your enterprise data easily and securely available in one place?
HSBC’s data assets are growing rapidly – from 56 PB in 2014 to 93 PB in 2017. Customers are demanding more, regulators are asking for more, and the business is generating more. In order to make trading data available to a multitude of new digital services, HSBC implemented an Operational Data Layer to become the single source of truth. The ODL, powered by MongoDB, enables HSBC’s development and architecture teams to meet the board’s strategy of using technology to make the bank “simpler, faster, and better”
RBS implemented Data as a Service – which they call an Enterprise Data Fabric – in order to improve data quality, reduce duplication, and simplify architectures to become leaner. The results? Cost reduction, plans to decommission hundreds of legacy servers, an environment of collaboration and data sharing, and the ability to develop new applications in days, rather than weeks or months on the old systems
“Data Fabric provides data storage, query and distribution as a service, enabling application developers to concentrate on business functionality.”
Alight Solutions (formerly part of Aon PLC) provides outsourced benefits administration for close to 40 million employees from over 1,400 organizations, but retrieving customer data from multiple frontend and backend source systems meant high mainframe MIPS costs, scaling difficulties, and high query latency. Moving to Data as a Service delivered from an ODL on MongoDB reduced query latency by 250x for better customer experience, lowered peak mainframe consumption to reduce costs, and unlocked new business innovation.
