To adopt market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance, most organizations are deploying new data technologies alongside legacy infrastructure.
However, these new additions like data lakes, customer analytics platforms, and reporting and visualization tools have increased the complexity of data architectures enormously.
Companies that have been leaders in tech -adoption and are at the forefront of innovation are already taking the next step to build the next-gen data-architecture blueprints that enables them to do more with their business data and simplifies the complex data architectures of today. They touch all data activities, including collating, processing, consolidation, analysis, and visualization. Although some shifts can be achieved without re-architecting the existing IT infrastructure or data platform, many of these require careful re-architecture, which includes both older and newer technologies.
And that is not a small effort for any organization by any standards. Investing in building capabilities for standard use cases, reporting and analytics automation, data migration, real-time data processing, building data catalogs etc., can often reach up to millions of dollars. Organizations must therefore establish a clear strategic plan and invest in the right level of architectural sophistication while prioritizing those shifts that will have the greatest impact on business goals.
So, when it comes to building the next-gen data architectures, what are some of the key changes that organizations need to consider?
1. Moving to cloud-based data platforms for more efficiency
Cloud is the most disruptive driver – revolutionizing the way organizations of all sizes source, deploy, and run data infrastructure, platforms, and applications at scale.
A fully managed cloud-native lease management software allows an organization to build and operate data-centric applications with infinite scale without the hassle of installing and configuring solutions or managing workloads. It reduces the hassle of building and managing the skill sets and in-house expertise, speeds up deployment from several weeks to as little as a few minutes and requires no operational overhead. It enables companies to decouple and automate deployment of additional computer power and data-storage systems. This capability is particularly valuable in ensuring that data platforms with more complicated setups are scalable as the business demand c
2. Experimenting with real-time data processing for faster results
The cost and capabilities of near real-time data processing has decreased over the years as the technology has matured, paving the way for mainstream use. These technologies enable a host of new business applications: from real-time updates about asset usage or fleet location to analyzing real-time behavioral data from smart devices to individualize rates for loans and leases. Integrating these actions into existing processes that may run in a lease management software (ERP (Enterprise Resource Planning)) or customer relationship management (CRM) systems will bring new levels of efficiency and speed.
From messaging platforms that let you streamline your customer communication like sending notifications, reminders for payment etc. to enabling real-time push notifications, collating real time data coming in from leased medical equipment or in-vehicle connected devices, to analyzing market conditions and customer behavior to determine customized interest rates for loans/leases- there is so much more than an asset finance company can do with near-real time data processing.
3. Adopting open design principle for more agility
It is often necessary for companies to push well beyond the boundaries of legacy data ecosystems from large solution vendors to scale applications. In recent years, many organizations are moving to highly modular data architectures that are built on open design principles that can be easily replaced with modern technologies as needed.
The open design platform approach enables agility and scalability at a whole new level. In addition to simplifying integration between disparate tools and platforms, pipelines and API-based interfaces reduce the possibility of creating recent problems in existing applications because they shield data teams from the complexity of the different layers and speed up the time to market. Additionally, these interfaces simplify the process of replacing individual components as the business requirements change.
4. Decoupling data access for more accessibility
APIs (application programming interfaces) enable quick, secure, and up-to-date access to common data sets, while restricting direct access to view and modify data. Using this method, teams can reuse data faster, enabling seamless collaboration among analytics teams to develop use cases more quickly. In addition to managing API (application programming interfaces) policies, controlling access, and measuring performance, API gateways enable companies to implement usage policies for APIs. Users and developers can also reuse existing data interfaces rather than creating new ones with this platform.
It would also be necessary to develop a separate data platform to “buffer” transactions outside of the core system. In such a case, central data platforms such as data lakes or distributed data meshes (such as data lakes and data warehouses) can provide buffers for each business domain’s data usage and workload.
5. Building domain-based architectures for better structures
As data-architecture leaders strive to improve time to market for new data products and services, they are adopting a more “domain-driven” approach to design. It is expected that “product owners” of each business domain will organize their data sets in a way that is easy to consume, both for users within their domain as well as downstream consumers in other business domains, even though the data sets may still reside on the same physical platform.
Enterprises are now using data virtualization techniques to organize access to and integrate distributed data assets. Data cataloging tools facilitate enterprise-wide search and exploration without requiring full access to the data or preparation. A catalog typically provides metadata definitions and a user interface that simplifies access to data assets.
6. Extensible data schemas for more flexibility
A lot of predefined data models from software vendors and proprietary data models that serve specific business intelligence needs are built using highly normalized schemas with rigid tables and data elements to minimize redundancy. Even though this approach remains popular for reporting and regulatory applications, it also requires organizations to undergo lengthy development cycles and have solid system knowledge when they want to incorporate new data sources and data elements, since any changes can affect data integrity.
As companies migrate to “schema-light” approaches and use denormalized data models instead of physical tables, they can gain greater flexibility and a powerful competitive edge. There are several advantages to this approach: agile data exploration, greater flexibility in storing structured and unstructured data, and reduced complexity, since data leaders no longer must add additional abstraction layers, such as multiple joins between highly normalized tables, to query relational data.
Building the right data foundations can drive efficiency today and prepare your business for the growth of tomorrow. With the right lease administration software and technology partner, you can start taking small steps towards building a cohesive data foundation- a centralized database, streamlined flow of business information across departments and processes, and building a data-driven culture within the organization by providing your employees tools that help them use and visualize data in interesting reports and formats.
Headquartered in Philadelphia, USA, Odessa is a software company exclusively focused on the leasing industry. The Odessa Platform powers a diverse customer base globally, providing end-to-end, extensible solutions for lease and loan origination and portfolio management. Odessa facilitates business agility through rich feature sets including low-code development, test automation, reporting and business intelligence to ensure organizations can more effectively align business and IT objectives. Learn more at www.odessainc.com