Many organizations claim to be data-centric and data-driven, making informed decisions based on their organization’s data. However, not many can say they have an effective and operational Data Management framework in place. Implementing a Data Model program and automation can remove the burden of managing data and improve the accuracy and quality of the underlying data. This is crucial for organizations to build into their data management process.
One of the benefits of implementing a Data Model program is building flexibility or future-proofing a mission-critical or business-critical application. As the needs of the business change and processes evolve, it is essential to have a system in place that can adapt to these changes. Agile software delivery systems have had a major impact in this regard, tearing down the walls between development and operations teams.
In the world of DevOps and Agile Software Delivery, the database is often the most rigid and inflexible part of the application. This rigidity can hinder the evolution of applications as business requirements change over time. To overcome this, organizations need to implement a Data Model program for application development, which is even more important for maximizing business outcomes.
DevOps has been successful in improving collaboration between development and operations teams, but there is a need to build on this process model. This is where DevOps 2.0 or BizDevOps comes in. BizDevOps acknowledges the input of business teams and their understanding of what an application should do to meet stakeholders’ needs. By involving business teams, organizations can ensure that they are solving actual problems and aligning their software with business processes.
Implementing a Data Model program involves several processes. Business process modeling is crucial to bridge the divide between business stakeholders and developers. It helps establish a common understanding and terminology among the teams. Business process modeling tools detail the tasks, responsible parties, and information elements involved in processes, helping analyze value chains, costs, and inefficiencies.
Data modeling is often overlooked after the initial development phase but plays a crucial role in operational efficiency, quality improvement, and business intelligence effectiveness. When DevOps teams can integrate with the data model, collaboration and automation become easier. By utilizing a Data Model from the start, organizations can capture changes, improve quality, and increase the speed of delivering requirements.
Metadata management is another essential process in BizDevOps. It involves managing the information about data elements and centralizing metadata in a data catalog. This ensures consistency and confidence when referring to data within the organization. This is particularly important when upgrading and merging multiple applications into a central solution.
Continuous integration and delivery (CI/CD) tools are crucial for the success of BizDevOps. They automate the software development and delivery process, ensuring higher-quality code and alignment with business requirements. Collaboration is also vital, and organizations need to make an effort to include business stakeholders in the process.
Overall, the success of BizDevOps relies on real-time analytics and continuous feedback loops. Real-time monitoring allows teams to make smaller adjustments to optimize applications and improve the customer experience. It also helps in preventing communication breakdowns and safeguarding sensitive data.
To achieve success and speed up software creation, organizations need to break down barriers between business, IT, and development teams. Implementing a Data Modeling practice can greatly contribute to this and ensure that organizations have an effective and operational Data Management framework in place. With BizDevOps and a strong Data Model program, organizations can maximize business outcomes and adapt to changing business requirements.