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Agile software development and big data go hand in hand

Posted by Esolvit on Mar 2018Share

As big data tools bring together large amounts of data for business strategy planning, agile software development can help companies focus on what matters to the company first. Agile development allows for early validation and learning throughout the project.

Overview of big data

Big data management and analytics tools are integral parts of business strategy planning in today's highly competitive marketplace. They are used to support various systems including data warehouses and recommendation systems, and can be found within user-facing applications such as ERP and CRM. Big data enables business leaders to leverage both structured and unstructured data to gain a more in-depth understanding of the impact of their business practices and operations on employees, customers, suppliers, and other stakeholders.

As such, big data has become a core strategic asset for most organizations - and has made the management of data a top priority for C-suite leaders. The structured and unstructured data (collated from people and processes) that make up big data can facilitate the development of cutting-edge customer retention and acquisition strategies. It can also help corporate management make better business decisions by revealing areas where processes and products can be made more efficient. This results in the reduction of overall organizational risk.

Most enterprises leverage big data as the take-off point for their digital transformation agenda. They invest in robotics, machine learning, analytics capabilities and other technologies to ensure a successful digital transformation initiative.

Although businesses now spend most of their budget on transforming data-related IT processes and infrastructures, the benefits of doing so are only felt in isolated areas. This is because such processes and infrastructure are created for specific functional areas or business units and are difficult to implement organization-wide due to lack of central governance or end-to-end logic. As such, critical business information remains in siloes and isolated systems.

When it comes to data management, reports show that most organizations face a huge talent gap. IT and business groups have limited expertise in newer emergent approaches to data delivery, data-migration technologies, capabilities and architectures. Therefore, there is a great need for agile leaders who understand the importance and benefits of big data in both tech and business groups.

For businesses to rapidly generate analytics-based insights to facilitate better business decisions and more efficient processes, they need a coordinated data-management strategy that can be deployed across multiple functional and business units. To combat these challenges in big data management, cutting-edge businesses have begun to leverage agile practices in running their data programs.

Overview of Agile development

Agile refers to the time-tested methodology used by software development organizations to develop software and effectively manage the development environment from sprint planning to product release. It is a collaborative approach that helps cross-functional teams to design, build and release software applications, updates and new features rapidly to customers. It is characterized by short iterative development cycles where software is tested, refined and enhanced on a rolling basis.

Introducing Agile development to big data analytics

Similar to Agile software development practices, Agile data also focuses on a joint approach to delivery and development. The IT and business groups in Agile data are the cross-functional teams described in agile development.

Implementing Agile data requires the two groups to collaborate in data labs that focus on generating dependable insights to enable organizations to quickly address its highest priorities and realize more positive business outcomes. Organizations that deploy Agile data realize immediate product and process improvements and set the stage for future innovations and advances in big data infrastructure.

Promote learning through feedback

By necessity, agile data relies on several organizational capabilities and core principles. These principles include a business-driven approach to digital transformation initiatives and by extension, data management. Such an approach requires agile-minded organizations to create a list of opportunities for new and enhanced products/processes as well as probable business use cases based on advanced analytics.

The data pertinent to these opportunities and use cases must be collated and analyzed to identify key customer activities and characteristics. For instance, financial institutions that face disruption from emergent digital firms may conduct a detailed analysis of critical business factors (such as time to serve customers or purchase behaviors) to increase product/service offerings to consumers, reduce costs and improve internal processes.

Once identified, business and IT teams rank-order the use cases and opportunities and determine the levels of big data architecture, quality and governance required for each. This results in the creation of two detailed road maps. The first highlights digital business budgets, timeframes, objectives and milestones while the second defines the data requirements needed to provide seamless analytics support and build effective big data architecture.

Early validation of software and continuous improvement

The collaboration between IT and business teams that is necessary to develop these roadmaps also facilitates the breaking down of the cultural barriers that existed in traditional organizations. IT managers are exposed to the business element while the business team familiarizes itself with the tech end of things.

Furthermore, it ensures joint-ownership of data-management and data-migration protocols. This helps the organization to quickly validate the business case for proposed solutions and ensure high-quality solutions since they are monitored from both a business and tech standpoint. Thus, applying Agile software development to big data ensures continuous improvement.