As a new way of building high-quality analytics systems, Agile analytics promotes maximizing of business value in functional areas like marketing, operations and supply chain. With practices for project planning, management and monitoring, Agile analytics enables effective collaboration with clients and stakeholders and ensures technical excellence by the delivery team.
Like Agile software development, Agile analytics is based on certain principles. It is not a rigid methodology but a development style that emphasizes on the client’s goals to make better decisions using data-driven predictions. According to Gartner, “Analytic agility is the ability for business intelligence and analytics to be fast, responsive, flexible and adaptable.” It is a continuous process of iterating, testing and improving the analytics environment.
Agile analytics includes practices for project planning, management and monitoring in order to have effective collaboration in the business process.
The three themes that any supply chain enterprise needs to focus on in order to adopt the Agile approach are:
Speed is a stepping stone in the Agile process. Firstly, one must identify business and technology challenges and plan the appropriate methodology to mitigate roadblocks. Agile methodology assists in extracting large volumes of raw data from multiple sources and transforms it into meaningful business information. Data insights are becoming rapid with machine learning techniques that make insights available in weeks rather than months. This helps in cost optimization and qualitative workflow management.
Flexibility refers to the adaptability to changing business needs. The important points to consider are data exploration, visibility and usability of data. In Agile analytics, projects are developed with short iterations so that at the end of one iteration, the result achieved can be displayed and the user can see a working version of the software before moving to the next iteration; this way, the overall project will be much more flexible. This methodology is flexible in terms of time, scope and quantity of the project work.
Responsiveness is a call-to-action process. In this theme, one identifies a new business problem through predictive modeling of the available data. It gives a preview of certain business risks that may happen while working on certain technology, tools and software. Agile analytics methodology is a modern self-service model to handle the entire IT landscape. Therefore, IT can be more responsive to the needs of the business and more proactive in supporting and scaling the necessary infrastructure.
Agile Analytics Use Cases
Global Traceability Solution
A common problem faced by global pharmaceutical manufacturers is the challenge in managing and restoring fragmented data. The supply chain involves a large number of stakeholders and a complex process from manufacturing to shipping to the end user. To overcome this hurdle and increase productivity, the latest ERP systems could be adopted in the process.
- Real-time material mapping,batch genealogy and chain of custody information would provide an overview of the end-to-end material flow from purchase order to plant to distribution to shipment.
- Context-specific visualization and drill-down ability could assist in converting complex processes into a simpler format for better workflow management.
- Google-like search over all product and batch elements enables real-time tracking of products, system and location. This would assist in identifying repeated issues in products.
The business outcomes of these processes are increased transparency, more effective cost management and lower times to insight.
Inventory Management Solutions
In any manufacturing industry, inventories play a crucial role. Managing inventories involves complex issues like maintaining stocks in terms of quantity and quality, data integrity and end-to-end visibility of inventories. To overcome this hurdle and get better output, manufacturers can adopt end-to-end processes.
- Value Stream Mapping is a lean management method for analyzing the current state and designing a future state for the series of events that take a product/service from the beginning through to the customer with reduced lean wastes. This helps to identify potential failure points, systems and data needs.
- Data Modeling is a detailed visual representation of your databases with many contexts for different stakeholders with different perspectives of working with data. It is where business and data align. This assists in identifying the correlation.
- Root Cause Analysis is an approach for identifying the underlying causes of an incident so that the most effective solution can be identified and implemented.
- Predictive Modeling is a process that uses data mining and probability to forecast outcomes.
These approaches help manufacturers reduce failure of inventories, automate manual processes and increase accessibility to multiple data sources.
Traits like early delivery of production quality, delivering the highest-valued features first, tackling risks early and continuous stakeholder and developer interaction determine true agility. By continuously seeking and adapting to feedback from the business community, Agile analytics teams evolve toward the best system design. With Agile analytics, companies can balance the right amount of structure and formality, with a constant focus on building the right solution.
About the Author:
Sampriti is Marketing Executive at Relevance Lab.