Why Organizations need to take an agile approach to Analytics

By Ramesh Hariharan, Co-Founder and Head - Innovation & Technology, LatentView Analytics

Today, owing to the tremendous pace of technical innovation, organizations face a variety of threats to their business models. New advances are coming at lightning speed, paving ways for start-ups to emerge and challenge established companies across industries. For example, it’s most unlikely that executives at FedEx and UPS are not talking about SideCar, or that those in top budget or mid-range hotel chains are not taking note of AirBnb. Trends such as the IoT, Big Data and ubiquitous mobility are increasingly blurring the boundaries between physical and virtual reality, and forcing organizations to go digital the quickest they can, in order to survive.

To do so, companies must build an architecture that helps them become nimble and resilient in the onslaught of big changes. A systematic approach needs to be defined to proactively identify disruptive technologies, evaluating their capabilities, testing out specific use cases and then deploying them widely. 
Embedded analytics is one of the critical pillars of this approach. Considering the fact that enterprises are drowning in data from a variety of sources, analytics have to move beyond dashboards and predictive models, and bleed into business processes and applications. Many operational decisions can be automated by analytics on a much larger scale than what is being achieved today, leading to a more intelligent and responsive organization. Even some of the “non-routine” and rigid business processes can become more flexible when infused with analytics.

I witnessed an analytics-driven transformation recently with a traditional service provider that provides appliance installation, maintenance and repair services to a distributed customer base. The original business model required multiple visits by technicians—for example, one to diagnose the issue and at least a subsequent session to resolve it—resulting in numerous inefficiencies and increased time to resolution. To address this, the company invested in a new solution driven by advanced analytics, text mining and supply chain optimization algorithms, operating on real-time customer feedback and individual appliance details. The supply chain teams can now ensure that the right parts are available to the technician on the first visit, eliminating the need for multiple trips and enabling the company to deliver better service.
Analytics is a complex endeavor as it requires combining big data, algorithms, processing and presentation in a single place. In this environment, companies must collaborate with teams across business owners, IT process owners, data owners, and sometimes even suppliers.

Moreover, these types of intelligent platforms need to be developed through a process of experimentation, discarding what does not work and pushing ahead with what does. These iterative, collaborative and evolving processes are also the principles of agile methodologies, which have led to significant improvements in innovation and productivity over the last decade.

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