He cited examples in public transport where analysis of real-time integrated data helped public agencies reduce fuel consumption. At Melbourne airport, for example, analytics plays a significant role in assessing passenger traffic as well as vehicle maintenance to ensure the passenger experience is optimized at all times without unnecessary delays or disruptions.
The conversation continued through a lively question-and-answer exchange with IT leaders from across logistics, shipping and transportation sectors, sharing their thoughts on the role of analytics in their businesses. Several themes emerged:
Resistance to Change
There was a consensus the logistics and transportation sectors were undergoing a digital transformation in applying analytics to their business operations. Though very conservative in their thinking, many enterprises in these sectors have deployed analytics to predict demand and supply, and have had good results.
Conversely, some models have not worked well due to insufficient data and inexperienced practitioners. However, there are clear links to commercial decisions in applying analytics. This has helped filter operations and customers that boost profitability from those that don't.
Most enterprises have setup innovation teams to manage analytics efforts but they lacked the breath and depth of subject matter experts externally. Instead, enterprises are building partnerships with vendors to drive them into the businesses. The goal is to use their expertise to identify opportunities within the business operations to apply data science and create richer collaborations.
Sharing Data and Integration
Establishing clear parameters and agreements on the sharing of data was a key concern for many companies in the logistics and transportation sectors. With numerous data touch-points across the entire transportation/shipping supply chain, it was challenging to update the data in time.
Matibet noted that there are industry discussions around monetisation to buy or exchange data for mutual benefit. "Even without complete data sharing, there is value in applying analytics to internal datasets, " he said.
"You could, for example, apply analytics to demand data management modeling to improve efficiency. Once you've acquired the skills around this process, you could extend it to other data sources in the ecosystem."
Architecting for Analytics
Could the cloud offer the ideal analytics platform to provide scale and resolve data integration issues? Some participants were not convinced though they acknowledged the cloud was a good development platform. They proposed a mixed-mode approach with some forms of analysis benefitting from a cloud platform.
Clifton Phua, Director of Analytics at NCS Group, observed that mature organisations center their architecture design around data linkages. "Using the data links, you can accommodate legacy datasets and integrate new external sources. It's flexible and scalable, and you can layer analytics easily over it."
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