Data Drives Better Decision-making via IA at Suncorp Group



Barbara Hodge
10/19/2018

Data

Data: the new 'gold'

As Shared Services enable the enterprise mantra of doing better, or more, for less, data becomes the foundation of improvement. With continuous improvement still the leading strategy for driving performance, according to SSON’s 2018 Industry report, process analytics data plays a key role. The challenge for organisations is that getting all systems data into one repository can still leave you with discrete system-driven partitions.

"You end up with clumps of data in a data lake that still are not integrated," says Brisbane-based Tim Johnson, Head of Insurance Operations Delivery at Suncorp Group.

By applying customer journey mapping, and analysing touch points across all systems, the team was able to bring all data into a single place, but still reflect a chronological element by stamping each footprint with a timestamp. “We could put everything together and track, real-time, the progress of the customer’s journey along the process. We call it the ‘footprints project’,” explains Tim.

This approach has helped enormously with the RPA implementation, but more importantly, it also supported leveraging machine learning. “We categorised all the outcomes from the process analytics journey and used this data to fuel our intelligent automation work,” says Tim. “The data tells us where the problem is, which means identifying the journey problem, which means drilling down into the process problem. The data, therefore, helps us drive better decision points. If decisions are made timely but inconsistently, the customers experience is degraded. We need to understand decision inconsistency and apply machine learning to drive better support.”

The OODA loop developed by military strategist and United States Air Force Colonel John Boyd, based on “observe, orient, decide, act” underpins much of this activity. “This approach drives better decision-making and identifies where our decision-making goes wrong,” explains Tim. “Inconsistency is driven by different subjective guidelines or methodologies, or interpretations of inputs. We use intelligent automation to get more standardised inputs, not just structured but also with less subjectivity, to support a better decision-making process.”

Understanding the inconsistent application of rules is a key opportunity, Tim emphasises. Some decisions are subjective and cannot simply be codified to the business – “it’s kind of the vibe of the thing,” laughs Tim, referencing the infamous clip from the Australian film, The Castle.

 


Data fuels automation’s capabilities

Shared services executives that have recognised this are quickly putting relevant strategies in place. Here are 10 steps to implement in order to optimise your data strategy.

  1. Communicate data management as a core skill and prioritise these roles – With much of the actual work being automated in future, the value-add of the human workforce will be its ability to drive, manage, and work with data.
  2. Reassess the role of humans – Instead of processing transactions, in future the human workforce needs to re-focus on creating the data sets that feed automation.
  3. Find, sort, and structure your data – Whether via mapping, identifying the source, structured or unstructured, real or virtual, your team's ability to identify relevant data, and sure it is accessible and workable, and feed it into the appropriate channels will be key.
  4. Identify the black holes in your data – With the vast majority of valuable data still unstructured, one of your key challenges will be to identify your gaps and fill them.
  5. Reconsider data flows – Traditional data flows are sequential, but in robotic or intelligent automation, systems can tap into multiple data sources at the same time. This means rethinking data flows from the beginning.
  6. Data-oriented processing creates an ever-wider circle of influence – More and more activities will be pulled into scope once you open the doors. Plan for this.
  7. Shifting into more sophisticated tool sets changes requirements – Do you have the data necessary for those new systems? If not, how quickly can you access it? Or does it make sense to reassess your tool implementation strategy?
  8. Plan for the future – Once you understand what data is available, design a plan for the data that isn’t available.
  9. New tool sets treat data as a virtual lake – Rethink "storage", access, and time.
  10. It’s not what you do, but how you do it that defines the future.

 

Summary

In order to manage this data, organisations cannot overlook the importance of a robust data strategy, which provides a framework for identifying and surfacing the data that's relevant for a given activity or process.

This challenge will only become more apparent as organisations embrace the cognitive capabilities associated with machine learning, which requires not just large quantities of data sets but also large quantities of patterns for these machines to observe and learn from.

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