How SSCs Drive More Value as a Data Analytics and Business Insight Hub
How can Shared Services drive more value as a data analytics and business insight hub?
As Shared Services Centres optimise transactions processing, a new value-add is emerging that is fast becoming a significant service delivery to the business: data analytics and business insight.
This service derives primarily from process ownership, and greater transparency over the data that flows through processes. As work becomes standardised, and increasingly automated, employees’ focus can now turn to analysing and evaluating data. These insights drive all kinds of performance improvements, from identifying suppliers that take up more time and costs than they should, to discovering that entire process steps are redundant and can be replaced by process automation technology.
What is key, perhaps, is that beyond delivering standardised, reliable, and cost-effective processes, Shared Services Centres can also deliver business insights that drive more competitive strategies.
Holding a dental conglomerate to account via comparative data
Data can take various forms, be function- or business-specific, or support overall enterprise operations. An excellent illustration of the difference a data management strategy can make was recently shared by the New Zealand-based Abano Healthcare Group Limited, which owns and operates around 220 dental practices in Australasia through its networks - Maven Dental Group in Australia and Lumino The Dentists in New Zealand.
Allan Wong Kam, General Manager Commercial for the dental business across the region, is a chartered accountant who brings a passion for numbers and data to the more 'creative' field of dental practice.
With the vast majority of the roughly 14,000 dental practices across this region being owner-operated, Abano spotted a lucrative gap in the market to acquire practices and immediately bring them into the fold by deploying more sophisticated management practices and synergies. With, on average, one new practice being incorporated every couple of weeks, the group’s turnover is already over AU$250 million, and fast expanding.
"Abano recognised the huge potential to deploy economies of scale across a fragmented industry and provide a lot of business management best practices that individual owners were simply not equipped to bring to the table," explained Allan.
Maven and Lumino have effectively become two Shared Services Centres (one each in Australia and New Zealand) to support over 220 individual practices. With speed being crucial – the moment a practice is integrated, all assets, bank accounts, contracts, etc., immediately become the responsibility of Abano – the group has set up a robust induction and integration process which it continues to refine, improve and challenge itself to constantly be better at doing. "The respective Support Office adds a new cost component to the owner-operator so it's our job to ramp up efficiency and quickly, to help the practice drive more business and reduce other costs to offset that cost – and provide more value," explains Allan.
The success of this strategy hinges on data. "On the basis of ‘you measure things that you care about’, we set out to measure a lot of Key Performance Indicators (KPIs) that are really key to a successful dental practice," Allan explains. "The challenge is that most practice management systems are designed for individual owner-operators. There was nothing tailored to suit the needs of large-scale dental consolidators, so we developed our own best-of-breed solution to drive a central database.”
Every night, Allan explains, 90% of the practices’ data is uploaded into a central data repository – “which is used to drive all of our financial systems, revenue, pay commissions to dentists, manage all customer data, provide analytics and so on."
Having access to this kind of real time analytics data is crucial to help drive maximum revenue opportunities, he explains. Metrics like utilisation rates, future appointment outcomes, recalls, revenue, treatment codes, etc. are all available at the touch of a button.
"We use Microsoft products and have made excellent use of data warehousing capability, financial systems, and customer relationship management systems available within the Microsoft applications suite within the Microsoft Azure cloud.”
So, the data is all there. But, as Abano’s Dental Business CEO is fond of saying: "So what? And what are we going to do about it?”
The key to driving value through analytics is in how to use it. What the central team across both businesses has done is spearhead best practices by comparing KPIs across all operations and driving desired behaviours. For example, says Allan, CRM systems are only useful if you can actually contact your customers [patients], “so we incentivised individual practices to update emails and mobile numbers of all their patients. It's one of the key KPIs for the front desk and drives greater efficiency for recalls and future bookings.”
Another KPI encourages efforts to fill the appointment book and ensure there are sufficient patients to fill chairs by minimizing failed attendances, or empty slots. Other KPIs measure revenue drivers, for example by ensuring the practice’s front desk targets full capacity and tracking individual clinician productivity. This transformation effort also requires evaluating the range of skills that drive practice productivity. These are not just technical, explains Allan, but also soft skills – patient satisfaction for example, which is tracked through Net Promoter Score surveys.
All this data is useless, however, unless you report on it to the right people. Abano has made extensive use of dashboards, which are today predominantly visual. "In the early days we sent around a lot of tables with numbers and traffic lights, but we soon discovered that dentists are primarily visual, not numbers oriented, and the majority prefer graphs – so we moved to using Excel graphs as a tool to reflect that preference.”
Today, the Support Office compares individual clinician productivity via charts allocating each to first, second, third, and fourth quartile according to benchmarks. “What we have found is that far from lowering morale, clinicians are calling us to find out how to get into a higher quartile,” says Allan.
More recently, the data has been leveraging cloud-based storage and delivery within Office 365. That means that the data can now be presented within the Microsoft Power Business Intelligence Tool (PowerBI) application. The reason this is so significant, Allan explains, is that data and dashboards don't stand still. "We want to make sure we stay ahead of the market so having real-time data and being able to tailor the reports for our practices, is key."
Abano’s central team can now deliver reports and dashboards across any device in a graphical format. In addition, anyone can drill down from one of the top KPIs into its sub elements – on the phone, on a tablet, an iPad, on their PC …“it’s device agnostic," Allan adds.
Of the 60 pages of data, on average, which is updated each night, a small amount is distilled for daily reporting. "We don't want to confuse people by giving them too much data on the front page – only that which they need to identify their gaps and opportunities … but still giving them the tools to drill down to understand what the key drivers are for those opportunities," says Allan.
The other exciting prospect is that Abano can leverage other tools in the Microsoft Azure Cloud – the company is already looking at machine learning, AI applications such as using Bots for customer interfaces rather than filling in a form, robotic process automation (RPA), let alone the opportunities that the Microsoft Azure Cloud delivers every couple of weeks in updates and new functionality that the business is only just beginning to explore.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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?
- Plan for the future – Once you understand what data is available, design a plan for the data that isn’t available.
- New tool sets treat data as a virtual lake – Rethink "storage", access, and time.
- It’s not what you do, but how you do it that defines the future.
Using data to drive better decision-making via Intelligent Automation at Suncorp Group
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.
The ‘data’ dilemma: Optimizing your data strategy
If automation is the magic tool in shared services right now, data is the power that runs it.
While data has been integral to linking applications and maximising the benefits of an ERP system, it has taken on an urgency more recently, given that automation’s value-add depends on the structure and accessibility of enterprise data.
Data analytics" is the biggest buzzword and opportunity for shared services. In a recent SSON survey* of shared services executives, nearly one out of three practitioners in Australia and New Zealand listed "driving enterprise wide analytics” as a key objective of their shared services strategy – while at the same time just over a third of respondents confessed they had a skills gap in data analytics, and were targeting these skills in recruitment, going forward.
Data, increasingly, has less to do with the ability to store and process, and more to do with using information. Data supports better decision-making and enables value-adding outputs based on the vast data lakes and data fabric that extend across an enterprise. But to be effective, this data needs to be clean, accessible, digestible, in context, and in the proper format for supporting decision-making and analysis. And that is where the problem lies.
As the enterprise becomes increasingly digital, vast quantities of data exhaust are released across the ether. One of the by-products of automation is transparency across everything – presenting a much fuller picture of the user profile, content profile, and context profile. But the sheer quantity of this data can be overwhelming and organisations that have tackled the challenge head-on are already coming up against the upper limits of what can be managed.
At the same time, many of the more evolved intelligent automation tools, for example those integrating machine learning or cognitive capabilities, dependent on enormous amounts of data to "feed the beast" and mine and learn from patterns.
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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