As a non-profit organisation, Australia’s On the Line receives funding from both private and public sectors to support its work in providing vital social health support services to more than 80,000 Australians each year.
Operating out of Melbourne, On the Line is staffed by 80 professional counsellors, social workers and psychologists who offer round-the-clock support and advice via telephone, web chat and video counselling. The organisation manages 22 separate lines, including public-facing services and tailored counselling programmes for specific industry groups and other at-risk community members.
At the heart of its operation is a multi-modal call centre communication system and a case management system for recording and monitoring end-user client data. It also uses dedicated websites and social media channels to build awareness and encourage use of available services, as well as separate accounting and HR software packages.
For a while now, On the Line has been grappling with the onerous process of drawing data from several disparate systems to fulfil its service level agreement (SLA) reporting requirements, relying heavily on manual entry and taking days to complete. There was a clear need for business intelligence (BI) software to simplify its reporting requirements and to streamline business processes.
Samantha Fredericks, head of brand for marketing and growth at On the Line, says after looking at a number of software packages, the organisation decided to go with Yellowfin, a Melbourne-based supplier of cloud-based BI and analytics tools. The roll-out was supported by Yellowfin’s partner, BI services provider Toustone.
“The decision to go with Toustone was easy” says Fredericks. “We liked the idea of dashboard reporting, the simplicity of use and the fact that everything was integrated, giving us exactly what we were after. The Toustone team was super helpful and switched on, which helped us identify the clear winner.”
On the Line implemented the project in phases, focusing initially on social media channels, followed by the call centre communication and case management systems.
The social media element was up and running in less than a day, thanks to pre-built connectors and dashboards for most social channels. This gave the marketing team immediate access to comprehensive data, enabling quick analysis of each channel’s traffic and trends. As a result, the team could better target the needs of people seeking social health services.
Also, by pulling data from the call centre communications system and using Yellowfin’s visualisation dashboard, On the Line can easily assess the performance of each service line daily. Integrating communications data – such as time on call – with specific case management system data gives the organisation a complete picture of call by client. This not only gives insight into current service levels, but also drives development of better business practices to improve client services and to realise efficiency gains.
Glen Rabie, Yellowfin
Moving forward, On the Line plans to integrate the Yellowfin software with its HR and accounting software, delivering a business-wide view of the current performance of each line of business and taking advantage of automated reporting from previously unlinked data sources.
Fredericks adds: “Our business is complex and we rely heavily on technology across a range of systems that all produce different datasets. Everything varies from month to month, so seeing the complete situation over specific times means we can easily plot trends and make changes accordingly, which helps with budgeting and staffing. The more information we have, the better – but the key is seeing that information in an easy-to-understand format and one that delivers insights on how we can improve the services we provide.”
Varying BI adoption
The ease with which modern BI and analytics tools deliver insights to organisations such as On the Line has been instrumental in the success of companies like Yellowfin, Qlik and Tableau in recent years. According to Gartner, global revenue in the BI and analytics software market is forecast to reach $18.3bn in 2017, an increase of 7.3% from 2016. By the end of 2020, the market is predicted to grow to $22.8bn.
“Purchasing decisions continue to be influenced heavily by business executives and users who want more agility and the option for small personal and departmental deployments to prove success,” says Rita Sallam, research vice-president at Gartner. “Enterprise-friendly buying models have become more critical to successful deployments.”
But in the diverse Asia-Pacific region, adoption of BI and analytics tools varies according to economic development. Glen Rabie, Yellowfin’s founder and CEO, believes that in wealthier countries with large services sectors such as banking and financial services, adoption will be higher as BI and analytics tools will have a bigger impact on business growth.
But what may be overlooked by industry analysts such as Gartner in their assessment of the BI and analytics market is the difference in the way decisions are made across the region, says Rabie. “Decision-making is different across cultures and I don’t think Gartner takes that into account.”
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For one, in markets such as Japan, where IT tends to be outsourced, conversations and buying decisions on BI and analytics tools are largely driven by systems integrators, says Rabie. “In Australia, however, business users tend to buy BI tools independently of IT.”
Although the BI and analytics market is growing, growth rates are expected to taper off over the next few years as most players in this increasingly mainstream enterprise IT segment are now offering similar tools.
“From our perspective, the shift towards visual data discovery has run its course, with no competitive advantage for anyone in the market,” says Rabie. “The future battleground would be what we call smart BI or automated insights, where instead of people logging in to find out what’s going on, we’re going to look at the data, run a whole lot of machine learning algorithms and tell them what’s happening and what could happen in future.”
As with any machine learning capabilities, users will also be able to provide inputs to improve the relevance of recommended insights, says Rabie. “We’ll also learn what the user does manually and we take that learning and their reactions to automated insights to generate recommended insights.”