Organisations buying business process optimisation services are keen to make the most of data analytics, according to a study by independent analyst HfS Research.
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The research, sponsored by Accenture, found that buyers have very high expectations for analytics to derive value from processes, but 15% felt their service providers would not really add business value.
According to HfS, almost half of the buyers surveyed said service providers were expected to be sources of insight – not just by providing descriptive transactional data from the process they operate, but also by providing predictive insights on how the processes can affect their overall business outcomes.
However, HfS found buyers are not likely to have given their providers access to the necessary data to create a deeper level of collaboration to bring business insights forward.
HfS recommended that buyers give their providers access to their intimate data if they really want value from the provider’s expertise. Several providers told HfS during the research process: "We wish our clients would give us access to their data so we can help them."
HfS warned that traditional cost-based labour arbitrage contracts are not suitable for delivering more than the occasional descriptive insight. This is because service providers would not have the capabilities to provide broader insights.
HfS believes analytics value will be created by automation. The push for early analytical insights currently provided by automation-light, people-heavy analysis will be replaced by a more integrated analytics engine once broad transformational processing platforms become the norm, says HfS. The analyst believes there will be two distinct analytics periods in BPO: the largely manual period of today and the predominantly automated period of the future.
According to HfS, BPO clients will eventually see their efficiency gains wither away if they cannot break out of the labour arbitrage model. Automation will provide the bridge between the labour arbitrage past and the state of technology-enabled transformation.