Social business software and big data

The analytics from social business software data can lead to the design of better processes, says WyseTalk.

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The conventional goal of social analytics is to gather and analyse brand chatter, says WyseTalk's Gys Kappers.
The conventional goal of social analytics is to gather and analyse brand chatter, says WyseTalk's Gys Kappers.

Big data - the practice of analysing large data sets - is increasingly driving value creation in organisations across all industries.

This is according to WyseTalk, which believes that social business software (SBS) can play an influential role in businesses' use of big data, due to its ability to capture ambient chatter produced in the normal run of business throughout the corporate ecosystem, and putting it to work directly in the enterprise.

"It is already well accepted that SBS-driven analytics can help shape a sales and marketing-led response to enterprise or customer feedback or changes in stakeholder sentiment, but its value is far greater and more immediate when used as an integral part of operations," says Gys Kappers, CEO of WyseTalk. "As such, it can inform strategic shifts in the application of business resources, including supply chain and logistics planning, manufacturing and distribution."

In a statement, WyseTalk says a McKinsey Global Institute report on big data notes that this branch of analytics will become a key basis of competition, "underpinning new waves of productivity growth, innovation and consumer surplus". McKinsey studied big data in five domains - healthcare, government, retail, manufacturing and personal location data - and concluded that it can generate value in each.

Adds Kappers: "So, for instance, a retailer can use it to increase its operating margin; a national healthcare system could use it to drive efficiency and quality; and governments could use it to reduce and boost collection of tax revenues."

According to WyseTalk, SBS' support of organisations' big data strategies lies in opening up a new vein of previously unharnessed unstructured data, in the form of conversations about the enterprise throughout its ecosystem of partners, suppliers, customers and regulatory oversight.

"The conventional goal of social analytics is to gather and analyse brand chatter, whether from SBS systems (internal or external communities) or social media sites (in the wild), to provide an early warning barometer of customer sentiment or feedback loop into further sales and marketing activities," says Kappers. "But of late, SBS data is also being used in corporate activities that go way beyond sales and marketing, thus entering the realm of business decision support."

For this to happen, he says, the enterprise needs to capture conversations about and with it in all its business interactions with stakeholders. Traditionally, the ambient chatter in the corporate ecosystem has largely gone unused, because it is difficult to capture.

"Unless feedback happens in a formal customer service or marketing setting, little was possible by way of recording, harnessing and responding to it as it happens, because communications is not embedded in business processes. This is where SBS in useful in making social data accessible to organisations and contributing to the swelling body of big data," adds Kappers.

One benefit of analysing the conversation is co-creation or crowdsourcing - which involves mobilising the problem-solving powers of the corporate ecosystem.

A Harvard Business Review article highlights the phenomenon of big, complex business problems, and points out the inefficiency of traditional business functions to solve them. A platform that connects companies with their stakeholders, however, allows them to harvest input into solutions to problems too big for them to solve alone.

"The analytics from social business software data can further lead to the design of better processes, efficiencies and better customer response times. Or it can feed into resolving normal business issues. For example, companies can determine distribution issues at every link in the supply chain to identify issues that would otherwise have relied on extracting data from enterprise systems, with all the integration and delay issues inherent in that.

"Companies that harness the inherently social nature of business at the interface with external corporate communities will always know what's wrong, where the trouble is, and best of all, what to do about it, because their communities will tell them. And that is where the contributions of social and big data intersect," concludes Kappers.

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