Businesses that have put their data house in order, or made significant progress to that end find themselves in the enviable position of being able to apply their data assets to transformational technologies like AI and machine learning, and automation. The laggards risk getting left behind.

Even as analytics emerged over the last few years as a core capability for businesses with a data-driven decision making culture, companies often found themselves struggling to get the information they need out of disparate data silos.

Those that have done so, or made significant progress to that end now find themselves in the envious position of being able to apply their data assets to transformational technologies like AI and machine learning, and automation. The laggards risk getting left behind

Whether that is for mass personalisation, asset intelligence, or internet of things implementations, all of these functions require data and integrations that need to form part of the AI and automation roadmap.

According to Darren Cockerell, Head of Solutions Consulting ANZ, Blue Prism, “When it comes to being able to harness AI technologies, the ability to manage data is everything. Digital disruption relies on the ability to ingest from, and disseminate to, legacy operations.”

Darren Cockerell, Head of Solutions Consulting ANZ, Blue Prism

Cockerell says there are already many impressive cognitive technologies available today, including those for marketers designed to gather insights to better understand and redefine the customer journeys “But every single one of these solutions follows the same paradigm; you have to get data in and then find a way to act upon what the solution delivers.”

Getting access to the cognitive service is the easy part since so many exist as cloud-based SaaS applications, says Cockerell. However, he cautions that corralling the data to feed to that service is the difficult part. 

“The biggest barrier tends to be the volume of disparate legacy systems, spreadsheets and PDF documents across siloed departments. The data required is often voluminous and dispersed,” he says.

Impediments

The impediments organisations face getting their data story straight are myriad, says Simon Belousoff,  executive director of Beta Evolution, an independent digital, data analytics, and customer experience consultancy, and who was previously Head of Personalisation/ Customer Decisioning (Customer Transformation) at Bupa.

He says organisations often adopt a mindset and approach for data, CX and  AI that is based on their legacy approaches to reporting. “What they really need is a different and evolved perspective and approach. This mistake often results in the data not being available in a timely way where it needs to be used.”

Unlike in previous processes, humans are often not directly involved

Furthermore, he says, “Data available for AI is consumed machine-to-machine at scale and needs to be consumable like this.” 

He also cautions that operational silos are as corrosive as technical ones, 

“Data is not seen as an enterprise asset, that is usable for the collective benefit of customers and the business. Instead it is seen as a discrete channel or function, or a business asset that is not for sharing with others in the organisation. You need to democratise the data.

According to Belousoff, “Internal organisation data benefits from being progressively augmented with many forms of external data to deliver use case and experience outcomes and that this needs to be done in an integrated, timely and governed manner.”

Belousoff nominates the CBA’s Customer Engagement Engine which is powered by Pega and which saw 200 machine learning models created by Pega’s AI based on the CBA’s data scientist developed predictive models.  

This article was produced for ADMA by the Which-50 Digital Intelligence unity. For the complete version of this story please visit ADMA.

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