The workshop on the role of analytics and AI attracted nearly 100 business executives
Top regional experts from PwC and Microsoft shared current trends in data analytics and artificial intelligence (AI) and revealed how these technologies can help businesses gain more insights internally and externally.
The power of data analytics and AI
Sales transactions, customer interactions, and other business activities are generating vast amounts of structured and unstructured data everyday.
According to International Data Corporation (IDC), data production is expected to double in volume every two years for the next decade. However, only 0.5 per cent of all data is ever analysed and used.
Data analytics’ purpose is to analyse and conclude valuable insights from these enormous volumes of data to support businesses’ decision-making and influence the future performance of the organisation.
Meanwhile, AI is increasingly finding use in many industries, including manufacturing, logistics, transportation, and finance and banking, among others.
For instance, AI can help organisations to automate non-value adding processes, identify fraudulent claims and invoices, steer self-driving vehicles in logistics, and drive customer interaction and engagement via mobile channels.
Companies operating in sectors like energy, maritime, real estate, and mining can use video analytics to detect intrusions, identify abandoned objects, evaluate traffic flow density, and enable facial and character recognition.
|Scott Albin addressing the workshop|
According to Scott Albin, South East Asian Consulting Data and Analytics leader at PwC, data is the heart of a business and leaders need to embed this thinking into their organisations.
“Using analytics and AI can add value to every part of the value chain and to every area of business decision-making.”
“For example, it can help organisations reduce machine downtime—therefore improve equipment efficiency and optimise the supply chain. Data analytics solutions can also enable increased profitability across the value chain, especially in FMCG and retail industries,” said Albin.
Barriers to applying data analytics
There is an evident gap between the need for insights from analytics and the capability to deliver those insights.
According to PwC’s 2017 Industry 4.0 survey report, business leaders are well aware of the importance of data analytics in decision-making processes.
|There is an evident gap between the need for insights from analytics and the capability to deliver those insights.|
However, 74 per cent of respondents do not have advanced data and analytics capabilities and only 14 per cent have a dedicated department for data analysis serving many functions across the company.
Meanwhile, the lack of skilled technical resources to manage the systems, high costs, and concerns over data and personal privacy are among the key obstacles holding back business leaders from successfully integrating analytics and artificial intelligence in their organisations.
Getting started with data analytics
Albin advised that organisations should embark on a data analytics journey which roughly comprises of four stages.
First, companies should assess the value which exists in their data and assure that the data can be trusted. Companies should focus on identifying the insights hidden in their data.
Second, companies need to prove that the insights can be turned into actionable changes and initiatives which have a clear benefit.
Third is to scale it so the insights from the data can be delivered to the right people at the right time.
Fourth is to repeat this process, since analytics can be applied to many different fields of an organisation.
“We have worked with many clients to embed data analytics in their way of working,” said Albin. “This transformation is by no means an easy task and it could take months and even years to get there. Yet there are many great tools and methodologies available to help businesses to get started on their journey to unlock the full power of data.”
Companies are also recommended to build a governance structure that enables them to develop and maintain necessary practices and capabilities to manage data more effectively.
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