Data – are you maximising one of your most valuable assets?

Author: Jennifer Ho, Global Data & Analytics Risk Assurance Leader, PwC Jennifer Ho_9908

Decision-makers today are faced with an ever-evolving inventory of risks and there is an increasing need for better data information so organisations can take action.  But, what data is needed to do a better job at identifying emerging risks and how can business leaders tackle this head on?

The results of our 20th Annual Global CEO survey tell us that CEOs are ploughing investment into innovation – and are focusing on boosting talent in the digital and technology space to capitalise on new opportunities. The next generation of cognitive computing and programming applications will fundamentally transform the ability of users to correlate and connect data in ways never before imagined.  With big data, cloud computing, governance, compliance and Artificial Intelligence, risk managers will be able to gain advantages from capturing, extracting and transforming data to perform risk assessments, stress tests and risk scenario analyses.

The need for good data has never been greater

Companies in all sectors are seeking consistent, credible and trustworthy data to make faster and well-informed decisions.  The demands of big data and data analytics are creating a world where data needs to be shared in new ways.   As a result, data is one of the most valuable assets a company possesses.  Yet many companies fail to unlock the full value of their data because they manage it in a fragmented fashion.  Often, organisations lack a standardised and coordinated approach to data governance, management and analytics.

Future risk technologies will use this evolution in computing to enhance the automation of traditionally controlled activities, such as the financial statement audit. This will form the basis for effective information, communication, monitoring and related processes - critical for an organisation to effectively manage risk management.

Advanced technological platforms that can address complex situations are currently characterised by ambiguity and uncertainty. Cognitive capabilities including data mining, language processing and machine learning are supplementing traditional analytics and being applied against massive data sets to help find indicators of known and unknown risks.


What’s in it for executives? 

Many companies are getting into the action on big data with existing resources and making the investments in technological platforms, business intelligence, data mining and others.  But the essential first step is to think about the critical business issues first.  For example, is it to improve product profitability, customer segmentation/analysis, supply chain optimisation, financial performance management, workforce performance and alignment, or for regulatory compliance?  Whilst big data means different things to different people in a company, the focus of big data should be all about making faster, smarter and more confident business decisions.   

There is an infinite amount of data out there, but organisations often suffer from time and resource deficits to perform comprehensive data analytics. Therefore, once the executive team agrees on the critical business issues, then – and only then – should they consider and analyse the data sources that will be most appropriate to help them solve these key issues.  This focused approach to data analytics should help to get better actionable insights.

Jennifer is the Global Data & Analytics Risk Assurance Leader based in Hong Kong. She also leads PwC China's Data and Analytics team. She has extensive experience in providing data analytics to clients to enhance their management and compliance reporting; improve business processes and internal controls; and gain operational efficiencies. She graduated from University of Waterloo in Canada with a first class honours degree in Master of Accounting and is a member of the CICA and HKICPA. She is also a CISA and CRISC and has attended Executive Leadership program at INSEAD Business School.


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