09 Sep 2020
We know COVID-19 has caused the most massive economic disruption since the great wars and depression. It has forced a reshaping of the business world, one that will be ongoing.
There is a question then around how has the pandemic impacted data and analytics investment? And how should it?
According to the Global Big Data Analytics Market report from January 2020, global spending on big data analytics was more than US$180 billion in 2019 while it is expected the big data analytics market will grow at a compound annual growth rate (CAGR) of 12.3 per cent for the period from 2019 to 2027.
Since the pandemic started, many organisations, such as government, financial services and retail have more than ever realised the power and criticality of timely, accurate data and insight for key decision making.
"It is particularly important to have a pulse on customers’ financial wellbeing and behaviors in extremely uncertain times as multitudes of jobs are lost and consumer sentiment slumps."Business needs to rapidly equip itself with the right solutions to ramp up the support for their customers and respond to their changing needs. The pandemic has highlighted this is almost impossible without the power of data and analytics.
Furthermore, organisations have used data to understand what the changes in market, workplace and lifestyle mean for their business, risk appetite and employees. Retailers and banks in particular have been able to share aggregated data and insight with government authorities to help them make precise, informed decisions to minimise the damage of the pandemic to the economy. Mobility analysis and telecom data has helped with assessing effective execution of social distancing restrictions as well as flagging high risk suburbs.
According to the Retail and Consumer Goods Analytics Study, COVID-19 has had a significant impact on analytics usage among small and large companies with 52 per cent stating their analytic resources shifted or changed so they could “more quickly react” or “analyse faster”.
If it wasn’t already apparent enough, the pandemic has proved data analytics is a must-have capability for organisations. Furthermore, organisations who already had progressed with their data analytics and artificial intelligence (AI) maturity have realised the need to increase their investment, prioritise key analytics/AI use cases and accelerate their analytic/AI adoption to survive, stay relevant and prosper in the post-COVID era.
I believe two main areas that have received considerable uplift in data analytics adoption during the pandemic and will continue to grow in post-COVID era are digital analytics and automation. My focus is on the banking and financial sector but I believe most of the argument holds true for other industries as well.
For major banks and financial institutions with a strong branch network, who always had the privilege to interact with customers face to face everywhere across the country, COVID-19 lockdown restrictions have made a considerable difference in the way they operate and interact with customers through different channels.
Obviously, operations and customer interaction in branch networks has reduced while a lot more is happening through digital channels. In fact, the pandemic has led to a major boost in adopting digital channels even for the customer segment which previously preferred to do their banking at a branch.
Whether it is updating account information, transferring money or applying for a home or business loan, there is a lot more being done digitally today than ever before. According to a McKinsey & Co survey in the US, the banking industry has the highest digital adoption rate in the pandemic while in general there has been an uplift in digital adoption across many other industries as well. Meanwhile, 75 per cent of people using digital channels for the first time indicate they will continue to use them post-crisis.
Growing interaction with digital channels not only creates increased flow of data through these channels but also creates an opportunity to leverage analytics to understand customers’ interests and digital behaviors and continuously improve their digital experience.
It is also very important to have a single and consistent view of customers across all channels so services can be provided seamlessly regardless of the channel through which the customer interacts.
In addition, banks also have quickly developed digital solutions for services which were only offered at the branch network before. For instance, ANZ bank has successfully launched an electronic identity verification solution to reduce the need for home loan customers to visit a branch as part of their application process. This has not only brought convenience but is an example of putting customer safety first.
Examples like this are not only providing the service in a convenient manner but also generating additional data in digital format which can be used for enhancing the customer experience and potentially accelerating future interactions by cutting the need for re-capturing the same data. At the same time, there is a need to scale the backend fulfilment capability to keep up with the pace and efficiency that digital space is bringing along.
As interactions and service offerings through digital channels increase, the capacity to fulfill digital enquiries and services will also need to scale up accordingly. The pandemic has highlighted organisations can’t take the ability to mobilise their staff quickly enough for granted when they need to scale up to address a high volume of requests or operationally heavy and highly manual areas of the business. Hence, the only sustainable solution is to automate manual processes as much as possible.
For example, just consider a small part of the home loan application process: machine learning and artificial intelligence techniques can be used to categorise loan documents (such as pay slips, bank statements, etc), extract information from documents, compare and validate the information against the application and flag suspicious transactions or undisclosed debt to be considered for credit decision.
In addition, robotic process automation (RPA) can automate data re-keying and routine processing tasks, prescriptive analytics can be applied for resource allocation optimisation and property valuation can be semi-automated using advanced property price prediction approaches using machine learning. Various credit decisioning can be also enhanced and accelerated by utlising a 360 degree view of the customer and applying AI to augment the risk-based modeling.
The pandemic, with all the challenges it has inflicted on society, industry and the economy, has pressure-tested our assumptions and crystalised what fundamental capabilities are needed in the future in many areas.
For every organisation, it is the time to ask the hard and important questions: are we ready and equipped with the right data, analytics technology and processes, to not only stay relevant but thrive in the post-COVID era? And how much do we need to invest to get there?
This article is drawn from my appearance at a recent conference, the Data and Analytics Leaders Exchange
The views and opinions expressed in this communication are those of the author and may not necessarily state or reflect those of ANZ.
09 Sep 2020
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