Big data has been a trending term for some years and represents a significant potential that many businesses are only starting to appreciate. Years ago, most big data came from a range of sources such as CRMs, billing and browsing activity. Today, data science is more dependent on external data sources, generating information that could be obtained internally. 

Through the challenges faced in the past year, big data has become a pivotal part of recovery and growth across many sectors. According to a survey by Oxylabs of the UK finance industry, the majority of businesses have increased their data budgets in the past year or intend to make changes very soon. 

These patterns are similar across other industries, and while many organisations are managing budget cuts, the overall spending on big data continues to climb to remain ahead of the competition. In terms of competition, it will become increasingly challenging for businesses that are yet to integrate big data into their organisation. There are several key reasons why a company should focus on implementing big data.

Competitor Research: Big data takes competitor research to a higher level. A competitors website can provide only limited information on their values, products and pricing. However, all this information is complex to follow manually. Automated web scraping can be useful in these particular scenarios. Automation enables regular monitoring of many websites simultaneously. For example, in eCommerce, a business can explore how their competitors are changing prices, how long it takes to sell a product and the popularity of particular items. This information can make certain decisions about products and pricing strategies.

Precision Marketing: Data-driven marketing is more effective, as high-quality data allows businesses to discover new opportunities, to customise the message and target more effectively. While many have been using data for years, the pandemic spurred many to adapt their strategies. Global lockdowns resulted in a considerable surge in more data. It became particularly challenging for older data systems that weren’t capable of capturing the changes in customer behaviour. This is when external data became even more important. McKinsey refers to these changes as the big reset in data-driven marketing. Businesses that have adapted to the new normal and adjusted their data approach have experienced growth, while others have fallen behind the competition.

Supply Management: Data supports the management of your supply chain in various ways. It can support you in selecting the right products, developing your catalogue, predicting demand and improving the efficiency of other processes. Applying public data collection tools allows businesses to determine the popularity of particular products, assess reviews and other recommendations. 

Protecting your brand: The online world is full of alternative products that are based on successful branded products. Big data solutions can help identify instances where your brand name may be used illegally. Web scraping can determine fake online goods, scanning marketplaces and eCommerce sites to discover the best products. This process represents an essential part of business and will continue to be a challenge for many companies.

Improve the efficiency of business operations: If used appropriately, big data results in more efficient business processes. Data provides vital information on where to focus your efforts to generate the best results, enabling a business to reduce costs and resources.

A report by the MIT Sloan Management Review discovered that the most analytically focused businesses use a wide range of data sources. Often viewed as an added feature for larger businesses, applying big data has now become the new normal. Today, there is information everywhere that can help your business make more efficient decisions. Companies shouldn’t miss an opportunity to utilise this data within their daily operations.

The pandemic has increased the focus on the importance of data analytics in measuring risk, encouraging businesses to invest more in training and upskilling their employees.

We are in a time where information is critical. Data is generated faster than ever before, and more specifically, data analytics enables businesses to maintain pace with technological advancements in their chosen industry.

During the pandemic, many businesses transformed their focus from the potential risks associated with remote working to shifting their environment and risk framework by using data analytics to generate significant value. Implementing resource monitoring enables a smoother transition towards remote working and enables visualisation of the necessary KPIs that support the overall decision-making process.
In finance, data literacy isn’t an option anymore. It is a critical factor in maintaining pace with competitors, generating new insights and providing a better customer experience, as well as monitoring overall risk.

As market and technology progress, the business talent requirements expand too. Studies suggest that many businesses are now experiencing skill gaps or expect to face skill shortages in the years to come. A report by McKinsey states that 43% of those surveyed expect these talent gaps to be in data analytics. Upskilling individuals, especially in data analytics, is critical in supporting businesses with digital and data transformation plans.

Alongside the movement towards data analytics, the pandemic and shift towards remote working have created an opportunity for employers and employees to focus further on learning and development. Remote working during the pandemic has reduced the commute time and enabled people time to focus on other things such as advancing their skills. People have shown great interest in enhancing their analytical skills via online platforms.

The ICAEW Data Analytics certificate supports finance professionals in playing a vital role in data and business, connecting commercial skills and business knowledge with data analytics experience. There are many relevant courses available to support professionals with widening their skills in data and analytics.

Tailoring an upskilling strategy to fit the individual and the team supports the best learning experience employees can get from each course. By doing this, individuals with varying knowledge support each other and collaborate to develop a stronger data analytics culture.
The progress of analytics has enabled internal audit teams to deliver a more comprehensive picture of their business and operations. Not only does this improve the quality of results, but it also enhances overall communication with stakeholders and senior management.
The increased use of analytics will enable businesses to become more efficient, explore new data, identify new risks and ultimately gain a better understanding of their organisation. For many businesses, data literacy is no longer desirable, it is essential.

AI can leverage a bank’s biggest asset: its data. This can provide traditional finance businesses with a new source of potential income.

It’s clear today and technology matters in the finance industry. The new emerging fintech displays the power of integrating technology with finance. It’s understood that some of the leading businesses such as Monzo and Revolut have succeeded in securing large numbers of customers predominantly because they were one of the first to automate the process of creating a bank account, replacing the traditional time-consuming way of setting up an account.

An automated process like this involves managing data, and as this becomes more advanced, it is often referred to as artificial intelligence (AI). Chatbots represent one of the most common forms of visible AI in finance. WeBank of China reports that nearly 98% of all customer enquiries can be managed via chatbots. Aside from the overall customer experience, AI can enhance finance systems, reduce costs and improve overall margins.

Data represents the biggest factor for conventional businesses to com[pete against fintech. Incumbents are gradually transforming in terms of data and digital technology. Their size and availability of resources provide traditional finance with a significant advantage over fintech and can allow them to catch up relatively fast.

Traditional finance businesses are investing rapidly in AI solutions, with banking scoring the highest of any industry for adopting AI, based on a recent study by GlobalData. The data incumbent finance businesses have gathered through their long years of building a customer base enables a relatively quick closing of the gap if applied with an AI strategy. Once this gap with fintech is closed, the new businesses may not have as clear a competitive edge as before. The Financial Times recently stated that the current performance of fintech banks during the pandemic suggests the concept that leading fintech companies can do anything conventional businesses can do better is diminishing. While fintech has had the initial advantage in terms of technology, it will need to continue innovating and enhance its product offering beyond its existing basic features.

Industry experts believe there is better technology available than apps. The digital-only platform, MyBank provides an example of how AI can generate new options for finance. By 2019 MyBank had launched the 3-1-0 model, a business loan that takes under three minutes to apply and less than a second to approve, with no human intervention required. When used in the right way, AI can reduce the time taken to make a loan approval and at the same time, ensure loans are more effective by lowering the non-performing loan ratios. Other businesses have applied their historical data from existing customers to develop a predictive model and determine the key variables that account for certain factors like missed repayments. Implementing this kind of process is not possible for new banks that lack past information.

Protecting finance data with AI

The more data acquired, the more responsibility you have. Finance data consists of some of the most private and sensitive information. It is therefore critical finance controls this data and AI delivers another layer of protection against potential cyber-attacks.

Several finance services businesses have incorporated machine learning into their security systems. Some have struggled to combat advanced cyber attacks with groups with access to their ML technology and managing their fraud detection rates, with high levels of false-positive alerts daily. Controlling false positives in financial security is a significant issue. Monzo, for example, has come under scrutiny for blocking customer accounts for extended periods because automated software has detected signs of potential criminal activity, and they lack the human staff to manage the backlog.

AI and deep learning systems have reduced this level of false positives and the overall level of fraud detection. These improvements have enabled the finance industry to focus more time on potential fraud, improving its security and enhancing the overall customer experience.

While there may be challenges and concerns with automation, the positives of giving more time to employees due to AI is valuable. In the scenario mentioned, fewer employees focusing on false positives means more satisfied customers and additional staff managing actual cases of fraud.

Whether referred to as fintech or banking, the case of managing money focuses on people and data. If data is handled effectively, people can create accounts, deposit and spend their money easily. When people apply for a loan, the process will determine that the right people are approved, and others declined, and there is transparency for both sides to understand their results.

The most effective data processes available today predominantly include AI technology, and this is the case for the finance industry.

The demand for skilled leaders in finance has only intensified further since the pandemic. Businesses are seeking more diverse opportunities and require talent with the knowledge and insight of digital transformation. Being capable of integrating remote workers has become a top priority for senior recruitment leaders.

The concept of today’s finance leader has shifted considerably with the rising focus on customers, the increase in digital, investor and regulatory measures. The pandemic has accelerated the need for top talent that can enhance and support businesses through difficult times. Candidates with diverse skills, particularly in digital and transformation are critical right now. For most businesses, future success will be dependent on a business ability to coordinate with remote workers.

The market is highly competitive for leading talent in the finance industry. Recruitment leaders have experienced a rise in urgency to find diverse talent in mid and senior-level roles within financial services. The pandemic has accelerated new trends, particularly around DEI and moving towards a more flexible working environment. While we experienced numerous challenges during 2020, the sector has gradually rebounded with strength and continues to be progressing further throughout 2021.

On the whole, financial services businesses have adapted quickly to remote work through the pandemic. Finance leaders have learned the best methods of managing a remote workforce and the importance of engagement and focus with their colleagues. The main challenge for the future will be integrating flexible working into a business for the long term. Now employees have experienced the benefits of working remotely, many wish to continue working like this and have higher expectations, in terms of hybrid and flexible working options. Employers that fail to offer flexible options will likely lose their employees to other businesses that prioritise these offerings to their workforce.

The rise of digital and transformation skills

There is an increasing demand for senior finance leaders with expertise in digital and transformation projects. Because of the pandemic, finance leaders are under further pressure to respond quickly and navigate their business towards more stability and resilience. Before the pandemic, many experienced a significant change in the finance sector due to digitisation. Traditional banks were pushed by emerging fintech to quickly transform their structure, services and their rate of innovation. Some have been effective in moving towards a more digitally-focused organisation, but others have struggled due to a lack of investment will potentially find it challenging to progress in the face of other more competitive and agile businesses.

More technology businesses are inevitably likely to enter the financial market. Fintech innovation will also continue to expand in the coming years. There is a growing activity with payments and open banking where traditional businesses invest heavily into these areas to maintain pace with emerging companies.

As we move into a post COVID world, the job market for senior leaders looks to be very strong. The demand for executive leaders was relatively high in 2020, but many businesses were cautious. The barrier in face-to-face contact and high levels of uncertainty about the future put many positions on pause. In 2021, the market began to clear and led to a significant rise in hiring. The delays experienced in 2020, combined with a strong economy, have resulted in a very prosperous job market.

The flexibility to work remotely has enabled candidates to consider more opportunities, resulting in a talent-driven market. The traditional idea of relocating to a business headquarters and travelling long distances has been replaced with a new era of home base work and travel when required.

When discussing the market with consultants, the industry has become very competitive and has shifted towards a predominantly candidate-driven market. Many businesses that traditionally focus on financial services have expanded into other financial technology, such as payment systems and cryptocurrency due to the surge of investment and advancement in these markets.

The challenges over the past year have placed even more focus on the position of the chief financial officer. The pandemic requires companies to have a dedicated and skilled leader at the CFO level capable of managing the challenges that emerged from the pandemic. It tested businesses in whether they had the necessary processes to accurately measure and have systems that generate insights into management and operations. 

As finance businesses continue to evolve their services and offerings, a new approach towards data and analytics is needed to meet rising demands.

Big data and analytics are important elements of today’s finance industry and go in collaboration with the challenge in improving the capabilities of data management for finance businesses. The key question is how do data analytical tools and technologies add further value to the financial services industry?

New data-focused services can improve revenues and overall costs reduced, improving competitiveness. Security can provide customers with enhanced and safer services. Successful AI-focused businesses target their service to the new era of tech-savvy millennials. 

Emerging fintech is unravelling the power of big data to determine customer behaviour and create structured risk assessments, which can set them apart from other established financial institutions. The speed of real-time data enables disruptive fintech and challenger banks the ability to adapt to a rapidly changing industry, along with a more comprehensive understanding of customer relationships.

Customer-focused analytics has become a key priority. This is a significant transformation from the past when the financial industry was mainly product-focused. Data insights, systems and operations are focused on the customer. As a result, it’s critical to understand how to determine changing markets and customer requirements.

Risk management has improved and digitisation has established the way for automation by increasing agility and innovation, as well as generating revenue for data. Data analytics has also made regulatory compliance simpler by establishing a platform for a business and enabling real-time frameworks with regulators. Realising the value of big data requires an analytical perspective as this support transforming data into valuable insights.

This is where a data analyst comes into play. Big data analysts recognise this process and what information to look for that will translate into value and enhanced customer satisfaction.

Rates of adoption for business intelligence platforms are rising as more businesses integrate with big data, seeking to enhance findings from large data sets. Digitisation in finance has allowed disruptive technologies such as advanced data analytics, AI, machine learning, big data to transform how fintech can compete in the market. Data analytical tools have become more sophisticated and so are more necessary to businesses today.

As the finance industry works towards a data-driven priority, businesses need to react to these changes in a structured manner. Those looking to remain competitive must be willing to adapt and understand how to use data skills on the job.

The finance industry is actively looking to harness the insights from several data sources but existing limitations within their IT infrastructure. A new industry standard intends to make it easier for financial services businesses to manage data in the cloud, opening the potential to a surge of new service and product development.

Cloud services facilitate the technical efforts of sharing data by moving the responsibility for developing, securing and maintaining information to cloud-managed service providers. Up until now, the finance industry has had very few ways to verify that cloud providers can meet their strict requirements in terms of governance and security. The new CDMC standard, announced by the EDM Council, focuses specifically on these concerns.

The CDMC delivers a set of measures to ensure cloud environments meet the security and governance required for regulated industries like finance. The standard was developed by a working group in collaboration with Morgan Stanley, Refinitiv and over 20 financial institution leaders and cloud providers. The standard goes beyond data sharing and explores the needs of financial services businesses as they shift their operations into the cloud. Data remains an important area where finance businesses can improve their response to customer requirements. A standard that supports their transition to the cloud will only enhance their capacity to digest and share data with a wide range of customers.
Traditionally, data feeds going in and out have typically been custom created and managed individually. Finance businesses are under growing pressure to innovate and deliver enhanced services due to the growing number of new fintech companies, but they are attempting this with certain limitations.

Cloud data platforms handle this challenge by managing the technical complications of sharing and securing data within a business. Cloud providers must display their following to the new standard via an independent group.

CDMC presents several opportunities by accelerating the rate finance businesses can transfer their operations to the cloud. A vital result of this is the potential to apply financial data from various silos, enabling companies to create new revenue streams through innovative products and work together across the new data economy. Multiple data sources translate into easier access and sharing of data between teams and the wider business. Aside from that, it enables the entire industry to be more connected and share data in innovative ways with customers and their partners.

Tackling fraud is a particular challenge, especially as every financially related business is trying to combat this problem by using their data. With the potential to utilise data from multiple sources and the ability to explore activity in banks and payment processors, there’s a lot more potential to identify and tackle fraud.

This model has worked for the security industry, where trusted providers explore activity across participating businesses to detect threats. With a structured system enabling financial data to share in the cloud, a new wave of service providers can handle the pressure of detecting fraud for financial businesses and likely do it more effectively than before. This system requires considerable collaboration, but the CDMC has created the foundations of trust for these services to be delivered.

Another area where data sharing in the cloud can play a significant role is in ESG. Businesses that manage pensions and sovereign wealth funds are under growing pressure to ensure investments meet ethical and environmental criteria that are constantly requested by investors and regulators.

Defining whether a fund has a positive environmental score or performs business ethically can be challenging to determine. Third-party providers are capable of consolidating this information and effectively creating an authoritative voice for the industry. The CDMC standard applies a similar baseline of trust to ensure shared data services are available.

These are examples of how data sharing in the cloud can enable finance businesses to transfer their focus from developing varied IT services towards an innovative system that focuses on progress and remaining competitive. There is a range of third party data providers available in the cloud that can improve predictive analytics and generate a more personalised customer service.

Combining this data can enable finance businesses to improve their predictive analytics and deliver a more bespoke service to their customers, but this requires a relatively seamless data process. Managed cloud services simplify this process, and the CDMC represents a vital step in delivering a more efficient cloud migration system for the finance industry.