At the recent MRS data-driven insights conference, industry leaders discussed what ‘data-driven insight’ means for businesses. Liz Lamb, head of data and insight at Card Factory, explains that she typically combines finance, data and insight to determine the potential ROI. 

Data-driven insights are based on genuine trust and confidence that this information will help a business make the right decisions in a turbulent time when marketing budgets are vital and ensure any investment made is effective. Understanding these insights means an organisation has the trust and confidence to make the right decisions.

Businesses are deciding to become more data-driven. For example, the media agency, The7Stars, took the step to merge a traditional research team with a traditional data expert team. These were two teams that stood apart and required time and effort to enable both to work together. Today, the teams collaborate on data and insights, democratising how data is discussed and helping them bring the two together.

Many businesses remain relatively fragmented when it comes to data and insights. Bringing together the potential of data analytics is challenging, it takes significant resources and budget to implement this process. 

Communication within a business remains paramount, especially to ensure all associated stakeholders understand and recognise the importance and value of data insights. 

Businesses working effectively with data insights will try to demystify the process and take them on the journey so they feel assured in any proposed methodology. A considerable amount of effort is applied to ensuring people understand what happens behind the scenes.

More businesses are implementing new technologies to generate more valued data-driven insights. While technology has enabled some automation, data professionals are still essential in determining how to use these tools properly and find the most valuable insights from the data provided. Data research has become a vital part of how we work today and how to deliver a strategy. 

The research stage has shifted to the start of the planning process rather than previously as an afterthought. Research and data insight has become critical in driving general business decision-making. 

The biggest shift in the research market is the rising application of more innovative tools, like AI-based solutions. Forecasts expect researchers will continue to feel more confident accessing these tools in their work in the future.

Data research is transforming from an added service to a valued strategic asset. Data professionals are considered problem-solvers, identifying opportunities and potential risks.

Finance as a function must ensure that any data applied is reliable and everyone has confidence in the information used. If data quality becomes a challenge, managing and assessing the overall strategy and associated investments will become a concern.

The digital transformation of finance requires a shift towards automating many processes with a move to cloud-focused enterprise resource planning (ERP) incorporating areas like accounting, HR and supply chain operations. Finance must consider the automation process and ensure it operates as expected.

The process of digitisation has resulted in a complete transformation of the traditional role and expectation of the finance function in many businesses. Finance activities can be automated to some extent, generating higher efficiency and better finance organisations. Digitalised finance processes can strengthen the quality of internal processes.

The finance function incorporates operation and financial data and applies advanced analytics and AI to enhance business decisions while working as a service provider for the entire business. It becomes a trusted partner to business teams, supporting them with the decision-making process. For many companies, this transformation is already happening and has enabled finance to integrate more effectively with the business and have more influence on the overall output.

Measures applied to evaluate a project are changing rapidly. The main focus of digital transformation has predominantly focused on refining the overall customer experience, so the entire business, including finance, must consider the end-to-end customer experience. Projects are evaluated on customer experience outlook, in addition to a financial performance metric.

New data sources aren’t the only changes to reporting and forecasting. Businesses are applying constant reporting and forecasting instead of annual budgeting, which can take time to complete. While financial reporting and planning has typically focused on internal metrics, businesses are exploring improvements in accuracy by harnessing external data measures such as customer behaviours. Finance as a function must monitor what added services and solutions are required and support the business with its data analytics and continuous planning and forecasting.

One of the main changes to the finance function is its critical role in the overall data ecosystem of a business. Finance as a function will define the controls around data, it will be responsible for ensuring an organisation has the necessary data and insights to ensure the business is performing and moving in the right direction.

Structured data from ERP and EPM solutions will typically support external reporting, financial results and management reporting. When combined with external, unstructured data, it supports making informed management decisions.

Robotic process automation (RPA) and Artificial Intelligence (AI) can automate processes, manage unstructured data and combine it with structured data.

Finance as a function needs to ensure data is reliable and people working with this information have confidence in using it. If data quality becomes concerning, managing and determining the business strategy and investments will be challenging. With so much data now available, it’s not possible to ensure all information is accurate and validated. Businesses must focus attention on data verification on information most vital to decision-making.
We all have a responsibility to create a clear roadmap and prepare ourselves for future developments. In the finance sector, this means committing to having the right people and technology available to take advantage of the next wave of progression.

Finance businesses are exploring how generative AI can support employees and customers with a range of text and numerical processes. Financial companies want to capture generative AI’s significant potential while managing the risks. In the finance industry, however, businesses are exploring support on the best way forward. The broad language models within generative AI have strengths with text-based generation, determining language and word patterns, but in the numerically based finance industry, does generative AI hold as much potential?

Despite the scale of generative AI in financial services, financial companies recognise the challenges involved. Most organisations discuss the risks associated with generative AI technology, which typically include data privacy, security and output accuracy. 

Another less discussed challenge is the requirement for the necessary storage infrastructure. To efficiently deploy both AI and generative AI, businesses must implement new storage capabilities to manage the large, real-time, unstructured data used to develop, train and implement generative AI. Without the necessary storage solutions, businesses will experience challenges such as latency that hinder and possibly stop generative AI deployment altogether. 

New storage solutions must be capable of managing data sets at speed and scale, which existing storage solutions cannot provide. AI-enabled infrastructure depends on innovative services like distributed storage, data compression, and data indexing. With the appropriate storage, businesses are perfectly positioned to support and accelerate generative AI.

Examples of use cases with adopting generative AI

Fraud detection and prevention – A core competency of generative AI is identifying patterns. In the finance industry, generative AI can support the recognition of anomalous transactions in real time, helping to determine and prevent fraudulent activities. For example, PayPal implemented a real-time data solution called Aerospike. The results included a 30x reduction in the number of missed fraud transactions and a 3x reduction in associated hardware costs. 

Regulatory Compliance – The finance world is heavily regulated, but generative AI can support the delivery of compliance reports. Through automating selected processes, like document verification and customer identity validation, generative AI can simplify processes like anti-money laundering and know-your-customer (KYC).

Financial Support – Generative AI can assist employees and their customers. It can help deliver a more bespoke financial analysis, including credit risks, credit score, budgeting and savings. 

Automation – Finance consists of multiple documents, countless contracts and account statements. Generative AI automates and streamline processes and repetitive tasks like data entry and reconciliation. 

Customer experience – In the finance world, applying generative AI-powered solutions can strengthen the customer experience. By providing constant support via chatbots, generative AI can be responsible for customer queries within a personalised portal.

Financial Services businesses are willing to embrace technological innovation. For years the industry has welcomed AI, and progress is accelerating thanks to generative AI solutions. The operational efficiencies and greater intelligence to support financial services employees and customers are distinctive benefits. 

In an industry used to proactively manage risk, generative AI will likely expand within the finance industry and support many positive transformations.

Innovation has become a driving force in today’s advancing business landscape. CFOs play a critical role in managing their organisation towards financial advancements, and their potential to drive innovation can be transformative.

One way CFOs can drive the change in innovation is by utilising the power of technology. The finance industry has experienced significant progression in recent years, including AI, big data and blockchain. These technologies can accelerate financial processes, reduce costs and enhance decision-making.

The finance industry has experienced a major transformation, supported by technological progression, changing customer demands and evolving regulatory conditions. Traditional financial groups face rising competition from emerging fintechs that are more agile and innovative. To remain competitive and in touch with new technologies, CFOs must embrace innovation as a core strategy.

One of the main ways CFOs can lead changes in innovation is by utilising new technologies. The finance industry has witnessed significant progression in new tech solutions, and CFOs should look to harness these opportunities to integrate these technologies into their finance services. For example, using AI-powered algorithms to assess financial data can strengthen operational efficiency. According to the latest Deloitte State of AI in the Enterprise report, nearly half of CFOs were skilled in their AI plans. Major businesses like American Express applied AI and Machine Learning to measure transaction data and determine possible fraudulent activities. This process strengthened security and improved the customer experience.

Innovation goes beyond new technologies. It’s about creating a culture that supports and rewards creativity. CFOs can lay the foundations by promoting conditions where individuals feel empowered to implement innovative concepts.

Leading by example: CFOs must engage in innovative strategies and show their commitment to change. When employees see their leaders driving innovation, they are more likely to follow the same path.

Encourage multi-collaboration: Collaboration within multiple teams can generate innovative ideas. CFOs should support this cross-communication, bringing together people with diverse skills and ideas to tackle financial challenges.

Providing Resources: CFOs should support the delivery of resources to support innovation plans. This process may involve allocating a portion of budgets towards innovation or investing in training projects to strengthen employee innovation skills.

Celebrate achievements and learn from mistakes: It’s vital to celebrate progression and achievements within a business. Similarly, it is equally important to support a culture where projects have failed and recognise it as an opportunity to learn and improve.

A recent study by Harvard Business Review found that businesses that promote innovation have a 25% higher employee satisfaction rate.

Utilising data analytics

Data is often considered the new currency in today’s business world. CFOs have access to abundant financial data, and by harnessing analytics, CFOs can create valuable insights that support innovation. By assessing historical financial data and using predictive analytics, CFOs can make vital decisions, identify trends and determine potential market changes. Additionally, CFOs can use data analytics to improve risk management, identify possible financial risks and deliver the necessary mitigation plans. This process protects a business and creates a culture of innovation by supporting data-focused decision-making. According to a report from PwC, organisations that apply data analytics are five times more likely to make faster decisions.

CFOs play a critical part in allocating financial resources for strategic innovation plans. This process involves assessing possible projects and ensuring they meet the strategic goals and financial capabilities. Implementing a Cost-Benefit Analysis will determine potential return on investment of innovation projects and explore the overall costs, benefits and possible associated risks.

CFOs must also consider prioritisating innovation projects, highlighting ones with the highest potential to accelerate revenue growth, enhance efficiency or improve customer experience.

It’s also critical to continue monitoring and measuring the overall performance of innovation projects after implementation. CFOs should regularly measure KPIs to ensure the projects deliver the expected results. CFOs should collaborate with other senior leaders to gain support and funding for innovation projects. A study by Accenture stated that over 60% of surveyed businesses intended to increase their investment plans in innovation in response to market disruption.

Innovation is no longer considered a luxury in the finance market but a necessity for continued growth and progress. CFOs have a critical role in managing the changes in innovation within their business. Embracing new technology and driving a culture of innovation, applying data and investing in creative initiatives, CFOs can encourage continued progress in the financial landscape. By doing this, they can provide financial stability and support long-term sustainability and competitiveness of their organisations.