As CFOs explore ways to tackle the impact of inflation on margins, self-service analytics will be essential in driving employee productivity, according to a Gartner report.
In December 2021, Gartner surveyed 400 finance leaders and discovered that over 50% saw self-service data and analytics as an essential driver of employee productivity. At least one in four saw it as vital for increasing business speed and agility. Self-service data analytics refers to technology and processes that finance users leverage with minimal involvement from IT departments.
Alex Bant, chief of research in Gartner Finance, explains that two out of three finance leaders have raised their prices in response to inflation. Finding ways to improve productivity and efficiency rather than passing on inflationary costs to customers can create a critical long-term competitive advantage.
Advanced data and analytics and AI technologies generating high value and where investment is forecast to rise to include self-service data analytics, automated machine learning, cloud analytics, big data analytics and predictive analytics.
Predictive analytics predicts a series of outcomes overtime or the distribution of an outcome that could occur for a specific event, using techniques like driver-based forecasting, time-series forecasting and simulation. Predictive analytics is one of the most popular use cases for finance executives automating their forecasting processes.
Bant explains that over 90% of finance leaders have increased their digital ambitions for 2022, but the same proportion is concerned about whether this development can continue due to slower growth, higher rates and the added pressure on profitability. Investment into the digital area, even as growth declines with be vital in determining the successful businesses of the future.
Big data and predictive analytics are considered critical technologies for generating higher revenue through improving products or services. Machine Learning and cloud analytics were viewed as the best solutions to improve cost efficiency.
The upcoming Gartner CFO and Finance Executive Conference will provide insights on the issues facing CFOs on June the 6th. The conference will deliver actionable insights for CFOs and their teams to support them on their digital journey and understand what makes a team successful. The Gartner Finance practice supports finance leaders meet their top priorities and delivering on vital initiatives that spread across finance and generate business impact.
The simplicity of integrating and the option to pay as you go are just two factors that have encouraged the accelerated adoption of SaaS products. While there are concerns regarding security, data privacy and limitations to customising software, the underlying benefits of scaling and cost have led to large-scale adoption. The software products marketplace typically comprises two sections: Companies which began on-premise but have transitioned to mainly SaaS but retained an on-premise option for customers preferring this method. The second concerns newer businesses that are predominantly SaaS. This format began with companies like Salesforce and quickly became a preferred choice for traditional on-premises CRM software. There are also an increasingly large number of tech-focused companies essentially working as SaaS businesses. Companies in the consumer service industry like trip advisor, or companies in the finance scene, such as Stripe, are examples of this. There is an emerging talent war within the SaaS space, with traditional enterprise software companies that have moved to SaaS are now competing for top talent with leading businesses like Salesforce, and Workday, who are similarly competing with the large internet companies like Amazon and Google.
Technical and executive positions at technology companies have never been in such high demand. This is likely to rise as businesses embrace automation, AI, machine learning and data intelligence. For software product companies, people capabilities are accelerating quickly as businesses and operating systems utilise cloud and SaaS services. Today’s software leaders need to work with more innovation and be capable of making quick real-time decisions, determining which products to use and improving what is working. A structured talent and engagement strategy is critical for software companies to attract the best professionals within the current industry. There are various sources to search, including new and pure-focused SaaS businesses, tech-enabled businesses, transitioned software companies and consulting firms. The booming industry environment has made it considerably more challenging to attract executives to a new platform, mainly due to the success in their existing roles. Generating interest from a candidate means the opportunity has to stand out, be captivating and specifically targeted at each individual.
The recruitment challenge is particularly challenging for businesses in a transition phase, than those focusing on pure growth. For companies trying to recover and move on from legacy technologies, hiring a leading professional is critical to enable them to attract other high-level candidates to their business.
The SaaS industry is growing at a considerable pace. Software service providers and tech-focused businesses compete for the same leaders and skillsets. Hiring managers and their search partners need to understand the software industry and where successful leaders are working and remain agile to how the industry is progressing. Creating the most compelling proposition to attract the best industry talent is critical.
A hybrid multi-cloud approach is emerging as the preferred IT platform for implementing a business-focused strategy.
The priority of delivering a successful hybrid IT strategy is ensuring that it aligns with your business needs. This plan involves determining the most suitable on-premise systems and combining these with the most effective software-as-a-service (SaaS) applications available that meet the requirements of your organisation.
Adopting the best available SaaS technologies needn’t mean eliminating all non-cloud systems. Many businesses need these installed systems to support any plans for further innovation and digital transformation. Other challenging, resource-focused services may not be cloud-ready. These conditions require a hybrid approach, which enables cloud and non-cloud systems to work together so companies can operate various applications in non-cloud conditions while adopting cheaper and more efficient SaaS technology services.
Another vital consideration for creating the best hybrid multi-cloud strategy is ensuring the correct infrastructure model is applied when moving from legacy systems to a blend of SaaS-focused models.
The costs associated with maintaining in-house systems must consider the operational costs of the buildings as well as the opportunity cost that comes with datacentre infrastructure. This is especially true when expanding a business or utilising new cloud technologies to accelerate transformation.
It’s important to take note of the possible inefficiencies that can occur with a data centre when factoring in these costs. Studies suggest that data centres can waste up to 90% of the energy used from the energy grid. Switching to a cloud-first datacentre model enables a business to take advantage of the economies of scale. It reduces a portion of that waste and can enable more efficient IT technology expenditures and allow for a quicker transition with the best available SaaS technologies.
Combining a hybrid IT strategy with a structured implementation plan allows infrastructure to be scaled, improves enterprise agility and enhances transformation by using a good mix of on-premise and SaaS technologies.
Innovative technologies have made financial processes more efficient, reduced the number of errors and transformed how customers view and interact with their money. In terms of product development, measuring customer habits can generate better decisions, and businesses can create structured plans based on real-time API metrics and data to respond effectively to any changes in the market.
With the integration of additional SaaS-based applications and other services by more financial businesses, the opportunity for risk continues to increase too. The importance of data quality and security will only rise in a world where cyber-attacks become more prevalent, and a lack of compliance can be significantly detrimental to an organisation. API technology provides opportunities to innovate in the financial industry, enabling organisations a competitive advantage.
Businesses today are established on data, while finance-related organisations are established on customer trust. If the security of a customer’s money or information is compromised, the trust with the business is lost. An organisation must have 100% security of the data used in developing and implementing an API tool that needs that data to function.
Open banking services have given customers greater control over their finances. People can move and manage their money with greater security from stricter regulations. Open banking platforms are accessible from many devices, making them a simple and popular option for many individuals. The customer experience has improved significantly through open banking and provided several benefits, such as more affordable payment options and smart banking services.
Finance businesses can provide customised products and customer service based on intelligent algorithms that can predict the needs and behaviours of their customers. Consequently, any transactional data quality is dependent on how this data is initially structured. With such a variety of data collected, this can become challenging for data cleansing. Data teams will spend more time applying potentially ‘faulty’ data for analysis. It’s likely that valuable information could be lost or not considered, resulting in the need for more efficient data preparation.
Utilising Real-Time Decision-Making
The instant message-based transaction generated from APIs benefits users at all levels. These systems generate quick, standardised information based on preselected details encoded into the API. This standard structure results in less uncertainty for individuals and a more efficient test design that enables professionals to explore data quality issues.
How Open Banking APIs influence data quality
Protecting outbound data
Poor data within the API will result in operational and transactional problems by impacting reporting quality, searches and analytics. Financial professionals or anyone in a customer-focused position may have to search for specific customer accounts and, in the process, find duplicated data for the same individual. Duplicated records in online financial systems can cause discrepancies and take up unnecessary space, resulting in time and costly implications for a business. In some cases, internal reports could be on incorrect data and potentially be damaging when making strategic decisions on products or other services based on inaccurate data.
API Security Considerations
Systems may be at higher risk of malfunctioning when poor data passes through an API. Quality checks must test against data structures and avoid any potential security breaches. While many cases of API security problems are unintentional, there are intentional cyber-attacks which can leave business systems inactive for long periods.
When APIs are exposed to external systems, it’s critical that security and data measures are in place and data professionals can manage all data types. Centralised services in the finance industry committed to data quality management must collaborate with the relevant regulatory and legislative groups to understand and manage these processes.
While defining the problem of data quality in open banking is relatively simple, the solution can be challenging and ignoring the issue can result in considerable problems for a business. The rapid development of technology has made it challenging for organisations to handle customer data. Technology is one of the least regulated markets, while finance and banking are the most heavily regulated industries, and data quality is critical to security.
Any customer data shared with third-party groups via an API will always pose a possible risk. If any of this information is inaccurate, the risk of exposure will rise further. Managing this risk involves having data quality solutions that ensure the customer data collected from open banking APIs are complete and correct.
Data quality assurance within finance must incorporate data governance measures and include a centre of data quality excellence. Establishing these frameworks and plans for data quality standards in open banking data ensures these problems can be solved.
The job of a senior finance leader has never been more demanding than it is now. Today, people are consistently searching online for new products and solutions, watching new media, making purchases and sharing content and experiences on social platforms. This significant transition towards digital behaviour means the customer experience has become a top priority for businesses.
A positive customer experience is vital to enable sustained growth in business. It promotes loyalty, retains customers and enhances brand reputation. CEOs of international banks and finance companies are focused on managing volatile economic conditions by eliminating any hurdles that may influence profit margins. They understand that growing a sustainable and profitable business requires investment in digital and analytics. The majority understand that their business must adapt to remain competitive in the future but recognise that this change will inevitably require some investment. The challenge is transforming to this new model without incurring high costs.
Determining the ideal customer experience requires recognising the diverse competition, understanding the main KPIs that drive success and creating a clear strategy. Businesses are exploring new technologies that provide smart experiences for future generations.
A new wave of businesses that apply data and technology as the core of the user experience is rising. In the financial services industry, online payment platforms are launching seamless check-out and payment options that enable them to take a more dominant role in the finance world. During this process, businesses gather significant behaviour data. Based on this asset, companies have progressed into other markets such as lending and online retail, rivalling traditional bank payment services at an accelerated pace.
What senior leaders should be aware of is that smarter investment decisions to improve the customer experience and their profits are often connected to the development or use of other platforms. Platforms are generating efficient data-focused processes that enhance the customer experience and the overall costs. Implementing this process, however, can be challenging and requires a focus from a user experience perspective with support to avoid any major costs.
To increase growth plans, business leaders are exploring the ecosystem business model in platforms, offering an interconnected set of services where users can utilise differing options in a singular experience. According to a study by McKinsey, customers are supporting this shift, with over 70% saying they are open to integrated ecosystems.
As markets converge, there is an appeal to provide everything for everyone. In reality, senior leaders must focus on their core capabilities and not get too carried away with their offerings. A major challenge that appears with shifting to an embedded eco-system platform is to determine the focus of a business and what capabilities it requires within the organisation.
With the move to ecosystem platforms, lots of new data-focused opportunities and challenges are emerging. Businesses are utilising AI and big data to generate new insights and information. It’s not a simple process, but if done correctly this information can enhance predictive accuracy and generate valuable information for a business. Business leaders who successfully transform their company towards an eco-system platform often understand the power of AI and data in driving efficient customer experiences and increasing profitability. It requires thinking innovatively, being smart and having the confidence to adapt when necessary.
Embedded finance is rapidly expanding, particularly in recent years. Google reports that searches for embedded finance have spiked in the last two years, partly due to funding for startups introducing new embedded finance solutions and big banks wanting to explore this new market.
Interest in embedded finance is increasing for various reasons. Juniper Research predicts that the market will exceed $138 billion by 2026. Another reason for this growth is that the pandemic has encouraged businesses to rethink their offerings and explore new fintech solutions. While there are opportunities, the development of embedded finance comes with new challenges, particularly concerning data privacy and complying with current regulations.
If a non-financial organisation provides a financial service that enables a seamless customer journey and no platform movement, it is considered an embedded finance solution. The concept is closely related to open banking. British and EU regulations mean big banks and other financial institutions must share consented customer data. Smaller businesses can utilise these data feeds, and as a result, it empowers startups to offer better financial services.
While there are talks in the fintech industry about how successful open banking has been, several startups have tapped into this market over the last few years. Embedded finance expands on the idea of open banking, expanding the service beyond fintech businesses.
Reports from BaaS provider, Vodeno suggest that over 50% of retailers and eCommerce companies in Europe will increase their services or plan to start offering embedded finance solutions in the coming year. Innovation in customer experience is the primary driving factor behind developing embedded finance.
Traditional retailers and eCommerce companies recognise that their customers expect a seamless shopping experience. Processes like taking users to an external payment portal are not acceptable anymore.
The challenge, however, is these solutions can be costly, and many smaller companies cannot purchase their financial solution. Most companies utilise fintech startups for these solutions. Most traditional financial interactions were often controlled by banks. Today banking-as-a-service (BaaS) means all businesses have access to innovative embedded financial solutions that are cost-effective and simple to integrate.
However, there is growing competition from larger industry businesses attempting to capitalise on the embedded finance industry. Big companies like JP Morgan intend to use some of their tech investment budget towards developing embedded financial services. Rival business Goldman Sachs recently announced its banking-as-a-service portal for developers.
Barclays recently announced its Rise Start-Up Academy, a digital skills programme for fintech professionals. The first project for developing new ventures focuses on embedded finance. The pandemic has increased the adoption of embedded finance solutions.
While startups have dominated the industry, incumbent businesses are starting to explore the market. Lower interest rates have made it more difficult for big banks to compete on price. A further factor relates to the global health crisis and how this has changed opinions toward alternative financial service providers.
While there have been significant changes, reports suggest that the average customer remains reluctant to shift their money from traditional institutions. It’s becoming clear that non-traditional financial service providers are acquiring the trust and attention of customers. In response, larger businesses need to be more creative with their products and services. The result has been increased availability of new services, including embedded finance solutions.
Embedded finance enables established financial organisations to reach out to existing clients via different channels. There is a rising demand for embedded finance solutions. Businesses looking to introduce these services have several challenges before moving forward with this option.
New data initiatives intend to deliver transparency in climate reporting.
Recently, the US Securities and Exchange Commission announced a draft measure requiring US-listed businesses to disclose climate-related risks and their greenhouse gas emissions. The ruling was announced in response to investors requesting further guidance on how to report on climate-related impacts. Some have criticised the plan believing it is creating an added burden for businesses disclosing information.
The announcement represents a significant step in financial climate reporting. Policies and regulations greatly vary from nation to nation, but the US has been lagging behind Europe in requiring companies to disclose data on their climate-related impacts. The US has preferred a market-driven approach towards reporting and disclosures, while Europe has created a taxonomy with guidance on what specifically is an environmentally sustainable activity. The differing approaches toward regulations and frameworks result in fragmentation and uncertainty for financial markets. Industry experts are urging more collaboration, and the use of a common language to support the financial industry in disclosing climate-related information.
A recent report called ‘Forging the path to international standards in sustainable finance’ explores these issues in detail. The mentioned report will launch at a virtual roundtable with financial industry leaders examining the alignment of international markets and sustainability standards.
Back in 2015, the Financial Stability Boards created the Task Force on Climate-related Financial Disclosures, providing a framework for businesses to disclose climate-related risks and opportunities. The TCFD measures have become mandatory in certain areas, and many plan to follow a similar path.
Pedro Guazo, a representative of the UN Joint Staff Pension Fund, is addressing the challenges and risks created by climate change in its investment activities. Guazo explains that the fund wants to ensure its stakeholders have complete transparency in the processes, scenarios and metrics used to integrate climate-related risks and opportunities into their investment process.
Adam Banai, executive director of the Magya Nemzeti Bank of Hungary, one of the first central banks to publish a TCFD report, emphasises the important role central banks play in encouraging the adoption of TCFD recommendations. Banai admits that financial markets in his region aren’t ready to shift towards a more sustainable way of working and believes the bank must lead the way in delivering the support and guidance needed to improve this transition.
Banaii believes that a key reason why markets aren’t willing to integrate environmental, social and governance factors into investment decisions is because of data. Data has posed the main challenge when compiling reports, especially in finance and the scope of business portfolios. The more complex and varied a portfolio, the more difficult it is to find relevant and comparable data from other businesses.
While this is difficult for larger businesses, it is even more challenging for smaller enterprises that usually lack access to resources to collect and measure data. Banai believes that with time more financial organisations will collect and share data because continued rising demand will lead to more data production and collection.
The TCFD focuses on standardising approaches toward climate-related financial reporting. The more areas that adopt its recommendations and more businesses producing TCFD reports, the more realistic it will become that we can reach net-zero. However, until relevant data becomes more available and accessible, the challenges to adopting TCFD on a large scale will continue.
Skills shortages mean that it will become challenging to fill future emerging opportunities in the data industry.
Business data continues to rise in scale and spread across more platforms, but data literacy skills struggle to maintain pace. Most companies have the equivalent of an average of six platforms of data. While leaders are reasonably assured that their teams can analyse data, many employees are not as confident. New research concerning data literacy and management highlighted this pattern in several businesses worldwide.
The 2022 State of the Data and What’s Next report from Red Hat and Starburst explore how businesses gather and manage their data. Data Literacy: The Upskilling Revolution explored what skills people require to deliver data-focused strategies compared to the viewpoint of senior members.
The data literacy report from Qlik presented several predictions concerning how data-driven work will transform leadership teams over the next few years. Nearly all of the leaders surveyed stated that they intend to create and hire for these new data-focused roles within their organisation over the coming decade.
People are demanding data literacy training, but as in many work situations, there is a disconnect between senior leaders and their employees. C-level executives believe that over half of employees are data literate, while only 11% of employees agree with this statement. Furthermore, over 50% of senior leaders are confident in their data literacy, but 45% rely on their instincts rather than data to make critical decisions.
Employees are actively looking to improve their data skills, but the report suggests that only 27% have had formal training with practical experience. Individuals in customer service, finance, marketing and sales stated the need for data literacy exceeds the amount of training available today. People are also concerned that businesses fail to see the responsibility of supporting their teams with developing these skills. According to the survey, senior leaders tend to allocate training opportunities for people working specifically in data-focused roles but fail to recognise people working in other general fields.
This way of thinking can cause certain business areas to fall far behind, like HR and Procurement. This method can also create a decline in enterprise value, with the report suggesting that companies with higher data literacy skills can achieve a considerably higher value for their organisation. The report describes the efforts at PwC UK, which trained 17,000 of its 24,000 employees in data literacy and advocated a shift in the overall mindset of data.
The latest data report from Red Hat and Starburst indicates the scale of the ongoing learning challenge with data. Businesses have an average of 4 to 6 data platforms and up to 12 individual data systems. The complexity of systems increases as an organisation spreads its data over various platforms, and the risk of security heightens too. Respondents to the survey highlighted the automation of IT and data operations as the top priority to enable data systems to work together. Aside from the increase in data spreading across more platforms, the volume of information has increased rapidly.
The lack of quality data and automated processes can hinder the rate of transformation at many businesses. Studies suggest that the potential for improved IT-finance collaboration is very under-explored. It’s important considering how much financial data supports the development of a positive strategy.
Jason Child, the CFO of Saas business Splunk, describes his time at Amazon within the Financial Planning & Analysis department. In 1999, his team performed a cost-benefit analysis of the free shipping system, a key driver of significant growth at Splunk. They compared free shipping to a 10% discount on each order and quickly found that free shipping generated far more business.
A focus group explored the feasibility of this idea with their CEO and created the concept of a 5-day delay on free shipping, separating it from those that pay for shipping. It resulted in the development of Amazon Prime, which has close to 200 million members, each paying $13 a month. This process is an example of data-focused financial analysis (FP&A), and the potential it has in transforming a value proposition, operational model or even the entire business plan.
Like most data-driven processes, FP&A is influenced by reporting, control and compliance. Inefficient data processes and inadequate financial reporting results in costs in the region of $7.8 billion every year, according to a study by DataRails.
One of the most common problems finance teams face is the overall quality and reliability of their available data. While they may have access to accurate information, the data is vulnerable to inaccuracies due to being shared with other members over time and analysed by multiple teams. Often, data is shared internally via manual copy and paste processes. Financial businesses work in a complex, data-demanding environment but are falling behind when considering automation and data integration processes.
Collaboration between IT and Finance is critical in scenario planning as businesses continue to shift towards a stage of recovery after the pandemic. A report by Workday stated that nearly half of C-suite leaders were concerned their business was incapable of analysing real-time data and making informed decisions or responding quickly to unpredictable changes in the market. Finance leaders are experiencing challenges in delivering, reconciling and assessing high volumes of data. These hurdles are mainly due to less than half of those working in budgeting and planning activities claiming to use digital technologies to do their analysis. In contrast, around three-quarters of sales and marketing teams typically use automation. In short, it is of no value to having an answer to a question a few months down the line when you have to make a critical decision on something in the next few days.
Strategic FP&A is vital for integration, performance management, risk analysis and forecasting for multiple business areas. The reality is that finance teams are allocating too much time towards manual tasks like account reconciliation, investing time in managing and data organisation rather than analysing the information.
Since the pandemic, financial planning and analysis have progressed as businesses actively look for a greater understanding of their figures. Despite the movement in this market, many FP&A professionals still rely on manual tasks, such as correcting errors, updating reports and collecting data.
Factors like operations, technology and productivity all take a lower priority to the bottom line. Revenue forecasts are at the top of importance for CEOs because, ultimately, that is what defines capital flow in a business. Despite this clear recognition, only about 1% of the biggest companies in the world achieve their finance forecasts accurately, according to a study by KPMG. Discrepancies can cause a decline in investor confidence and result in a negative impact on share prices.
Gartner predicts that by 2024, nearly three-quarters of all new FP&A projects will expand beyond the finance world into other business areas. Cloud-based solutions are supporting the potential of extending automation past FP&A to other areas such as HR, sales and supply chain management.
Traditional systems working with finance operations still predominantly depend on manual entries and are more susceptible to errors and discrepancies. AI-based software has increased financial automation. Businesses that apply financial automation can accelerate and improve particular processes like financial close. Often this involves a long monthly process for recording and reporting transactions. Automating selected steps to this area can improve accuracy and reduces time applied to laborious tasks.
Other technologies such as robotic process automation (RPA) allow the auto-creation of documents from the predefined text and screen scraping to validate and consolidate financial data. KPMG predicts that businesses can gain cost savings of nearly 75% by automating finance operations, providing a quicker turnaround and reduced human intervention. Automation cannot replace the human element in financial planning. Instead, it can allow financial analysts to shift their focus away from daily reporting to focus on more insightful analytics and dynamic planning.
Imperial College London has announced the UK’s first degree to enable students to study a combination of economics, finance and data science. The new undergraduate BSc in Economics, Finance and Data Science plans to launch in October 2023.
The first of its kind degree offers an innovative approach towards leadership and prepares the new wave of economists, policy professionals and business leaders. The new qualification takes a different approach towards the study of economics and finance by integrating the importance of data science. The course has been designed by several leading academics from each core discipline, with support from public policy and industry leaders.
Utilising global-leading expertise in science, tech and business at Imperial, the new degree focuses on the rising demand for new professionals with academic experience in economics and finance, with the analytical knowledge supported with data science and coding skills. Professor Emma McCoy of the Imperial College believes education represents one of the most critical elements of global economic recovery from the pandemic and preparing for global disruption.
Students will gain the necessary skills to pursue many roles in industries including technology, finance, consulting and the public sector. Imperial College emphasises that the programme includes societal impact, diversity and sustainability within its core elements.
Dr Pedro Rosa Dias, the Academic Director of the programme, explains that we now live in an era of big data and has transformed the workplace and the way we recognise the challenges facing our world. The programme design and feedback from employers were relatively clear. We need the next generation of economic and finance graduates to have the ability to use data science to navigate businesses, public groups and international organisations within the digital economy of today.
Professor Emma McCoy of Imperial College London believes education is the most powerful force of our economic recovery from the pandemic and to manage further disruption. McCoy highlights that the students will become the thought leaders of the future.
The launch of this degree represents Imperial College’s support towards the next generation, influencing the discussions that will shape our future society. Successful individuals should expect to complete the course with a diverse skillset, a broad understanding of tackling global challenges and a flexible approach towards applying data in important decisions.