CFOs and other financial professionals are fairly optimistic that we are approaching an economic recovery. Over 70% anticipate that we will return to normal by the end of the year, according to the latest Enterprise Financial Decision-Making report from OneStream, a leading provider of corporate performance management solutions. The report suggests that businesses have increased their investment into data analysis tools and general usage considerably over the last year.

The study by OneStream explored financial leaders across North America and determined certain factors that influenced their priorities, budgets and technology integration plans for this year. The findings suggested that the pandemic had generated a bigger need for flexible forecasting, predictive planning and digital transformation. The capability to forecast budgets quickly and change workflows have become a critical feature since the pandemic began.

The report also discovered that financial executives have significantly increased their usage and investment in data analysis tools. Businesses generally tend to invest in artificial intelligence and the results showed increased use of cloud-based planning and reporting solutions. Most businesses (around 70%) are already using or plan to adopt (20%) a low-code development platform that allows business users and developers to adopt new roles while managing complex coding requirements.

In the case of return-to-office budgets, data privacy tools are regarded as the most common priority, followed by hybrid cloud solutions.
In comparison, the OneStream 2020 Enterprise Financial Decision-Making Report indicated that less than half of finance executives used cloud-based solutions regularly for reporting. The 2020 report also showed that less than a quarter used machine learning and artificial intelligence solutions.

Many financial leaders are exploring their workforce, technology and supply chain requirements in a post-pandemic world. However, the influence of changing policies and social development has had a significant impact on investment plans, encouraging leaders to focus on sustainability and diversity measures as well.

The study was conducted by Hanover Research this year covering hundreds of financial decision-makers in Northern America. The full OneStream Report – Enterprise Financial Decision-Making 2021 – North America can be viewed here.

Businesses today are becoming more and more reliant on data and fortunately, the availability of data is rising considerably. The amount of information generated by one business would have simply overwhelmed some of the largest companies from a decade or so ago. 

Yet, the majority of people in recent studies confirm they continue to find the volume of data too much at a certain time, while another 45% from the recent State of Growth report stated that the lack of data represents one of the biggest challenges in business.

How can the finance industry work through this balancing act of remaining in control of their data and satisfied with the volume and quality of information they have available?

Enterprise Resource Planning (ERP) systems have become a vital tool for the management and organisation of business data. While each team may only require access to a certain portion of the data, combining it all within one system creates a much clear and effective view of the entire business. There are certain considerations to think about for financial analytics and successful reporting.

Are the tools you use suitable for your business?

Spreadsheets are popular reporting options due to their simplicity but they have their limitations. Working with conventional spreadsheets can result in errors, often due to calculating formulas incorrectly. To create a higher level of accuracy, information needs to be constantly updated, which makes it complicated to maintain this level of data quality. 

How accessible is your data?

Combining various data sets in different ways is one of the biggest benefits of applying the right tools to your business. This could be combining financial, production and other data to determine the production costs of a selected product in two varying locations. This level of data wouldn’t be achievable if the business is using varied systems or spreadsheets, or possibly the incorrect ERP system. Utilising an ERP system with a singular database makes it simpler to create centralized access to the information across the business. If data is being captured in this manner, it becomes easily available for various teams to assess and generate new insights.

Are the reports flexible?

ERP and finance software providers generally provide a range of pre-formatted reporting templates that can be applied to standard reports. This reduces time and money spent by eliminating the need to generate frequently used reports. The problem is that one format won’t necessarily work for another. Using predefined reports can be useful but many financial reports need to be customised to present data in other ways for different clients. Often, altering the layout or incorporating new data into an existing report can be complicated and time-consuming.

Are there restrictions with your KPI monitoring?

KPIs allow business leaders to understand how well their team is performing against certain objectives and enable businesses to focus on reaching specific targets. Most software solutions are embedded with built-in KPIs but to be effective these KPIs must correspond with the correct business model. No two businesses are the same and some software providers do not appreciate this, assuming that several standard KPIs are all that business requires. 

Can dashboards be customised?

Associated with the KPIs are dashboards, which are an important element in providing visibility into goals for a business. In addition to displaying KPIs, they can support individuals to remain focused on their work, keeping them alert to potential issues that need to be addressed or certain jobs that need completing.

To grasp the full potential of data, businesses need analytics that truly add value to your business and reporting that is insightful. While many software providers offer various tools, not all systems are equal. Selecting the incorrect solution that has irrelevant data can limit the ability of a business to take full advantage of its information. Selecting the best solution will generate a higher level of flexibility and confidence that the right information is being channelled to the appropriate people at the right time.

Forward-thinking financial businesses are applying data, analytics and AI to create new products and services and to enable the delivery of more intuitive and personalised service. Future success requires attracting skilled talent, enhancing existing processes and meeting the increasing demands of customers.

The finance industry is continuously focused on digital transformation and utilising data, analytics and artificial intelligence to improve their services. Banks are applying AI to enhance their current offering, build new products and improve the overall customer service. Data and analytics are proving vital in supporting the progression of new business models and improving decision making.

This significant shift comes with its challenges, such as accessing data, the quality of information, the available skilled talent and the delivery of necessary resources to generate the required results. Most financial teams also lack the required digital experience to create and build a digital-focused organisation. Nevertheless, the decision-making influence of data and analytics provide financial businesses with the competitive edge needed to provide the added value to their customers.

According to studies from McKinsey, Financial businesses that apply data and advanced analytics to engage with customers and drive new business are capable of generating value in several ways:

Enhanced Customer Acquisition: By focusing on delivering personalised information at every step of the customer acquisition journey, engagement becomes simplified and the overall customer experience is improved.

Higher Customer Lifetime Value – Offering new products and services to customers is done through intelligent communication and recommendations based on real-time analytics and data.

Reduced Operational Costs – Automating and simplifying data processes and decision making plans, overall efficiencies are improved and costs decline.

Reduced Risk – Applying advanced analytics enables selective decisions on potential customers and allows detailed analysis of customer behaviours to detect potential cases of risk and fraud.

Financial businesses are increasingly looking for analytical solutions to improve their position against other competitors. According to a report by the Cambridge Centre for Alternative Finance and the World Economic Forum, several global financial services firms have implemented or are working towards adding AI solutions within a range of business functions. Data from the World Economic Forum suggests that approximately 85% of all financial businesses have implemented AI, in some shape or form.

Beyond the security and risk detection side, an increasing number of financial services are applying analytics to create new services and an innovative customer experience. Advanced analytics can be applied across the entire customer journey, from initial engagement through to evaluation and ongoing relationships. As a business uses AI to further deepen its understanding of each customer journey, the organisation can deliver highly personalised offerings directly to the customer in real-time.

Applying data to generate insights that can be applied across the business isn’t enough. Financial businesses that engage continue to increase, experiences are enhanced and loyalty is strengthened. In other words, the ROI on data and AI is not fulfilled until these factors are achieved.

The necessity for clear plans on how to reach net-zero targets and creating a level of resilience are some of the biggest challenges for financial businesses addressing climate risk.

According to a survey by Willis Towers Watson of leading financial businesses, the availability of data represents one of the biggest challenges businesses expect to experience over the next five years to tackle climate risk. 

The survey suggested that 80% of respondents reported data as their biggest concern in regards to an effective transition to a Net Zero economy. Delivered as part of the Willis Towers Watson’s Climate Risk and Financial Stewardship Summit, the survey included banks, insurers, wealth and asset managers to gather insights on the progress made to manage the impact of climate risk on their business.

The study also showed that few businesses expect the level of risk related to climate change to decline, with most anticipating the existing level of risk facing businesses to escalate in the coming years. With current trends indicating that we are struggling to maintain temperature levels from exceeding the 1.5-degree target of pre-industrial levels, respondents stated that creating an effective net-zero strategy was the biggest challenge, followed by a lack of data and resources.

Aside from the climate-focused challenges facing the financial sector, respondents highlighted that transition, reputation and social responsibility were key measures that organisations were prioritising to reduce the gap and achieve net-zero targets.

Rowan Douglas, Head of Willis Towers Watson’s Climate and Resilience Hub stated that climate risks for the future are unprecedented. While the financial industry is in a strong position to take a leading role, climate-focused risk needs to be integrated into daily management but also influence the entire economic transition towards a low-carbon and resilient future.

Reports from the Carbon Disclosure Project (CDP) indicate that approximately half of the world’s 500 biggest businesses are yet to conduct any analysis of how their portfolio is impacting the climate. This is even though the portfolio emissions of global financial organisations are over 700 times bigger than direct emissions.

We are experiencing a transformation in what Net Zero finance means for the financial industry and its importance in an economic transition towards a climate-resilient future. The added pressure from bold new climate targets and further inspections from banks, regulators, investors and the wider public are continuing to rise. To succeed, financial institutions need to adapt, acquire data to support their new plans, and ensure their portfolios are aligned with a net-zero carbon world.

Amazon’s AWS cloud business has announced the launch of a new data management and analytics service for the financial industry.

As with most industries, data represent the driving force behind financial services, for banks, insurance companies, hedge funds and many more. This data, whether it be structured or unstructured, is stored in a silo separate from other data. Being capable of aggregating and ordering this data can enable businesses to unlock new insights such as patterns with transactions, customer profiles and predicting future buyer trends.

Known as FinSpace, the new service will reduce the amount of time spent by the financial services industry, replacing traditional manual processes that are often complicated due to governance and compliance policies. The service includes an analytics system developed on Apache Spark, an open-source analytics engine dedicated to processing big data.

As with most AWS services, FinSpace is priced on a per-usage basis, including the amount of data stored, user numbers and the resources used to process the data. Amazon said Legal & General and Deloitte are some of the first businesses to use FinSpace.

With investment in cloud continuing to rise, it’s clear that big public cloud businesses need to specialise. A one size fits all approach won’t necessarily work to attract leading big business from on-premises to the cloud.

FitSpace fits into a larger trend that includes larger cloud businesses target markets with varied toolsets tailored to meet a specific market.

In previous years private equity may have held back the financial industry in terms of the uptake of new technology and automation. As more data becomes available, the requirements for reporting and transparency have increased and businesses are continuing to diversify their investment plans, and realising that having strong expertise in analytics is a necessity.

Technology has become such a significant part of every industry, including the finance sector. In regards to the growing role of data and technology, industry specialists have emphasised the shifting position of the chief financial officer (CFO) within private equity firms. Technology and innovation have become critical across the industry in general. The CFO is becoming pivotal, a highly valuable asset within a business is data. This role has more responsibility concerning how to utilise data and apply the benefits to a business.

Portfolio Analytics and technology and key functions CFOs highlighted as required to shift from standard to strategic planning. The EY 2020 survey discovered that 70% intend to increase time spent on portfolio analytics and a further 69% plan to spend more time on technology.
CFOs are leveraging technology to manage routine tasks in their business. In the future, we’re likely to see CFOs continue working strategically, focusing on analysing data across the entire business.

Deal and portfolio executives are becoming increasingly popular so CFOs are taking on more responsibility and moving beyond finance into several new areas, such as data protection, resilience, digital strategy and ESG.

In a report named Delivering Value from Data, data-driven decision making has become a critical element of business as data-focused leaders have displayed the effective use of data to leverage new opportunities, disrupt markets and generate a competitive advantage.
The increase in fund sizes and expansion of portfolios in regards to funding size and location has spurred a need to utilise technology to manage these changes. Companies that can leverage data, utilise predictions and decisions, will be positioned to create a more competitive advantage.

The desire to increase the benefits of technology is also being experienced with institutional investors. As wealth and pension funds get bigger, more complex and competitive, their capacity to use technology is paramount. The availability and quality of data are becoming crucial, assisting funds in making better investment choices in today’s rapidly transforming global market.

Anything that makes funds more efficient at selecting the best investments is a major competitive advantage, and this is where data comes into play. Being agile and responsive is key and being capable of adapting and managing the pace of data demand.

This increase in data, analytics and technology is influencing hiring strategies and developing a finance workforce that includes relevant data analytics and technology skills. The conventional workforce of private equity is accustomed to standard systems like Excel but today employees need training on new tools as the technology landscape continues to transform.

The skill sets needed for the talent pool are much more technology-focused, with higher data analytics experience. CFOs are now focusing on how, in this competitive market, you’re not just competing against other private equity firms, but actually against other industries, like technology. So with this in mind, it’s critical to remain focused on attracting and retaining the best talent. Private equity CFOs recognise the need for them to accelerate the pool of talent within the business, especially as data analytics takes on a more important role in an organisation.

David Alich, director of analytics and technology at PwC recently gave his view on the future in a panel discussion. Alich believes the investment will continue to increase in data and analytics technology solutions. He anticipates that every fund will have its analytics and data experts or data science teams who focus on high-value projects and implementing analytical solutions into portfolio businesses.

Data quality is regarded as one of the biggest threats to reputation according to finance leaders. Data quality has become one of the biggest issues requiring attention according to senior finance leaders. As businesses continue to look towards data to enhance performance, the risks and challenges related to poor data have become even more important.

Focusing on increasing the quality of data can create many positives. In the latest IBM 2021 Global C-Suite Study, 70% of senior finance leaders believe that implementing company-wide data standards is a key priority in supporting their business with consolidating systems, cost reductions and scaling effectively.

CEOs are requesting finance leaders to take further responsibility in terms of standardisation, integrations and other associated services. Representatives of the board are demanding more insights, real-time reporting and analytics. These insights are unreliable and have limited use if not based on reliable and consistent information. It can, however, be challenging as raw data is generally quite messy and CFOs can find it difficult to maintain consistency and keep the information clean.

Many larger businesses have approached data quality by hiring a chief data officer (CDO), but in smaller businesses, CFOs generally take on the CDO role themselves.

CEOs understand that positioning the CDO in other functions can create bias in the way data is collected and interpreted, but CFOs are viewed as independent. They are generally regarded as the key to financial data accuracy and are appropriate for controlling other internal and external data.

As organisations become more reliant on data, the effects of poor data on decisions and overall performance will become more serious. More often than not, businesses will invest great sums of money into cleaning, integrating and managing data that may not even really matter to their organisation. Many CFOs often regard the cleaning and management of data sources as an additional burden. The reality is, the insights can be very important to making critical business decisions and have the ability to transform the role and finance function of CFOs.
With a solid platform of cloud-based data, CFOs can transform services in the finance industry by implementing automation in accounts receivable, accounts payable, reconciliation and report, enabling them more time for other valuable duties.

Sarah Ghosh, director of Onyx AI believes that the roles of CFOs were expanding partly due to advanced data analytics and machine learning technologies offer new ways to discover value in the data and delivering new business insights. Applying these technologies effectively requires a big focus on data quality.

Approximately 70% of businesses have made major decisions with inaccurate financial data, according to a survey by software company BlackLine. A further 55% of finance leaders stated they aren’t confident that they are capable of identifying financial errors before generating reports. It, therefore, comes as no surprise that there have been several stories about the detrimental impacts of misinterpretation of information.

External data from outside the finance departments can be even less reliable, with many senior leaders stating aside from cybersecurity, poor data is now the biggest issue to the future of boards and management. This pressure has accelerated even further during the pandemic. One particular pressure on finance leaders has been the rising demand for data by a combination of management, other markets and regulators. In some cases, regulators have utilised data to interpret themselves, which can result in varied results. In this industry, accuracy has become a critical element.

Businesses of various sizes are attempting to manage a range of systems that do not necessarily communicate with one another, which can make data management a big challenge. Generating quality requires effort to assess, validate and reconcile data, and the ability to correct errors.

Thankfully, cloud systems provide more flexibility and integration solutions that generate new insights from both structured and unstructured data.

Combining cloud with analytics tools can simplify data consolidation and cleaning, instead of applying the conventional means of spreadsheets. To manage these tools, the CFO may not necessarily require new skills but must be capable of understanding the variety of skills they may need to incorporate into their finance team. In an ideal situation, this would be a data scientist or analyst with strong business experience. The need to find people with these skills is critical and is causing challenges, due to the lack of such talent being available.
CFOs are exploring how to deliver an optimal mix of expertise and solutions to establish data governance and management teams with a clear framework for monitoring and support. A deeper understanding of these structures will allow to CFO to create teams and allocate the necessary budget to generate improvements and efficient returns, without requiring the need for more detailed technical knowledge.
Applying the data quality role enables businesses to have the chance to increase their technological, analytical and presentation skills. If they can implement all of this successfully, CFOs can confidently lead their businesses into the future.

Data represents a key element of success in the finance industry. Recent studies suggest that 60% of finance leaders consider the leveraging of data science and analytics for clearer insights and enhanced decision making as top priorities for this year.

The ability to use data analytics and big data to create a competitive advantage and to improve the operations and management of strategies plans are considered top factors for senior-level executives across the world.

Data analytics can be applied in three particular areas. In regards to planning, data analytics can be utilised for efficient risk management, data testing and statistical sampling. Data analytics can also improve the delivery of audits, generating rapid and efficient monitoring of particular controls, detecting possible cases of fraud and recognising any trends suggesting future risk.

Based on the findings from Protiviti’s latest finance trends survey, security, privacy and data analysis are considered top priorities. All three are connected with data, with data analytics regarded as very important by over 60% of CFOs surveyed.

Data is viewed as a very important area because it supports the generating of useful commercials insights, the ability to increase sales and improve the overall management and decision-making process. It also enhances the internal operations of the finance area, with over 50% of respondents regarding data analytics as a vital element of process improvement. Applying data analytics effectively, however, requires several factors to be put in place.

One key factor that senior finance leaders are struggling to cope with is the quality of data. As with most other industries, the analytical and reporting that is delivered by finance depends on the overall quality and completeness of the data used. Data governance is a vital part of this process. Finance leaders need to ensure they implement a strong data governance system and understand data ownership in the business.
The quality of data can also be improved by dedicated data management, an area that can require considerable time and expense. Once implemented and ready, however, finance leaders will be capable of gaining a much deeper understanding of various functions associated with profitability and risk. Without quality data, the results from any data and analytical processes will not necessarily hold as much reliability and value to the business. This is particularly true with the continued growth of AI and Machine Learning, meaning data quality will become even more important to businesses soon.

Data security

In a time of rising cybersecurity threats and new data legislation, businesses need to keep a close check on data safety. If any financial data is leaked, businesses could face considerable financial and reputational damages. This is why security and data in finance are regarded as a top priority by finance leaders. Over 70% of CFOs and VPs listed this as the most important factor.

For larger financial organisations, the stakes are much higher. The volume, complexity and sensitivity of their data reach another level and with more businesses moving to the cloud, the range of security risks continues to increase.

Other demands pointed out by finance leaders are the changing demands of customers, managing regulatory changes, the movement to the cloud and new tax requirements. One particular area that is often covered in the robotics process automation (RPA) market, yet many finance leaders are taking a fairly cautious approach to RPA. Only around 20% of CFOs and finance leaders regard RPA to be a top priority for the next year.

Tony Abel, the MD of Protiviti explains that many businesses are still collecting more information on how to leverage tools like RPA. Growing financial concerns and the demand for improving efficiency mean the use of RPA and other innovative technologies will increase over time.

One of the key benefits of RPA is that it can be deployed relatively easily and perform repetitive tasks across various systems. For many financial businesses, RPA could be effectively used in the accounts payable area, for processing invoices, payment verification and account reconciliation.

CFOs can often take a cautious approach to accept new technologies but data and analytics professionals can support them with really understanding the benefits of implementing these solutions into their business.

According to a study by Gartner, after a significantly disruptive year, CFOs seem to be prepared to invest in projects that enable the implementation of new analytics and automation technologies. There are two key reasons for this transformation:

-Looking specifically at finance, where analytics information on certain metrics is valuable, there is a demand for more innovative analytics to enhance performance.
-RPA technology is an automotive solution that eliminates manual tasks that can’t be integrated. Repetitive tasks can be a problem for finance, where a mix of various systems can be difficult to integrate.

While reports suggest that analytics and services like RPA are regarded as top priorities, there is also a level of the hesitancy of CFOs to invest in these new technologies. In the same survey by Gartner, nearly 80% of CFOs stated that they had some doubt they would reach their goals in advanced analytics and over 50% showed concern with reaching goals by implementing RPA.

These figures are a little concerning for the IT industry. The feelings in the finance department can influence other sections of a business. CFOs also hold a lot of control over which IT projects to implement and what tools to use in a business.

What are the key steps IT leaders can take to ensure that CFOs are supportive of analytics and other innovative digital projects?

Clear project success

Creating short term projects that have clear, achievable goals and returns on investment will demonstrate success and build confidence for the long-term implementation of new services.

Understand strengths and weaknesses of users

In terms of finance, the team require more analytics but it is relatively easy to get overwhelmed with all the information and lose sight of the bigger picture. For example, users can explore financial results, but yet still lack a clear understanding of key elements that influence the bottom line.

The customer service team may utilise analytics to explore which customers are satisfied. This is useful in determining customer attrition and predicting which groups are likely to be long-term customers. Similarly, manufacturing teams can use analytics to understand equipment downtime before it may happen, which maintains productivity and reduce expensive periods of downtime.

There are two examples of how analytics can influence financial health and highlight areas that may be overlooked. If IT teams can utilise these solutions with finance, CFOs would understand the real value and be more likely to accept these projects.

RPA isn’t the only technique available to reduce repetitive work

RPA isn’t the only way of eliminating repetitive work but represent one of the clearest ways for finance to understand the benefits of applying automation to its workload. Automation and eliminating repetitive work can happen in multiple areas of a business by applying automation technologies that differ from RPA.

This is somewhere where IT teams can present clear defined business cases, with an explanation of the technology and the resulting ROI from applying these solutions.

Why is this important?

The ultimate goal is to ensure the CFO is connected with how big data, analytics and automation can be applied in the company to generate better results and revenue. While there is always a level of risk and uncertainty with new projects, having the CFO and other business units in support and invested in the success of the project is a big step in enabling a successful implementation.

In previous years, employing a chief data scientist was viewed as quite a luxury. Today, the role has become a necessity, especially as more businesses continue to accelerate their digital transformation plans in challenging conditions.

The importance of data science and analytics has surged during the impacts of the pandemic as businesses recognise how data is critical for survival and continued progress. With data now influencing major decisions at the highest level, the need for senior leaders in data science has become more important.

Today, it is clear that leaders need to work beyond identifying and measuring analytics. Organisations require chief data scientists, capable of connecting executives and data science teams, an individual capable of defining strategy and executing data-focused plans. As a result, investment into chief data scientists roles has increased as they primarily focus on managing the data systems, creating a clear strategy and improving the overall quality of data used. 

A chief data scientist is regarded as having a deeper understanding of how new technology systems such as AI and Machine Learning can enhance data management. This has become more important as ML has continued to influence data quality and navigating big data concepts into real-world ML implementation. The chief data scientist is responsible for the navigation of this process, focusing on data as the primary driver for new initiatives. 

Businesses are actively engaging with their customers in new and innovative ways, generating new business models and exploring more efficient ways to launch their products. These processes all require complex data plans and need the support of an experienced and skilled data leader.

When it comes to making important decisions, senior executives are becoming more reliant on the chief data scientist. A study by the IDC discovered that nearly 60% of chief data scientists report directly to the CEO. The position has progressed significantly over the last few years in terms of value and responsibility.

For this year, the priorities of chief data scientists will be focused on discovering ways machine learning can be applied to manage the challenges related to the pandemic and economic recession. One area is identifying churn rate, understand when customers are likely to leave. This type of information requires expertise as well as different levels of technical and data science knowledge.

This year, chief data scientists will need to expand on their existing influence across their business during a critical stage in the economy. Pressure will be higher for them to discover solutions and so challenges will focus on ensuring data science teams are focused and working with the right data sets. At the same time, chief data scientists will be empowered to utilise corporate data assets to make important decisions for their business.