Finance leaders have had to make critical business decisions faster than ever before, causing an accelerated focus on the importance of big data and predictive analytics. Implementing strategic decisions has become essential for finance leaders while businesses transform their finance systems and adapt plans to continue growing. With this rising pressure, predictive analytics has become an even more vital tool in supporting the current challenges and providing a competitive edge over other businesses.

Businesses recognise that the more use they make of their data in developing plans and building scenarios, the more they can be ready for future disruption and changes. A blend of predictive analysis and simulations will enable clear insights and scenario development. This process is becoming more necessary as unpredictable events, ranging from climate change to geopolitical, continue to disrupt business plans and all need to be accounted for.

Predictive analytics today plays a distinctive role in strategic and operational decision making, with many industry professionals stating that a decision without data input has little or no value. A report by Deloitte suggests that just under 50% of senior executives believe that the main benefit of applying analytics is its influence in driving better decisions. 

The responsibility of a CFO has become more focused on balancing resource management and enabling the continued growth of other strategies that may lie outside of standard operations. If a business has access to data, they will likely want to generate insights on what is happening and determine why these activities are happening. Companies want to harness this information and apply predictive models to gain a better idea of future trends.

The pandemic created a seismic shift in the role of the CFO and their importance in business strategy and responsibility in business model transformation. Generally, any transition period involves a drop in initial revenue, followed by a long term plan to improve this. The more analytical support available, the better strategic and operational plans a business can make to reduce the impact of this change.

Predictive analytics is generally associated with supply chains, marketing and HR. In finance, there are many opportunities to improve and automate financial reports. CFOs can utilise automation and predictive systems. They recognise that it is necessary to enhance resources and apply new technology. Those finance leaders that have taken on predictive analytics into particular areas such as cash, audit, FP&A are likely to have a competitive edge over other businesses. However, it isn’t a simple transition and will require upskilling of employees to gain the benefits of this transition in finance.

Upskilling finance professionals and delivering an environment where knowledge is shared is critical. It will reduce time spent on particular activities and strengthen the relations across the business.

Automation analytics will create a competitive advantage for businesses as it creates additional time and resources that work towards high-value activities. With the correct insights and processes, the overall time to value is reduced considerably. It allows for more time to focus on value-added activities and to establish new opportunities within a business. Companies today are working with a range of data sources which means that it is even more vital to have the necessary tools to analyse this information.

Establishing a clear data plan that can effectively combine different sets of information and make it available to decision-makers with the right insights is probably one of the biggest challenges businesses face right now. When a data plan comes together, it can deliver significant benefits to businesses and create a distinct competitive advantage over other companies.

How storytelling can create deeper connections and allow your audience to relate to critical financial data

In the world today, finance professionals have greater access to a range of data and information. More information, however, may not necessarily translate into meaningful and decisive action. What is vital for people in finance is deciphering how these numbers affect their role and the business. 

Factual information, numbers and lots of data can be challenging to understand. If information is displayed more interestingly and humanely, people are more likely to connect, remember and take action. Storytelling is one of the most impactful methods that has emerged over time to connect and share information with others. 

Becoming an efficient storyteller may not necessarily have to be such a challenge. It does, however, require time to discover a story that relates to your data and then determine the most appropriate way of presenting the information to your audience. One method of reducing time spent on this process is utilising automation, allowing finance professionals to focus on discovering and delivering key insights.

The concept of storytelling has grown in popularity, as more businesses adopt automation and recognise the impact it can have on finance professionals. Storytelling is establishing itself as a vital tool within finance, creating deeper connections within an organisation and building the required company culture.

Storytelling is often considered as something that requires a sophisticated narrative, but it doesn’t have to be this complicated. The key is creating an accurate and logical way of sharing insights and ideas. The concept of storytelling is predominantly focused on improving communication. The important part is ensuring you highlight what discoveries you have found with the data, particularly information that is important to your business or a client. If this step is forgotten then the rest of the process is redundant.

After determining the key data points, the focus is on presenting the main findings and delivering the core theme. Considering the problems and how this impacts your audience, followed by a summary of insights and possible solutions is an efficient way of developing your story. 

When converting detailed, data-focused financial details that many will not understand, you are effectively transforming numbers into something that is relatable and allows individuals to recognise the importance of the data. Visuals are often a useful complementary part of a story, displaying a visual representation of the information. Visuals, however, should support a story, and not tell the story on their own.

A business needs to understand its audience and its language when putting together a report or presentation. Considering what’s important to your users, what motivates them and ensuring they recognise the insights is critical.

As AI continues to present new opportunities, the finance industry is putting its potential to good use. Predicting future trends, however, comes with its challenges. There are clear benefits of using AI in finance, but there are risks associated with implementing new technology.

AI improves financial inclusion by ensuring banks can determine credit scores, which is a critical factor in money management. AI can draw on social media or other sources to understand the ability of people to repay a loan. Reducing the constraints with financing means institutions can focus their efforts on better access to finance and growing the economy. ML and AI models in finance utilise big data to generate accurate predictions about the market. They assess multiple risk factors and determine the investment performance against various industry and economic scenarios. This process reduces the overall investment risks for finance businesses and their customers.

AI also supports investors in generating insights from multiple areas to develop their investment strategies within a relatively short timeframe. Several research groups are discovering that AI-based investments are exceeding the performance of conventional ones. AI and ML can improve efficiency and inclusion, but they also have two main risks.
AI-based credit scoring models may cause unfair lending processes. While a credit officer will be cautious not to include gender or race-related factors in scoring, ML may mistakenly consider these factors. ML models are only as reliable and accurate as the data they are made with. If models consist of poor data or data that reflects core human prejudices, it may generate inaccurate results, even if the data generation improves. The second challenge is that algorithms can also make finance businesses vulnerable to cyberattacks. It’s easier for cybercriminals to take advantage of models that all activities in the same way, compared to human systems, which work independently.

Policymakers need to accelerate their resources to combat the risks related to AI and other technology. One important method is improving the overall communication process. For example, finance-related businesses should instruct all users if a particular service uses AI. They should also explicitly identify the limitations of AI models so customers can make their own informed financial decisions. This process creates further trust and confidence and promotes a safer integration of new tech like AI.

Furthermore, policymakers should highlight human decision-making over AI-focused decisions. This approach is especially relevant for high-value areas like money lending, which can have a significant impact on the customer. Customers will feel more empowered in this scenario which allows them to adapt to the outcome of AI models. Users should have the option to opt out of having their data measured within AI models. Over extended periods, these measures increase the level of trust in new technology, like AI and ML.

Policymakers need to ensure that finance-related businesses test AI and ML models before implementing anything to remove possible bias. Testing allows businesses to check that the models are operating as expected and are meeting current rules and regulations. AI and ML can help finance businesses create a more accurate forecast of financial markets, but it can’t be considered more than a forecast. New technology like AI and ML should be viewed as tools with considerable potential if all the associated challenges are dealt with correctly.

It’s fairly clear to most now that the continued advancements in analytics will have a profound impact on the business world. The Big Data Analytics market is anticipated to exceed $225 billion in the next few years, and according to LinkedIn is a major driver of new job opportunities worldwide.

Advanced analytics, machine learning and AI will transform every part of our lives, from business innovation and government plans to our health, wellbeing and the environment. We often perceive big data and AI as technical fields but is heavily interconnected with our lives and nature. Big data and analytics is driving changes in multiple markets, enhancing R&D and improving healthcare systems.

Considerable investment and energy go towards developing AI and analytical technologies. Venture capitalist investment in AI-targeted startups has expanded by over 20 times to a value of around $75 billion, according to the Organization for Economic Co-Operation and Development (OCED). Investment is quickly expanding to new industries from transportation and construction to retail and financial services. Sooner or later, everyone will need data analytics within their business management plans.

The challenge is a lot of time and energy is being directed at the technology, there is less focus on investing in talent. To meet the rising demand, the world will require additional people with STEM skills, especially those with experience in data science and advanced analytics. Demand for data scientists has grown significantly in the last few years. Data Science and ML jobs represent five of the top 15 fastest-growing job areas in the USA, according to LinkedIn. There is, however, simply not enough young people moving into these industries, despite the lucrative salaries and career options. This is particularly true in the case of younger women.

According to Cornelia Levy-Bencheton, author of Women in Data, believes the industry is underutilising women in data science. Women make up 57% of undergraduate students and 60% of post-graduate students, but only 35% follow their studies in STEM. In the US, women represent 56% of the total workforce, but only 25% work in technology. The number is even lower within the data science area. One of the main issues is the lack of role models and the representation of women in senior-level positions.

Any plans or discussions concerning the future of business analytics and data science need to incorporate gender representation. It’s clear we need more data scientists, but more importantly, the industry requires a diversity of viewpoints and ways of creating new solutions with data. In a society where AI and advanced analytics will become vital in driving creativity, customer experience and innovation, the business equipped with the most data scientists is likely to have a competitive edge, but the one with the most diversity of skills and opinions will come out on top.

Having a mix of viewpoints, skills and opinions are important to the industry of data science. The data scientists are what matters the most and their ability to tackle problems and determine what questions need to be asked about data to deliver the most effective insights.

Gender diversity will impact the industry as the more women in the field, the greater the volume of perspectives and knowledge will be for generating new value and solutions. In an industry where 80% of big data professionals are men, more diversity can only improve processes and enhance the ability to utilise large data sets effectively.
Data skills need to be interconnected with other subjects, such as economics, engineering and robotics. The majority of future careers will require some STEM skills and knowledge of computer science. Despite recognising this importance in STEM, most students fail to take STEM classes or focus on computer science. There needs to be integration with education, the community and general awareness. These areas are creating economic and gender gaps within big data.

Aside from the obvious barriers, there are other personal factors like confidence and participation which influence the uptake of data science roles. Studies suggest that most young women are interested in STEM careers, but very few pursue this further into later stages of education.

Industry experts highlight that we require more women as role models to encourage young female professionals to feel more confident that they can pursue a career in the data and analytics market.