What are the most important trends in business systems analysis for finance teams?

Retailer's finance is a tough job to handle all kinds of payment flows and credit maintenance day to day. The financial team needs statements and aggregative reports to summarise their sales Data of multi-channel streams and the report of what is outstanding against their PO is the best they can utilize and plan in the retailing.

AI and automation are key to modern financial analysis and task execution. Cybersecurity budgets unfortunately tend to be poorly appropriated. Let's not forget the most significant evolution in the finance landscape over the last decade; BLOCKCHAIN Technology. Any firm with serious ambitions in fintech can not survive without ongoing analysis and leveraging the capabilities afforded in these areas. Also, not including data analytics and visualization as part of a long-term strategy could leave organizations at a disadvantage to their peers and competitors in the finance space. Finance teams are utilizing advanced analytics tools to gain insights from financial data and data visualization tools to make these insights more understandable.

Leveraging artificial intelligence (AI) and machine learning (ML) in financial systems for predictive analytics, fraud detection, and decision support and investing in technology solutions to ensure compliance with evolving financial regulations and standards.

I think those who apply FinOps - where we can measure fine-grained processes to capital and costs - and center around Cost of Delay will have a competitive advantage. This is enabled through the commoditization of Serverless infrastructure, As Simon Wardley calls out.

Finance teams are progressively employing automation and Robotic Process Automation (RPA) to optimize operational workflows, diminish errors, and elevate overall efficiency in financial processes. Concurrently, there is a rising utilization of sophisticated analytics and business intelligence tools within the finance sector. Business systems analysts play a pivotal role in extracting valuable insights from data, enhancing the decision-making capabilities of finance teams. Moreover, heightened attention to data security and privacy necessitates business systems analysts in finance to possess comprehensive knowledge of cybersecurity measures and compliance requirements, ensuring the safeguarding and integrity of financial data.

My journey at Minnesota Power highlights the transformative impact of data analytics and visualization on financial strategy. By developing a dynamic Power BI dashboard, we unlocked actionable insights from complex data, enabling precise financial decision-making for projects. Combining this with statistical modeling in Python, we shifted from reactive to proactive financial management, forecasting with a newfound precision. For finance professionals, this is more than a trend; it's a powerful tool for strategic foresight. Embracing data analytics and visualization leads to clearer decision-making and sets a new standard in financial analysis.

One important advantage of mature data analytics and visualization teams is the ability to use trusted data to support multiple business functions and contexts. HR, Services, Sales, and others all require insight from Finance, but not the same insight or even the same data. Quickly presenting targeted insight relevant to each team is critical.

Its very easy to get side tracked or misled without proper facts. Data analytics tools and tracking helps my team to keep everything fact checked. We generate very precise and factual business insights by churning through tons of data using various analytics tools such as GA, Adobe etc. These are presented to stakeholders to make decisions and define strategy. Business teams then use this data to implement the business decisions and monitor and track their success. Resulting in great financial outcomes that keep us growing and improving day by day.

The increasing adoption of advanced analytics tools and business intelligence platforms empowers finance professionals to gain deeper insights, make informed, data-driven decisions, and enhance the accuracy of financial forecasting.

I have to say based on the experience of different types of management in system analysis that "Due to the existence of financial systems in all industries, the diversity of users' needs is always very high and expanding day by day with the advancement of technology. Therefore, it requires agile management that constantly changes according to the needs of the users and acts creatively and flexibly. This ensures that employees do not face work interferences and have an up-to-date and practical system, allowing us to surpass our competitors

I think the operative phrase here is "able to work in cross-functional teams". Data analytics and methodologies can only be relevant and useful if the program owners are collaborating with the stakeholders ... after all they are the ones closest to ground zero.

While simple & basic AI has been around for years, there has been an explosion of innovation & capabilities where AI can handle more complex business functions. Today's AI software can even write a term paper by rapidly gathering & assimilating extensive detail history & pattern matching with the input request. AI is not magic, & only as capable as the human programming behind it. However its promise is to automate some routine tasks so less "grunt" & time-consuming work is needed. AI allows for more helpful "self customer service" than a phone waiting tree. AI can edit requests deeper & improve accuracy. AI is only a tool - but just as PCs replaced typewriters - it may make users work more efficient & meaningful in the future.

In my experience, I've noticed a growing trend in the finance industry, where there's a gradual integration of automation and artificial intelligence. This shift aims to streamline processes, minimize manual efforts, and elevate accuracy in financial analysis.

It's important to note that while Business process automation/digital automation offers many advantages, it also requires careful planning, integration, and ongoing management to realize its full potential. Organizations should consider their specific needs, processes, and technology infrastructure when implementing automation to maximize its benefits.

In my professional journey, discussing my experience with Business Process Automation (BPA) and RPA often sparked concerns about job security. However, it's crucial to understand that BPA is a valuable friend! BPA is designed for repetitive manual tasks, giving talented staff (yes, that's you!) room to showcase their brilliance. By automating the repetitive, we're freeing our minds and time for addressing complex challenges, strategizing, and delivering excellence in our work. BPA helps to ensure smoother operations, reduces errors, and opens up opportunities to elevate our performance. The focus remains on growth, innovation, and empowering exceptional teams to conquer new horizons. Remember, automation is to enhance, not replace! 🚀✨

While an Innovation Analyst, I came to understand the pivotal role of a well-thought-out user experience design. Overlooking the needs, preferences, and behaviors of end-users can result in significant roadblocks, such as technical issues, user resistance, and a lack of adoption and engagement. In addition to conducting thorough research, creating use cases, and designing a thoughtful interface, securing buy-in and conducting user testing are essential steps to ensure optimal functionality and user satisfaction.

In my experience many of the previous related topics are hyper-technical topics; they fail to grasp the core of what being a business systems analyst means to a business. Many of the previous topics, being able to implement and select software, analyzing technical and financial efficiency of systems, being able to map current architecture, all will be tasks replaced by AI in the next 5-10 years. What can NEVER be replaced is the personal touch of relationship and trust building within a business, this is the true purpose of analysts. Their ability to provide information that is impactful and relatable to the audiences needs and desires, not a soulless prescriptive response.

The advancements and capabilities of AI, machine learning, predictive analytics, and visualization tools will supercharge financial companies agility, efficiency, and profitability. One key challenge for large financial institutions and business analysts is the breadth of their application portfolios, with hundreds to thousands of enterprise, 3rd party, and legacy applications, and the requirements and need for effective data lineage within and across business processes. While many banks have been able to realize some of the promises that came with big data, many have ongoing challenges connecting the dots through MDM, data warehouses, data lakes, and the like. You add in the pace of AI advancement and BAs are in for a challenging ride.

Certainly! In addition to the mentioned trends, finance teams are increasingly focusing on real-time data analysis to gain instant insights, leveraging blockchain for secure and transparent financial transactions, and incorporating robotic process automation (RPA) to streamline routine tasks, reducing manual errors and improving overall efficiency. Collaboration tools and remote work solutions are also gaining prominence, enabling finance teams to work seamlessly in distributed environments.