You’re a financial analyst. How can you make your job easier with the right financial management tools?

It's essential to be able to grasp in one tool the key performance indicators of any org to be able to support accountability and growth. Of course there are numerous systems at play at every organization - but being able to follow the key ones, in a simple and intuitive matter and set the figures against their respective target is key. Financial Management Tools help you do that in a visually appealing way. This helps financial analyst immensely because they can more easily see the impact from the KPI's into the P'n'L and understand where they need to focus on impact.

Financial analysis tools like data janitors, visualization wizards, and storytelling powerhouses automate the grunt work, letting you unleash your inner data detective. From wrangling numbers to crafting captivating reports, they'll transform your job from spreadsheet swamp to insightful oasis. Time to embrace the tech wave and ditch the data drudgery.

It’s baffles me that despite the existence of Power Query for more than a decade most accounting and finance professionals, charged with financial management tasks, do not know it exists. Power Query is arguable the world’s best ETL tool in terms of its ability & existence in 3 main instances. It’s the same tool but with some varied features, capabilities and processing power. Once you get familiar with Power Query in Excel you can then venture into Power Query inside Power BI and eventually also onto Data Flows (which is Power Query online). Power Query helps with data integration of multiple sources through queries that are loaded inside the transformation interface that is low code. You click and drag and it does the coding for you.

Automate data crunching: Say goodbye to Excel fatigue! Tools can import, clean, and analyze data from multiple sources, freeing me for deep dives. Visualize trends in a flash: No more squinting at spreadsheets. Interactive dashboards bring data to life, revealing patterns and anomalies at a glance. Boost collaboration and communication: Sharing models and reports becomes seamless, keeping everyone on the same page and fostering smarter teamwork. Reduce risk and error: Built-in validation and audit trails ensure accuracy and confidence in my findings. Free up time for strategic thinking: With the mundane handled, I can focus on uncovering hidden gems, analyzing scenarios, and crafting impactful recommendations.

The right financial management tools can significantly ease your workload as a financial analyst. Automated data collection minimizes manual errors and saves time, while advanced analytics enable sophisticated trend analysis and modelling. Real-time reporting tools facilitate swift decision-making, and visualization tools aid in presenting complex data. Enhanced spreadsheets efficiently handle large datasets, and collaboration features improve team efficiency. Compliance and risk management tools ensure adherence to regulations. Customizable and scalable solutions adapt to evolving needs, mobile accessibility allows for data access, and AI integration automates routine tasks, providing predictive insights for better financial analysis.

There are many data visualisation tools like Power BI, Tableau, Qlik, DOMO and yes Excel too. But irrespective of the tool, how you present and display the data story and narrative is critical and often lost in the complexity rather than stepping back and thinking with the end in mind. A simple mock up wireframe visual to start with perhaps an Excel dashboard can go a long way to improving the bigger more complex data sets. Simplicity is key and it’s much easier to make something complex but simplicity takes time and thought that is well worth the effort. Being able to easily and simple communicate, display and remove clutter and noise if key. Colour management, display objects eg slicers and ink ratio should be considered too.

1. Algunas herramientas están integrando Inteligencia Artificial, es importante aprender a incorporarlo y ser consciente de los límites actuales de este tipo de tecnología. 2. la presentación de estos datos deben estar orientados a un objetivo marcando el equilibrio de que esto no se convierta en un sesgo, 3. Se debe tener la habilidad de presentar de manera sencilla y concreta los datos.

There are many modern Business Intelligence tools like Power BI, Qilk Sense, Tableau, etc in the market and some of the them are also enabled with AI and machine learning features, but as a Financial Analyst you must have control over the data and how you present it to your audience is a key, you must understand the audience understanding level and need. A proper and well designed story line needs to be developed using visualizations that are most relevant and compatible with the data numbers.

Whilst traditional analysis is useful (NPV,IRR etc) and the most powerful being unit of economics, there is other analysis that is often ignored. Meta data (data about data) and variance analysis on prior analysis and modelling to enable insight and learning. An example is when a forecast and a budget is created there are underlying assumptions and drivers that are used to generate the forecasts or budgets. Many simply move on once those are produced and don’t go back and analyse what changed on the underlying core assumptions not just the variances on headline financial outputs. Understanding and improving cycle times to produce analysis, error rates and accuracy. Without post analysis of the data we simply won’t learn to improve.

L'analyse de données est devenue un enjeu stratégique majeur pour les acteurs de la finance. Dans un environnement complexe et concurrentiel, exploiter intelligemment les données représente un levier de performance et de compétitivité incontournable. Grâce aux techniques de data mining, de modélisation prédictive et d'intelligence artificielle, l'analyse de données offre aux entreprises une capacité d'anticipation et de pilotage décisionnelle forte. l'analyse de données est un véritable catalyseur de performance et est amenée à se généraliser. Les entreprises mettant en place dès à présent des outils puissant d'analyses de données sont amenées à avoir un avantage concurrentiel sur les autres entreprises de leurs secteurs.

Much the same way that OpenAI released ChatGPT to the world to get feedback for the LLM to evolve, so do members of teams need to share and collaborate across the organisation to get feedback and improve. Data shared purely in silos is simply never going to grow into a highly valued artifact and digital asset. The way Power BI can be embedded into Teams chats, enable comments and feedback and published daily or a chosen frequency to users is a great show case of what collaboration looks like. In addition to sharing the dashboards and artefacts it’s equally important to collaborate prior in the design, resource prioritization and alignment to increase adoption and value.

Tools often get too great a focus without recognising that they are only part of the visualisation delivery process. It’s not the tool, it’s how you use it to influence and tell the data story that is valued. The stakeholders and decision makers literally couldn’t care less which tool you used but rather what you are trying to pitch to them and whether they can understand it clearly to make a decision. You can achieve this in almost any tool depending on the size of the data (Excel will break with too much data). Start small and grow. If you don’t have how to build a simple set of dashboards and lay them out on a page neatly and allow for some elements of interaction in Excel, then jumping to bigger tools is not a good idea.