What do you do if you're overwhelmed by the task of managing and analyzing big data in corporate finance?

In my recent job I've colloborated with experts in Python and Data Analysts on showed them a big data file (data running into '000s of rows and how I wanted to see it. We first did some data cleaning and then mapped using external tables. Final output is an amazing dashboard which we showed to product leaders and executive leadership. They were amazed to see how the finance ledger data is converted to a product analysis.

Most of the times, overwhelm happens for 2 reasons: 1. Too much work, not enough time, 2. unclear on what's been asked of you. There's not always a lot we can do about No1, so I start with no 2, making sure I understand what's the purpose of that analysis, what questions am I trying to answer or what business decision and I trying to support. Other things I consider is what assumptions I might need to make in my analysis and what restrictions I have to deal with. Once I'm clear on that, the rest clicks in

Antes de come?ar a analisar os dados, defina claramente os objetivos de negócios que você deseja alcan?ar com a análise. Isso ajudará a direcionar o foco da análise e garantir que os insights sejam relevantes e acionáveis. Fa?a uso de ferramentas avan?adas de análise de dados. Essas ferramentas podem ajudar a processar grandes volumes de dados de forma eficiente e identificar padr?es, tendências e insights ocultos. Considere a implementa??o de tecnologias de Big Data. Utilize Inteligência Artificial e Machine Learning.

Break it down: Divide the task into smaller, more manageable components. Prioritize tasks based on their importance and urgency, and tackle them one at a time. Utilize automation and tools: Leverage automation tools and software designed for big data analysis to streamline repetitive tasks and enhance efficiency. This can include data visualization tools, machine learning algorithms, and data management platforms. Prioritize data quality: Focus on ensuring the quality and accuracy of the data you're working with. Invest time in data cleansing, validation, and verification processes to minimize errors and inconsistencies that can impede your analysis.

Absolutely, seeking expertise from financial data analysts or data scientists can be invaluable when dealing with vast amounts of data. Their specialized skills in data mining, predictive modeling, and statistical analysis enable them to uncover meaningful insights from complex datasets, helping you make informed decisions and strategic financial plans without having to delve into the intricacies of data analysis yourself. Collaborating with these experts allows you to leverage their expertise while focusing on your core competencies in financial planning and decision-making.

I agree, leveraging the right tools is crucial for effectively managing big data in corporate finance. Advanced spreadsheets and sophisticated business intelligence (BI) platforms offer a range of features such as automated reporting, real-time analytics, and customizable dashboards that streamline data analysis and visualization. Investing in these technologies enables more efficient processing and analysis of large volumes of data, facilitating quicker and more accurate financial forecasting and reporting. By harnessing these tools, organizations can gain deeper insights into their financial data and make more informed decisions to drive business success.

For the above mentioned project, we've used Power BI (Power Query), Python. You can also use ETL tools like Alteryx to seamlessly integrate to ERP like SAP or Workday. I recently found Datarails and Salesforce CRM analytics tools which also use AI to forecast the trends and generate insightful commentaries.

Working with Financial Data could be overwhelming at times and that too when it's at a corporate level. What I suggest is having a SME onboard with whom you can work on the KPIs which actually matters. Then breaking the problem into multiple silos maybe one KPI as a problem and then focusing on one problem at a time could help in reaching a favourable solution with a simplified procedure.

Yes, Simplify complex data by breaking it into smaller, manageable parts. Focus on key performance indicators (KPIs) relevant to your financial goals to extract meaningful insights and streamline analysis.

This is the daunting task out of the entire process. We might not need the entire data to smooth. We need to identify the right elements of the data which are relevant. That reduces stress on tools to process unnecessary data. Also, please note to ensure completeness of data before removing the data elements.

Once the dashboard is ready to use, the next step is show the impact of it and how to seamlessly navigate through it. Take a few employee trainings and prepare a Standard Operating Procedure or Knowledge Operating Procedure so that users can fix issues on their own.

Empower yourself and your team with data analytics training. Online courses cater to all levels, reducing overwhelm and building confidence in managing big data for corporate finance.

Prioritize relevant data in corporate finance based on its impact. Focus on key metrics like sales figures, cost analysis, market trends, and customer behavior to allocate resources effectively and avoid overwhelm.

Once the project is live, seek feedback and make the dashboard flexible. Remember, your dashboard should be useful. Continuously update the data.