Como você pode melhorar a qualidade dos dados em FP&A corporativo?

Strategies to enhance data quality include: • Establish a robust data governance framework. • Implement automated data validation and cleansing processes. • Ensure standardized data sources across the organization. • Integrate data from various sources for a comprehensive view. • Strengthen data security measures to protect sensitive information. • Automate data collection to minimize manual errors. • Conduct regular data audits to identify and address inconsistencies. • Provide training on data management best practices. • Define and track key data quality metrics. • Continuously monitor and improve data quality processes.

For sure it is the most important and crucial factor which is a must for reliable Reporting and Analysis and it's importance is the same importance of the overall FP&A function itself, therefore the good FP&A has to grant strong and sustainable data sources to collect a reliable and accurate information. Good system solutions will help for that but not enough as part of the daily Tasks from my perspective for the good FP&A is the interaction with the work environment, having sources of information flow not only the data as the data most of the time needs supportive explanations so that you can feel a good level of reliability and satisfaction from. The data you are getting and Reporting

A predetermined data standard structure and mandatory information inputs will improve the quality of data. To ensure that system generate data that provides the users with insight and usable for business decisions, data should be standardized and stored correctly.

In my experience, the better the data you get, the better you do your analysis. In the company that I worked with initially it was difficult to analyse the data, coz the descriptions in the expense transactions wasn’t easily consumable for analysis. We worked closely with the respective teams to ensure the necessary fields were captured properly

Another thing that needs to be defined in the standards is how to handle the changes. For instance if one of the sources of data cannot be used going forward, how do we decide on a new source and what changes that need to be done to previous comparable data.

In addition to implementing data governance, using automated data validation tools can significantly enhance the quality of data. These tools can identify discrepancies, errors, and inconsistencies in real-time, allowing for prompt resolution and ensuring accuracy in financial analysis and reporting. Also, establishing regular data quality reviews as part of the governance framework can proactively address any emerging issues, fostering continuous improvement and reliability in decision-making processes.

Having a data governance framework will help boost data productivity by creating an diverse environment which promotes practices that lead to data driven decision-making.

Contar con una governanza de los datos en toda la empresa es muy relevante, apoyado siempre del equipo de IT y áreas que tengan responsabilidad sobre la data e información que generan. Politicas y procedimientos respecto de los datos también ayudará a proteger la información. ¿Cuál es el problema? la capacidad de organizarse y estructurar un data warehouse eficiente-eficaz. Por lo tanto, los roles y el compromiso de la alta dirección es vital para que esto ocurra.

Implement data governance. This involves establishing roles, responsibilities, policies, and procedures for managing data across your organization. Data governance helps you align your data strategy with your business goals, ensure data compliance and security, and monitor and measure data quality.

Implement data governance by establishing roles, responsibilities, policies, and procedures for managing data across the organization . Align your data strategy with your business goals, ensure data compliance and security, and monitor and measure data quality.

An Enterprise Performance Management (EPM) Software like OneStream, Board, Acterys, Hyperion etc... is the backbone infrastructure needed to perform data consolidation. The state of the art is normally to connect that to the local ERPs and have automated updload of data, complemented by preparer reviews and additions to ensure quality. One key factor of success is to have a joint solution with accounting consolidation, so that there is really only one source of truth in the group, and FP&A data is equal or at least easily reconciled with accounting consolidation.

En algunos casos de tareas simples la automatización funciona. Sin embargo, esto trae más de un dolor de cabeza. a veces es más conveniente realizar el trabajo de forma manual con un buen analista. De lo contrario debes lidear con los RPA programar una y otra vez si existen cambios. No siempre hay reducción de errores o de tiempo. El foco debe ser calidad y procesamiento. Santas Macros :)

Automate data workflows to reduce manual errors, save time and resources, and enhance data reliability and consistency. By automating data workflows, you can streamline your FP&A processes and focus on value-added activities. Use a data integration tool to automate the process of extracting data from multiple sources and loading it into a central repository. Use a data validation tool to automatically check for errors and inconsistencies in data.

It is not just about collecting data and automate it, it is also how it should be automated so it doesn’t include information that is not needed that can, not only bring wrong conclusions but also make additional issues in other businesses processes (like billing for instance). Best approach is always having processes prepared manually and automating it based on accurate manual results with constant cross-reference with automated process. Once both are on the same place, it will reduce manual errors, save time and give other stakeholders reliability to trust data and reports. This also doesn’t mean manual crosscheck shouldn’t be used - it has to be checked frequently in order to confirm processes are working properly and smoothly.

Automating data workflows is a key strategy in improving data quality for corporate FP&A. Here’s how automation can make a difference: Reduction of Manual Errors: Automation minimizes the risk of human error during data entry and processing. Time Efficiency: It saves significant time by performing repetitive tasks quickly and accurately, freeing up staff to focus on more strategic tasks. Consistency: Automated workflows ensure that the same procedures are followed every time, leading to consistent and reliable data. Scalability: Automation can easily handle increased volumes of data without the need for additional resources.

Integration of Data sources is the key. Use APIs wherever possible in cases where it's not, have a unique ID which will be common across multiple systems/platforms. This Unique ID will help tie the data across systems.

Data integration plays a crucial role in enhancing data quality. When data originates from various channels without any connection, it tends to become diluted. By leveraging data integration, this problem of dilution can be eliminated. Due to the cost implications involved, some organizations choose to avoid system integration, but another way this will reduce collections time and accuracy of data consolidation. Therefore, the integration of systems plays a vital role in maintaining high-quality data. There are so many advantages of data integration as below. 1. Consistency 2. Accuracy 3. Timeliness 4. Completeness

Integrate data sources to eliminate data silos, avoid data duplication, and enrich your data with additional insights. Use APIs or unique IDs to connect data across multiple systems and platforms.

Integrating data sources is indeed a vital step in enhancing the quality of data in corporate FP&A. Here’s why it’s beneficial: Elimination of Data Silos: Integration allows different departments to access and share data, promoting a unified view. Reduction of Data Duplication: It helps in maintaining a single, accurate record for each data element. Enriched Data Insights: Combining data from various sources can provide a more comprehensive view of business performance. Single Source of Truth: A centralized data repository ensures that everyone in the organization is working with the same data set. Cross-functional Analysis: Integrated data supports broader analysis, leading to more cohesive and strategic business decisions.

The last thing you want to do is spend hours analyzing incomplete or incorrect data. Make sure you have a check point to quickly catch anomalies (sudden changes in numbers, too many negatives or missing info) - and always trust but question - they call it professional skepticism for a reason. Keep the source raw data clean and create a version that is a work in progress - in case anything goes wrong you can always go back - to either original data or a previous version.

Validating and auditing data are essential processes to ensure the integrity of FP&A data: Data Validation: This involves checking data against specific rules and constraints to ensure it meets quality standards. It’s crucial for maintaining the accuracy and consistency of the data used in financial planning and analysis. Data Audit: Regular audits help verify the accuracy and completeness of data. They also help in tracking the data lineage, understanding how data is used, and ensuring it aligns with compliance standards.

Debe existir un area especialista que genere información de forma transversal. 1 a muchos o muchos a muchos. Cuando todos quieren información, las areas acostumbran a utilizar formatos y estructuras diferentes, no existe uniformidad y la data se comparte abiertamente, esto puede ser riesgoso. Si tu empresa capacita a todas las áreas en tableu o pBI es un indicio de que todos utilizarán la data como quieran, es un desperdicio de tiempo. Centraliza la función y ordena. 100 informes no incrementarán las ventas :)

In my experience training and education often fall short. The best systems implementations will not be as successful if folks cannot use them correctly. Training should include a hands on component with real world examples. Clicking through a packaged script does little to prepare end users for how they will be using the systems.

Utilisation Pratique des Données : Offrez des formations pratiques sur l’utilisation de l’information comptable pour effectuer une analyse financière et juger de l’impact de différentes méthodes comptables. Outils et Technologies : Familiarisez les utilisateurs avec les outils et technologies modernes qui peuvent automatiser et faciliter le travail, comme les logiciels de planification et d’analyse financière. Apprentissage Continu : Encouragez un apprentissage continu et l’adaptation aux nouvelles technologies et méthodologies pour rester à jour avec les tendances actuelles de l’industrie.

All of the above is critical, but furthermore, to do it in the right order. Two examples, 1) You can't pretend to validate the data if you can't not assure that the data is complete and with enough level of quality. 2) It would be almost impossible to ensure a consistent data workflow without previously defining the data governance, the sources and having the commitment from the data owners to ensure having the data on time and with the adequate level of quality. Strategize your approach to data before building your data set or you will end up wasting more time redoing it than building at once with the right approach.

The key to quality FP&A reporting relies on going 1-2 levels deeper than whatever hypothesis or trend you're trying to test. Deep-diving allows you to have a better understanding on what data can be aggregated, averaged or just removed entirely. It is also important to ensure that data is clean, and manual adjustments are minimal. Where are adjustments are needed, these need to be documented clearly and published into a central database. This ensures everybody works from the same dataset, and has an understanding of what adjustments take place.

One basic rule, which usually broken and effects the data readiness and data quality: you have to save data when and where it is collected! If you delay or transfer the data recording, then the data readiness is broken and the data quality can lowered.