Que faites-vous si l’analyse des données de votre organisation ne parvient pas à identifier et à atténuer efficacement les risques ?

Reassessing your primary goals are fine but it is also an opportunity to study what led to that failure and if there is a trend / insight that conflicts the usual trend which has always been perceived as the crux. Easiest problem to fix should be plugging the data source, tool upgrade and have better brainstorming among the staff. Studying the outliers could be equally fascinating and may be rewarding as well!

If your organization's data analysis isn't identifying and mitigating risks effectively, take a strategic approach. Start by validating your data for accuracy and completeness. Reassess your analytical methods and tools, and consider adopting more advanced techniques or integrating new technologies. Invest in team training to enhance data analysis skills and consult industry experts for insights. Implement robust monitoring systems for real-time tracking of key risk indicators, and foster cross-functional collaboration. Conduct root cause analysis to address gaps. Strengthen your risk assessment framework with both qualitative and quantitative methods, and use predictive analytics. Regularly update risk models to reflect new data and trends

It’s essential to take proactive steps to enhance risk management. Below are few ways I use to mitigate risk 1) Identify data vulnerabilities and potential risks. Categorize data into sensitive data and PII to prioritize protection efforts. Apply encryption to safeguard sensitive data 2) Control who can access data by implementing user authentication 3) Make sure to follow local rules and guidelines and ask for user consent before processing data 4) Keep ready a response plan to minimize the impact of any breach 5) Lastly, monitor new threats and make necessary changes by incorporating feedback

When data analysis isn't catching risks, follow these steps: Evaluate if your objectives are clear, specific, and aligned with the organization's broader goals. Verify if what you're analyzing matches what you should be monitoring. Make goals SMART like Clearly define what you want to achieve. Measure Quantify objectives for progress tracking. Ensure goals are realistic and attainable. Align objectives with organizational needs. Set specific deadlines for goal achievement. Adjust your analysis to concentrate on high-risk areas. Continuously review and refine your approach to ensure effective risk identification.

É necessário mapear os parâmetros que serão registrados e em seguida obter uma base de dados confiável e representativa. Uma boa análise de dados começa por tirar uma “fotografia” dos parâmetros, entender o comportamento da variáveis para em seguida filtrar para uma análise mais profunda. Se a base de dados não for bem confiável e representativa, sua análise não será boa. Você perderá tempo e dinheiro.

data analysis depends on the objective set in the beginning. Statistical analysis can be 'garbage-in-garbage-out', so we need to reassess the 'smart' objective, whether it was really smart enough. and, the methods followed to achieve the goals. thus, reassessing goals is a good way to analyze why the data analysis is not showing what it was supposed to do.

If an organization’s data analysis fails to identify or mitigate risks effectively, it is crucial to review the entire process. Examine methodologies, tools, and data sources to identify weaknesses, and assess data quality to ensure accuracy. Reevaluate assumptions made during the analysis for validity. Transparent communication with stakeholders about the failure and its implications is essential. Gather feedback from employees and experts to understand the issues. Use this feedback to improve analytical capabilities and develop a contingency plan with risk mitigation strategies and continuous monitoring.

If your organization's data analysis fails to identify and mitigate risks effectively: 1. Review and refine the analysis process. 2. Re-run the analysis with a different methodology or additional data. 3. Consult with experts and enhance data quality. 4. Use multiple risk assessment techniques and consider alternative frameworks. 5. Develop a risk monitoring plan and communicate with stakeholders. *Remember to document lessons learned and continuously improve the process to prevent similar issues in the future.

Na minha opinião, uma auditoria completa dos dados é essencial. Verifique a qualidade, a integridade e a relevância dos dados utilizados nas análises. Identifique possíveis lacunas ou inconsistências e tome medidas para corrigi-las.

Data Model Reassessment: A well-designed data model is the foundation for successful data analysis. If your current model isn't capturing the right data elements or relationships, it could lead to blind spots in your risk identification. Re-evaluate your data model to ensure it aligns with your risk management objectives and captures the necessary data for comprehensive risk assessment. Data Retraining: As your organisation and the risk landscape evolve, your data models may need to adapt as well. Retraining your data with new or updated information can help improve the accuracy of your risk predictions and ensure your models stay relevant over time.

I think it is good to ensure that the data which is used for analysis is accurate, complete and consistent, as it is critical for effective risk identification. Auditing the data can help to identify any outliers or anomalies which were not recognised earlier, which in turn led to misinterpretation of the risk. The result of the data analysis must be verified to ensure they align with actual organizational performance and risk exposure. It would help in identifying weaknesses in data collection and processing workflows, allowing for targeted improvements. By systematically auditing the data, the organization can uncover critical insights, improve data quality, and support the overall risk management efforts more efficiently.

Review and Audit the Data Analysis Process: Ensure data quality by verifying accuracy and completeness. Assess the appropriateness of statistical methods and validate models using techniques like cross-validation to confirm robustness and reliability.

In my opinion, when data analysis fails to effectively identify and mitigate risks, a comprehensive audit of the datasets is imperative. This should involve deploying data profiling and anomaly detection techniques to uncover any gaps, inaccuracies or inconsistencies that may have been overlooked. Validating the integrity and reliability of data sources through data lineage tracking is essential to ensure that the data is timely, accurate and relevant to the risks being analyzed. Additionally, implementing rigorous data quality assurance processes, including data validation rules, data cleansing algorithms, and standardization protocols, can ensure the dataset is robust and reliable, ultimately enhancing the accuracy of risk identification.

Data Validation: Implement rigorous data validation and cleansing processes to ensure accuracy and completeness. Data Sources: Re-evaluate and, if necessary, diversify data sources to ensure a more comprehensive risk assessment.

1. Auditing data with enhanced use of tools will definately provide an opportunity to improve the process and gain more knowledge. 2. Team members can also be indulged to gain more insights.

En la auditoría de datos es importante revisar el corte de la data utilizada. Por un lado para análisis estadístico, mientras más amplia la data es mas consistente el modelo. Pero por otro lado hay que asegurarse de que la data pertenezca al mismo ciclo que se quiera estudiar. Es decir aislando cortes que ya no explican la realidad actual y observaciones atípicas.

Assess the current data analysis processes to identify potential gaps. Look for issues in data collection, analysis methodologies, or interpretation of results.

Like they say, "Gabbage in, Gabbage Out". It's necessary that the quality of data used for your organization analytics is audited in order to establish its quality, accuracy, completeness and releveance. It may also be wise to diversify data sources to ensure the data used is dynamic and represents a complete subset of your variables. It is also important to ensure that the data used is obtained from reliable sources as opposed from assumptions.

Upgrade Analytical Tools and Techniques: Utilize advanced analytics such as machine learning and predictive analytics. Implement real-time analytics to detect and respond to emerging risks promptly.

When an organization’s data analysis fails to identify and mitigate risks, upgrading analytical tools is essential. Modern platforms like Power BI transform data handling, offering key advantages: 1. Data Visualization: Transforms datasets into visual formats, making it easier to identify hidden risks. 2. Real-Time Analysis: Provides access to current information, crucial for timely risk mitigation. 3. Integrated Data Sources: Consolidates data from various sources, revealing patterns and trends. 4. Analytics and AI: Detects anomalies and predicts trends, enhancing risk management. 5. User-Friendly Interfaces: Makes data accessible to non-technical staff, promoting a data-driven culture.

To improve risk identification and mitigation, it's essential to enhance data analysis techniques. This may involve: Advanced Analytics: Implementing more sophisticated analytical models such as machine learning algorithms for predictive analysis can uncover hidden patterns and potential risks that traditional methods might miss. Integration of Data Sources: Ensuring that data from all relevant sources across departments or business units is integrated and analyzed together provides a holistic view of organizational risks. Real-Time Monitoring: Setting up systems for real-time data monitoring allows for proactive risk identification and immediate response to emerging threats. And at last Continuous Improvement is the key.

In the field of IT, adaptability is essential. I make it a point to quickly adopt new technologies as they become available. I have extensive experience using Python and working with numerous libraries. Therefore, I am currently in the process of upgrading my skills.

It's important to ensure that the tools used for analysis within the organisation are advanced and up to the market trends. This helps to accomodate complex anaytics while also deploying modern methodolgies of analysis like machine learning and AI. It also ensures that all analysts within your workspace are accomodated in terms of their generations and adaptability to any kind of analytical environment.

With the wave of Gen AI and LLMs, my team has been very efficient and productive. They get these models to ask relevant questions. Now, they have automated part of their data analysis workflow. Not only are they able to do quality data analysis project, they have learnt a lot with this assistance.

If a cybersecurity team's current data analysis tools are inadequate, they may struggle to effectively detect and respond to emerging threats. For instance, relying on basic intrusion detection systems (IDS) that primarily focus on signature-based detection might overlook sophisticated, zero-day attacks that exploit unknown vulnerabilities. By upgrading to advanced threat detection software capable of behavior analysis and anomaly detection, the team can better identify unusual patterns indicative of potential breaches. This upgrade enhances their ability to proactively mitigate risks by promptly detecting and responding to new and evolving cyber threats before they escalate into significant security incidents.

Actualizar las herramientas de análisis puede ser crucial, pero, también es importante que los conocimientos y experiencia en esta ciencia estén actualizados. Herramientas actualizadas con capacidad de machine learning y análisis predictivo permite manejar grandes volúmenes de datos y descubrir patrones ocultos; plataformas de Big Data y tecnologías como Hadoop o Spark ayudarán a lograr un procesamiento de datos a gran escala; y, plataformas y software como Tableau y Power BI pueden facilitar la visualización y comprensión de datos complejos. Así las cosas, la inversión en herramientas y su implementación de la mano de conocimiento apropiado, contribuirán a la detección precisa y rápida de riesgos, mejorando la capacidad de respuesta.

Ter ferramentas para análise de dados é fundamental, porém os dados precisam estar alinhados às diretrizes da organização. Assim os gaps identificados terão tratamento mais acertivos.

Invest in Training and Development: Provide ongoing training for data analysts and risk management teams. Encourage continuous learning and improvement within the team.

Organisation fails only if staff fails. So train people for effectiveness. Provide them enough tools to overcome any issues.

É natural que em uma equipe haja níveis diferentes de conhecimento e experiências. Treinamentos podem ser necessários para que haja um nivelamento. Outro fator que pode contribuir é a interação contínua entre os membros do time. A troca de conhecimento entre os componentes pode acelerar o processo de aprendizagem, tanto na parte técnica quanto nas soft skills.

Yes, it is important to provide ongoing training for staff on the latest data analysis techniques and risk management practices. Hire experts when needed! Bring in data scientists or risk management experts to enhance the team's capabilities.

Offer training programs for staff on data analysis techniques, risk management, and the importance of data-driven decision-making to improve skills and awareness.

It's important to invest in upskilling and reskilling of your staff establishment from time to time. This ensures that your staff stay up to date with new methodologies and deliver the most complex projects. It also helps your team to adapt to new methodologies without compromising the quality of work they deliver.

Over the years, I've have seen this mistakes among juniors in our firm, best thing we do is recollecting the data and specific to the problem. We ask good questions. It'll surprise you that, these juniors fails not because they do not have the skill. Wrong question, wrong answer. I have been teaching them how to ask good data driven question. This has helped them arrived at the right answer.

La capacitación deber ser una práctica constante para que una organización esté preparada para nuevos desafíos, evitando que nuevas contrataciones deterioren el ambiente laboral al ser asumidas por el equipo de trabajo como un desconocimiento de habilidades y desplazamiento en los roles del proyecto. Es recomendable la formación en técnicas avanzadas (machine learning, análisis predictivo, minería de datos, estadística avanzada y técnicas de modelado), lenguajes de programación (Python y R); y, por supuesto, el uso de SQL para gestión de datos; lo que mejorará significativamente la capacidad del equipo para identificar y mitigar riesgos. Por lo tanto, la inversión en capacitación asegurará la mejora continua y un equipo eficaz y eficiente.

This failed mentioned above is human error and the best way to avoids such as things is to train the staff in the first place and then upgrade the tools

Reviewing your data analysis processes to ensure they effectively identify risks is crucial. Process inefficiencies can lead to missed opportunities for risk detection. For instance, a company might discover that their analysis does not account for all relevant variables, leading to inaccurate results. By reviewing, we can find any data updates in the dataset that have not been refreshed within the system. Regularly updating and validating data examination procedures can help address these gaps, enhancing risk management efforts.

Acredito que a maneira mais eficaz para atuar nessa situação seja o envolvimento de todos os stakeholders, revisando todas as etapas correntes do processo. A simulação falada de todo o desenrolar do processo pode destacar diversos pontos de atenção.

I believe that reevaluating the analytical methodologies and algorithms is crucial to ensure they are suitable for identifying and mitigating specific types of risks. This should include conducting a thorough process mapping and bottleneck analysis of the entire data analytics workflow to identify inefficiencies and streamline processes, thereby improving the accuracy and speed of risk identification. Leveraging stakeholder feedback sessions and root cause analysis can provide valuable insights into the current processes, helping to refine the analytical approach in a way that addresses the real needs and concerns of all stakeholders.

Renovarse o morir! Conseguir que los datos que registramos sean fiables y nos aporten valor requiere de un ejercicio de revisión continuo, planificado y orientado. Eso nos ayudará a que nuestro indicador sea cada vez más exacto.

Revisar y optimizar los procesos de análisis de datos es esencial, para lo cual, es recomendable: 1. Establecer un marco de trabajo ágil y flexible para la revisión continua y ajuste de los procesos; 2. Implementar metodologías como CRISP-DM (Cross-Industry Standard Process for Data Mining) para estructurar y guiar el análisis de datos, 3. Automatizar tareas repetitivas con scripts y flujos de trabajo programados para mejorar la eficiencia. Lo anterior, teniendo como base fundamental que la revisión periódica y la incorporación de retroalimentación mejoran la precisión del análisis, permitiendo la identificación temprana y efectiva de riesgos.

Review your data analysis processes: 1. **Understanding the Problem**: - Define the objective of the analysis. - Understand the context and the specific questions to be answered. 2. **Data Collection**: - Gather data from relevant sources. - Ensure the data is reliable and relevant to the problem at hand. 3. **Data Cleaning**: - Handle missing values by imputation or deletion. - Correct inconsistencies and errors. - Standardize and normalize data if necessary. 4. **Documentation**: - Document the entire process, including data sources, methods used, assumptions made, and conclusions drawn. This structured approach helps ensure thorough and accurate analysis, leading to meaningful and actionable insights.

This involves a thorough examination of the existing data analysis workflows and methodologies to identify any gaps or inefficiencies. By reassessing how data is collected, processed, and analyzed, I can pinpoint areas that need improvement. I would look at the data sources to ensure they are comprehensive and reliable, and evaluate the analytical models to confirm they are appropriately designed for the specific risks we aim to mitigate. Additionally, I would involve key stakeholders in this review to gather insights and ensure the revised processes align with the organization’s objectives. This proactive approach helps to refine the data analysis framework, enhancing its accuracy and effectiveness in identifying and managing risks.

Processos que não geram dados em quantidade ou qualidade suficiente para uma análise podem ser melhorados a partir de estratégias como: - criação de validações, ex: não sabemos quem é responsável pela falha de um processo, adicionamos uma validação de qualidade na passagem de responsável, indicando o status final daquela etapa intermediária do processo; - visão segmentada dos agentes responsáveis por um processo, isto pode revelar se o processo é ruim para todos ou somente para parte dos agentes; - visão segmentada dos inputs do processo, eventualmente tratamos igualmente todos os inputs (ex: usuarios de um app) do processo, mas ao segmentarmos (ex: canal de aquisição) podemos notar que existem perfis de risco diferentes.

Data Collection Identify and gather data from reliable and relevant sources. Quality ensure data quality by checking for accuracy, completeness, and consistency. Data Cleaning and Preparation** Remove or correct any inaccuracies or inconsistencies in the data. Transform and format data for analysis, ensuring it is in a suitable structure. Risk Identification Techniques Statistical Analysis Use statistical methods to detect anomalies and trends. Trend Analysis Examine data over time to identify emerging Compare data sets to find discrepancies or unusual patterns. Detailed Analysis conduct a thorough examination of the data to uncover hidden patterns. -

Yes you must take a second opinion as you would do it for a prognosis of your health issues. Data analytics is ever evolving and you need to collaborate to learn new things or change the course if your actions

Effective risk management requires collaboration across departments and clear communication of findings. As a data analysis expert, fostering a culture where data-driven insights are shared and acted upon is essential. Regular meetings involving stakeholders from different functions can ensure that risk analysis findings are understood and integrated into decision-making processes.

A cybersecurity team collaborates with the finance department to analyze phishing attack trends targeting financial transactions. By pooling their expertise, they identify a pattern of fraudulent emails mimicking vendor payment requests. This collaboration leads to the implementation of enhanced verification procedures for financial transactions, significantly reducing the organization's susceptibility to financial fraud risks associated with phishing attacks.

There is need for reassessment, collaboration and retraining for proper data analysis. Data analysis requires teamwork. Here are some steps to avoid this risk: Establish Clear Objectives: Ensure that the goals and objectives of data analysis are clearly defined. Data Governance: Implement a strong data governance framework. Centralize Data Sources: Use a centralized data repository to avoid discrepancies. Improve Communication: Facilitate regular communication and collaboration between data analysts. Training and Education: Provide ongoing training for employees. Regular Audits: Conduct regular audits of data processes and analysis. Clear Documentation: Maintain comprehensive documentation.

The burning platform for change or organisational action is often an external threat or movement in the market. Collaborating with different internal departments and also external industry partners helps provide a better context of the risk/opportunity. Data analysis can then help leaders understand the current trend and the size of the change required to mitigate a risk or create a competitive advantage in the market.

Bringing in individuals with diverse perspectives, expertise and experience is essential for vetting your data analysis procedures and outcomes. What is hidden to me might be glaringly obvious to them.

Diferentes pontos de vista contribuem para o processo de construção. Muitas vezes os responsáveis pelo desenvolvimento, por estarem imersos no processo de construção, não percebem detalhes que podem impactar o resultado final. Criar uma rotina de feedbacks dentro do projeto pode auxiliar para que novas ideias sejam geradas.

Collaborating more can significantly help as different viewpoints enhance risk identification. Pooling expertise improves analysis techniques. Better data quality as it comes from multiple sources. Collaboration fosters creative solutions and better decisions. With clear communication risk information is promptly shared. And lastly, accountability ensures risks are addressed properly.

Hold a bi-weekly meeting for all group members to collaborate openly and freely with trust and kindness. This fosters creativity and prolongs effective cooperation.

Start all over again. Fire everyone. Delete your database and everything in excel. Start over and do it right this time. Simple as.

A primeira coisa a entender é: qual é o seu risco? O que você deseja prever? É atraso? Perda de clientes (churn)? Entender seu objetivo é o primeiro passo, pois dele podemos desbravar ideias de indicadores antecedentes (early indicators). Se definir esses indicadores antecedentes for difícil, devido ao desconhecimento da causa, um modelo preditivo pode ajudar. Modelos preditivos podem ajudar a entender os padrões e variáveis que indiquem que o risco ocorrerá. Se isso não ajudar, faltam dados. É possível comprar scores de mercado que ajudarão a ter mais informações sobre seus clientes e assim ajudar a mitigar riscos.

Enhance Data Collection and Sources: Incorporate additional data sources for better insights. Improve data collection mechanisms to capture more relevant and detailed information.

Engage stakeholders from different departments (e.g., finance, operations, IT) to gain diverse perspectives on risk and enhance the analysis process. Also, we should develop a comprehensive risk management framework that incorporates continuous monitoring and iteration of risk analysis processes.

Lastly, continuous improvement is key. Conducting regular reviews and audits of the risk management processes themselves ensures that methodologies remain relevant and effective. This iterative approach allows organizations to adapt to evolving threats and regulatory changes effectively.

Implementing scenario analysis and Monte Carlo simulations to test the impact of different variables and conditions on risk, along with regular stress testing, can evaluate the organization’s resilience to extreme risk events, highlighting vulnerabilities that might not be apparent in routine analysis. These methods provide a comprehensive view of potential risks and their impacts, allowing for more informed decision-making. Additionally, incorporating a risk governance framework establishes clear accountability and oversight mechanisms, ensuring consistent application of risk management practices aligned with the organization. This approachenhances risk identification and strengthens the organization’s overall risk resilience.

There's no perfect Risk Management Program. In risk management we can only work continuously to identify, ahead of time, inherent risks, put controls in place & address residual risks to the point that is "as low as reasonably practicable". We should also mitigate risks and the low or insignificant the risk, we can accept (low probability with low impact). We take on risks in consideration of the rewards weighed against our risk appetite. Risk may present opportunities. A red flag is if this risk of loss was clearly foreseeable or avoidable. Worse if it is not a first time occurence and risk mitigation measures are not in place (ex. poor risk identification - data ware house built in a flood prone area). A high priority issue.

Whenever there is a deviation to a standard or a performance gap, there are two questions that must be answered. The first is to determine why the deviation or gap occurred and the second is to understand why the deviation was not detected early enough In both investigations, formal methods of investigation can be followed to arrive at the answers. My favourite is to use a combination of 5 whys and 8 disciplines. The practice of forming a team and solving the standard deviation or performance gap together has proven to be a very effective tool of Kaizen and TQM. The methodology never fails to identify the true root causes of the problem and it leads you to identify and permanently correct problems/issues.

There are arguably two higher level issues to consider - does your organisation have a proper understanding of risk (both the things that could go wrong but also what could go well)? And are the right questions being asked of the data? There is every possibility that the data & analytical tools being used and the people & processes involved are the right ones but the inadequate understanding of risk and poorly framed questions have led to the suboptimal outcome.

When I coached my team on gen AI, the first year was very fantastic. Developing objective functions for the team help them write good prompt for their work. Now, I have a team that I trust, one, they do not rely on gen AI. But follow the human leadership.