What do you do if you need to gather and analyze data as a leader solving problems?

As a leader, we often require a lot of data & also need to analyze it. As an Internal Auditor Leader, collecting data is an important step. You need to have a thorough & structured plan. It is good to check that the data resources are reliable, so that the data received is accurate. In fact, it is always better to cross-check your data from alternative sources. Also, an important thing to keep in mind is to check the date or version of the information received, to ensure it's not outdated or replaced by a more recent version. Analysis & interpretation of data must be objective. It must also be holistic, comprehensive and unbiased.

It's important to figure out exactly what data you need before you start collecting it. Take a good look at the problem and think about it from different perspectives. What are the main things affecting the situation? What kind of information would help you know if you've found a solution or made things better? Talk to the people involved and the experts to really understand the issue. They can help you pinpoint the most important pieces of data to gather. You also need to think about how easy it will be to get the data you need. Once you have a clear plan for what data you require, you can start gathering. Keep in mind, the quality and relevance of the data you collect will directly affect how well you can solve the problem.

Defina o Escopo do Problema: Isso inclui definir claramente os seus limites, em qual parte do processo ou em qual produto ou sistema está sendo afetada pelo problema. A partir da identificação da necessidade, podemos avançar para metodologias de levantamento de dados para geração de diagnósticos e tomadas de decisão com nossas equipes.

Define the Problem: Clearly articulate the problem you're trying to solve and identify the specific questions you need to answer through data analysis. Ensure that the problem is well-defined and relevant to your goals and objectives. Determine Data Needs: Identify the types of data that are relevant to the problem at hand. This could include quantitative data (such as sales figures, customer feedback, or performance metrics) and qualitative data (such as interviews, surveys, or observational data). Collect Data: Gather data from relevant sources, both internal and external to your organization. Use a variety of methods to collect data, such as surveys, interviews, observations, or analysis of existing databases and records.

The structured problem solving methodology the elite strategy consulting firms use is great with identifying the need. First, it has you clearly identify the problem using the Situation - Complication - Question - Hypothesis format. Then, you create a logic tree of assertions under the hypothesis that can be tested with analysis and data. This enables you to focus on gathering and analyzing just the data you need to prove or disprove your hypothesis. It prevents you from “boiling the ocean” and getting distracted by looking for data that is interesting but not essential to figure out the right answer to your problem. If they data says your first hypothesis is incorrect, then you test your next best guess until you find the right answer.

Data sources are crucial for addressing any need. Begin by thoroughly identifying all potential sources, including your own knowledge, input from peers, well-wishers, and friends. Consider outsourcing to third parties if necessary. Above all, prioritize data sources known for accuracy, reliability, and robust data privacy practices to ensure the integrity of your insights.

Here's my approach to identifying and utilizing data sources: Diversify Your Sources: Look beyond internal reports. Explore external databases, industry publications, and relevant surveys. This provides a holistic view of the situation. Prioritize Quality: Not all data is created equal. Assess the reliability, credibility, and timeliness of each source. Ensure the data is accurate, up-to-date, and comprehensive for trustworthy analysis. Embrace Unstructured Data: Don't limit yourself to structured data. Consider customer reviews, social media sentiment, and text-based reports. These can reveal valuable insights.

Once the information need is determined, identifying data sources becomes the next challenge. Sources, whether internal or external, must be reliable and up-to-date. The quality of your analyses and decisions will directly depend on the quality of the data collected

Data collection is a critical step in addressing any need. If poorly executed, it can undermine your entire effort. Prioritize ethical collection methods by ensuring data agents or services respect privacy and adhere to regulations. Additionally, employing automated data collection and management tools can streamline the process and accelerate your journey towards meaningful analysis.

Once the right data source is identified, make the data collection process seamless and integrate it with the core data source. effortlessly collected data releases management bandwidth. automate mundane and augment human, my success mantra.

Data collection is critical. Using multiple data collection methods to gather the information need that can fill in gaps in knowledge. Techniques include interviews, surveys, focus groups, document reviews, and research. Identify key personnel who can provide you with information. Design questions that are a mix of closed and open-ended answers. Find subject matter experts who can provide factual and/or contextual information. Look both internally and externally for different sources than can provide data applicable to the problem you identified.

Data collection is a crucial bridge between identifying sources and analyzing information. To preserve the trustworthiness and quality of the collected data, ensure this process is thorough and respectful of ethical and legal norms.

High-quality data collection is the foundation for effective data analysis. Once gathered, the focus shifts to using that data to achieve your desired outcome. Whether you require detailed insights, or a broader overview will shape the data analysis approach. Modern data analysis tools, including AI, can automate processes and accelerate analysis, but providing clear instructions remains essential to ensure accurate results.

Here's my approach to data analysis for problem solving Clearly Define the Problem: I clearly define the problem I'm trying to solve. This ensures the data I gather is relevant and sheds light on the root cause. Gather Diverse Data Sources: I don't rely solely on readily available data. I actively seek out diverse data sources, both quantitative (numbers) and qualitative (textual feedback) to gain a holistic understanding of the situation. Utilize the Right Tools: I leverage a variety of analytical tools and techniques depending on the data and the problem. This might involve statistical analysis to identify correlations, data visualization to spot trends, or even predictive modeling to anticipate future outcomes.

A key aspect of data analysis is avoiding data fallacies. These fallacies can include: 1) Cherry Picking - selecting only the data that supports your preconceived notion and ignoring data that contradicts it. 2) Data Dredging - keep running tests on the same data set until you find a statistically significant result, even if it's just by chance. (False positives.) To combat data fallacies, look at the source of the data (if it is not your own data set.) Is it credible? Does that person have an agenda? How was the data collected? Look for indicators of conflict between correlation and causation.

The appropriate way to analyze data depends on the kind of data collected, the question you're trying to answer, and your personal comfort with interpreting the results of the analyses. However, it's also important to step back from heavy analyses and think critically about what you're working with. - Do the results you're getting make sense on their face? If not, interrogate whether you're missing something in your understanding of the situation or in your analysis. - Does the pattern of missing data mean anything? If it's not random, why? - Are you expecting the data to answer more/bigger/longer-term questions than it is capable of? Sooth-saying beyond data limitations has its place, but be honest with yourself about when you're doing it.

Decision-making is the critical process where you interpret the insights gained from data analysis. To ensure the best outcomes, prioritize objectivity and minimize bias. Every decision should have a clear rationale and withstand the 'why' test. By repeatedly asking yourself 'why' you challenge your assumptions and reduce ambiguity in the decision-making process.

When possible, It can be helpful to identify the TWO leading decisions (or two variations), and then evaluate them against each other to determine which one is ideal (or generate a new variation).

Resulta clave distinguir la información relevante de información. Parte de la clave de tomar bien las decisiones con datos es jerarquizarlos y saber cuáles de verdad mueven la aguja.

Timely action is critical and when it is backed by data, we position ourselves to success. However, as true leader, one must surely know how to pick up right signals from the noises. And surprisingly, data may also make noises that need validation. In data realm, signals are meaningful patterns and trends. Cross validation techniques help us assess model performance. And finally, taking action is key.

Let me quote from "Management in times of War" by Kostiantyn Koshelenko: "Master emotional combat... 1. Name it. 2. Tame it. 3. Reframe it. 4. Activate the antidote" 4 refers to identifying an activity that triggers the manifestation of an opposite emotion: love/apathy, envy/unity etcetera.

As a leader solving problems, my approach to gathering and analyzing data is structured and thorough. Firstly, I define clear objectives and identify key metrics relevant to the problem at hand. Then, I leverage various data collection methods such as surveys, interviews, and data mining to gather relevant information. Next, I meticulously analyze the data using statistical techniques, visualization tools, and machine learning algorithms to uncover patterns, correlations, and insights. Additionally, I ensure the data is accurate and reliable by validating sources and employing data cleaning techniques. Throughout this process, I prioritize collaboration, involving team members and stakeholders to gain diverse perspectives and foster buy-in.

One important thing to consider is to take people's opinions too apart from just believing the data metrics. You will be amazed to see what a beautiful turn your research can take with the power of experienced opinions. This unfolds some unseen aspects of your research. Good leaders can mentor you to become the best leaders!

As a leader, you always want to be analyzing data and solving problems. I actually prefer to see a leader as a solution finder more than as a problem solver. Effective leaders should be able to assess multiple issues across dimensions in a 360 scope and readily assign the best person/team to address the most effective solution/s. Training teams to implement solutions is also key, so the teams don't expect the leader to be the sole keeper of solutions. As a practicing psychotherapist and executive coach, I always made the point to help my patient or client to be their own solution finder. Leaders should also focus on finding areas of unmet needs to continue with an eye of innovation into the future.