What is the best way to identify relevant data points for problem-solving?

Thinking out of the box always leads us to the higher layer of the problem. For example, maybe the question is “How we can go faster in this route?”, but the answer is “Going through another route is the fastest one”. Originally, the question must be “Which route is the fastest?”

To identify relevant data for problem-solving, define the problem and potential causes. Collect accurate and relevant data from various sources, like historical data, surveys, and observations. Analyse the data for patterns, trends, and potential solutions. Adjust your approach as new information arises. Follow these steps to solve problems using data effectively.

In my experience, one of the pitfalls that people often fall into is not utilizing an interdisciplinary approach to define what the problem is. In healthcare, all parts of a system work together and different components of the system will likely have different perspectives on why something is happening. Using input from various operational components will ensure the accuracy of defining the true problem, any challenges, and potential barriers to improvement-all of which are essential in continuous improvement.

After defining the problem, ensure that you also identify the stakeholders involved. Understanding their needs or pain points can offer clues about which data points might be particularly relevant.

Begin with the end in mind! Once you establish the goal of the analysis, it is easier to sort out unnecessary data. So often, time is spend going through multiple analyses and data points in hopes of an answer just emerging. Be clear on your expectations and deliberate in your approach.

In the realm of problem solving, meticulous preparation in the problem definition stage not only sharpens the focus on the issue but also serves as a strategic compass for determining the precise data requirements. This process aids in discerning the type of data that needs to be collected, specifying where and how to obtain it. Consequently, a well-prepared problem definition lays the foundation for an effective and efficient data collection strategy, streamlining the subsequent steps in the problem-solving process.

It’s essential to explore emerging technologies and data collection methods. In today's rapidly evolving landscape, leveraging advancements like machine learning algorithms for predictive analytics or tapping into social media sentiment analysis can provide a more comprehensive understanding of the problem. Integrating innovative data sources can enhance the depth and accuracy of your analysis, contributing to more informed decision-making.

En mi entorno laboral, cuando se requería obtener información detallada, nuestro proceso implicaba contar con una base sólida que comenzaba con la resolución ministerial emitida por el Estado. Luego, procedíamos a identificar el registro administrativo en el sistema de administración financiera. A partir de este punto, se establecía la conexión entre los ingresos y los gastos, siguiendo las directrices establecidas en la resolución. Estos gastos estaban destinados a cumplir con el propósito original de la resolución. Este enfoque no solo nos permitía mantener una conformidad precisa, sino que también facilitaba la detección y corrección de desviaciones, ya sean de naturaleza registral o de ejecución.

In my experience, understanding the reliability and comparability of the data has been a critical step in solving problems. Additionally, determining the appropriate scope, such as time frame and completeness, is critical.

the process of identifying relevant data points and sources is a critical step in problem-solving. It involves a clear understanding of the problem, identification of key variables, and selection of appropriate data sources

Resulta esencial poseer un entendimiento profundo de los datos disponibles en el sistema de la entidad. Este conocimiento no solo nos permite gestionar una base de datos y obtener un análisis preciso de la información en cuestión, sino también nos habilita para responder futuras consultas con datos pertinentes y valiosos.

As a data enthusiast, I know that collecting information is just the beginning! Once you've gathered data, the real magic happens when you filter and organize it. That means saying goodbye to irrelevant or inaccurate bits. How? Well, think spreadsheets, databases, or cool software tools. These help sort, categorize, and label the data according to what you need. Mastering these methods makes data analysis a breeze and ensures spot-on decision-making.

When handling data organization and outlier recognition is very important when identifying a pattern. Visual representations of data assists with this organization. Some visualization techniques such as charts, graphs, and diagrams are especially helpful when identifying patterns, trends, and outliers more effectively. Having specific parameters, like an operational definition, is also useful for filtering data.

This stage is crucial for ensuring the integrity and relevance of your data, which directly impacts the quality of your insights and the effectiveness of your solutions. Proper documentation and adherence to ethical standards are also essential throughout this process.

Adopting a mindful approach to data governance is crucial during the filtering and organization phase. Establish clear protocols for data security and privacy to ensure compliance with regulations and protect sensitive information. By implementing robust data management practices, you not only enhance the accuracy of your analysis but also foster a foundation of trust in handling data, which is particularly vital in today's data-centric environment.

Keep in mind that the most relevant data points for problem-solving might change as the problem or environment evolves. It's a good practice to periodically revisit and re-rank data as more is learned about the problem.

Focus on the things that matter, such as 20-80 rule: “ 80% of outcomes (or outputs) result from 20% of all causes (or inputs) for any given event”

Evaluating data points based on relevance, urgency, or value is necessary. Use diverse criteria and methods such as scoring systems, matrices, or charts, assigning weights and ratings accordingly to acheive this. Leveraging ranking algorithms, rules, or preferences helps to arrange data based on priorities you decide on. Mastering these techniques helps with precise decision-making and drives impactful solutions.

The critical part of this is to know how much deep I want to go with the solutions. Many problems are not the problem but symptoms of a big problem. If you do not know the big problem (if you do it is another approach) do not get focused in the problem you see by itself, look for the problems related. The aim to find the real problem, when you understand and see the real problem, you can start to find solutions for each symptom. At that point, data talk on its own... the pattern is the key If you are not interested in the big problem because whatever reason, it does not matter the solution because it will be a patch anyway. In that case, do as some people do, make your patch sounds the best as possible for you to get a promotion.

Involve key stakeholders in the prioritization process. Collaborating with individuals who have a vested interest in the outcome ensures a more comprehensive understanding of the broader implications and potential consequences of different data points. This participatory approach not only brings diverse perspectives to the prioritization but also increases the likelihood of addressing critical issues that might have been overlooked in an individual assessment.

I've seen the best analysts sense check their insights by involving their business stakeholders during the analysis process to incorporate additional context they don't have in the data. Is that really a spike in sales or is it just expected activity from a promotion? This approach helps to isolate the valuable insights within the data while building the analysts domain knowledge and business acumen. It helps to build trust and credibility with business stakeholders because they have a personal connection to the outcome by being involved in the analysis. They also have the additional confidence that the Data and Analytics team have a real understanding of the customer and business outcomes through effective collaboration.

In the realm of data analytics, this pivotal step should be oriented toward crafting a compelling narrative that not only conveys the problem but also captivates the audience during the presentation. Data, in isolation, remains mere numbers and figures devoid of significance. Therefore, the focus should be on imbuing each data point with context and meaning to transform raw information into a coherent and engaging storyline, ensuring that the data serves as a powerful tool for comprehension and decision-making.

Consider fostering a culture of continuous learning within your team. Encourage members to share their diverse interpretations and insights gained from the data analysis process. This collaborative approach not only enriches the overall understanding but also promotes a dynamic environment where collective intelligence contributes to more robust problem-solving outcomes. Emphasizing ongoing skill development ensures that your team remains adept at navigating evolving analytical challenges in the ever-changing landscape of data interpretation.

Once you have filtered and organized the data, you need to interpret and synthesize the data to identify patterns, trends, and relationships. This may involve using data visualization tools, statistical analysis tools, and machine learning algorithms.

Cultural and regional differences can influence data. For global projects, consider the cultural context of the data points being used to ensure accurate interpretation and relevance.

Consider historical data, patterns, and trends, while being open to new, unconventional data sources that may offer fresh insights. Collaborative brainstorming with diverse perspectives can also unearth valuable data points that might otherwise be overlooked.

As with so many things, trust is vital when solving a problem. People will sometimes be reluctant to be honest out of fear that they will be held responsible. This is where you need to always be building a growth culture where we learn from our mistakes, grow, and move on. Without this trust, it can be difficult to get to the root causes and, for that matter, build a healthy organization.

As an experienced auditor, the best way to identify relevant data points for problem-solving is to employ advanced data analytics techniques, leveraging statistical methods and technology tools to sift through large datasets and extract meaningful insights efficiently. This can later be customized using tools as Power BI, data visualization depending on the purpose.

Within the problem definition phase, it's imperative to identify the key stakeholders involved in the process. This identification is essential because we must collaborate with these individuals throughout the problem-solving journey. They play a pivotal role in data collection, analysis, solution discovery, and presenting the final results to the relevant and interested audience. Recognizing and engaging with these key participants is fundamental to a seamless and effective problem-solving process.