What are the best strategies for managing complex business problems with data?

Defining the problem clearly entails thoroughly elucidating the specific issue or challenge at hand, including its scope, underlying factors, and potential implications. It involves providing detailed context and background information to ensure a comprehensive understanding among all stakeholders. This clarity enables effective problem-solving strategies to be developed and implemented, fostering consensus and alignment towards finding viable solutions.

Companies should adopt a coherent data strategy that balances defensive (security and governance) and offensive (predictive analytics) data management practices. In Operations, data can optimize processes and enhance efficiency. For Marketing, data-driven strategies can personalize customer experiences and improve campaign effectiveness. In Finance, data analysis aids in forecasting, risk management, and decision-making. Key practices include defining clear data management protocols, using data for behavioral modeling, and forecasting outcomes, overcoming challenges like maintaining high-quality data and avoiding data silos, and leveraging data to optimize processes and improve efficiency.

To manage complex business problems with data, start by clearly defining the problem and identifying key metrics. Use data visualization to understand trends and patterns. Employ statistical and machine learning models to analyze data and predict outcomes. Break down the problem into smaller, manageable parts and tackle each with targeted data analysis. Ensure data quality and consistency for reliable insights. Foster a data-driven culture within the organization, encouraging data literacy and collaborative problem-solving. Continuously monitor outcomes and adjust strategies based on data feedback. Leverage insights from data to make informed decisions, optimizing processes and addressing business challenges effectively.

The effective analysis of complex problems with data requires the identification of their root cause. To accomplish this, it is necessary to engage in a thorough analysis of the inputs and processes involved, aligning them with the desired outcomes to gain insight into the problem's source. By doing so, one can gain a comprehensive understanding of the issue at hand and develop informed solutions to address it.

It is important whether simple or complex to first define the problem and understand the context. Look for dots that connect with the problem by identifying the stakeholders that are involved in the problem.

Checklist for breaking down a word problem. 1. Read the problem 2. Highlight the questions you need to answer 3. Underline essential information 4. Cross everything out you don’t need to know 5. Pick a strategy 6. Solve the problem 7. Show your work

Decomposing a complex problem into smaller, more manageable chunks is very helpful. Study the problem to find recurring themes or common characteristics that help to group smaller parts into categories. Then arrange those categories into priorities and identify dependencies. This approach allows you to put first things first and understand how they tie together. You should also try to objectively quantify the importance or value of each part/category to help communicate what's important and why. You can use numerical or categorical measures such as 1-5 or high/medium/low, respectively. As long as everyone agrees on the scale used, it will work well. We recently accomplished this prioritization using a dot-voting system with stickers.

Identify the Problem: Clearly define the overarching problem or challenge you're facing. What are the symptoms of the problem? What are the underlying causes? What are the potential implications for the business? Define Key Objectives: Break down the main problem into specific, measurable objectives that you aim to achieve. These objectives should be actionable and aligned with the overall business goals. Identify Contributing Factors: Analyze the factors contributing to the problem. This may involve examining data, conducting interviews, or consulting subject matter experts to understand the root causes and underlying dynamics

-Understanding the data flow -breaking down to the transaction level -Understanding the source - digging out for the supporting documents - finding patterns, trends, comparing to previous data

For data driven solutions one should first validate the source of data like machine generated/collected by person then go through it birds eye view and accordingly preprocess and clean the data. After performing with different algorithms, the best outcome must be validated with tools and compare with the required goal. Involve the stakeholders from concerned dept to ensure alignment and consensus on the approach and outcomes. The approach should be stay agile and flexible in approach to adapt to changing circumstances and emerging challenges and feedback, finally one must ensure that the models generalize well to unseen data and provide actionable insights.

Internal datasource, external datasource, analytical methods, data collection methods and data integration & enrichment By carefully selecting data sources and methods aligned with your objectives, you can effectively analyze complex business problems and derive actionable insights to drive decision-making and business outcomes

Two Way Communication - In word problems, the communication is typically one way. The student reads the question being given and they communicate the answer once they have a solution. With business problems, we want to communicate early and often. The chances for misunderstanding a portion of the ask are high, so it’s important that we communicate with the stakeholders throughout the solution process to ensure a successful outcome.

True , but visualization should be for correct variables. Their correlations, group by, cross tabulation, Bivariate analysis and multivariate Analysis plots should be translated in word in correctly. Only variable related to understanding of problems and those which have potential to give solution should be plotted rather to create junk and confuse.

Choose the Right Visualization Type: Select visualization types that best represent the relationships and patterns in your data. Common types include bar charts, line charts, scatter plots, histograms, pie charts, heatmaps, and geographic maps. Simplify and Focus: Keep visualizations simple and focused on the key insights you want to communicate. Avoid clutter and unnecessary elements that distract from the main message. Use Clear Labels and Titles: Clearly label axes, data points, and categories to provide context and aid interpretation. Use descriptive titles and captions to summarize the main findings or conclusions.

Define Success Metrics: Establish clear metrics and key performance indicators to measure the success of the implemented solutions. These metrics should be aligned with the objectives defined earlier in the problem-solving process. Collect Feedback: Gather feedback from stakeholders, end-users, and other relevant parties to understand their experiences and perceptions regarding the implemented solutions. This feedback can be collected through surveys, interviews, focus groups, or direct observations. Monitor Performance: Continuously monitor the performance of the implemented solutions against the defined success metrics. Use data analytics tools and dashboards to track key indicators and identify any deviations from expected outcomes.

Start by reading the problem. Move on to identifying the parts of the problem. Highlight the question to answer: “How many apples does she need?”. Underline essential information: “4 friends, 3 apples for each of them”. And then cross out everything we don’t need to know. From there it is pretty straight forward. To solve this problem, we need to use multiplication. If we have 4 friends and they each need 3 apples (4x3) then we need 12 apples total. Simple! This is the type of problem that we are now able to solve without a checklist, but when word problems were new to us, these strategies helped tremendously.

Understand the business requirements and end goals needed by the C-suite or the stakeholders. This will help the data engineers to better lookout for the data points relevant to the requirements.

Lo principal es identificar el problema que se quiere resolver. Con el problema claro se pueden identificar con mayor nivel de certeza los modelos de analítica a aplicar acorde a las necesidades, entender los datos que se necesitan, así como los stakeholders involucrados. También es importante tener claridad sobre el problema, ya que no todo en las empresas es cuantitativo. Si encontramos que en el análisis de causa raíz los factores son de tipo cualitativo, es probable que se necesiten metodologías que no están del lado estadístico. Por último, no olviden alinear sus modelos de analítica con el modelo de negocios a través de OKRs o BSC. Espero sea útil.