What do you do if your project is facing challenges and you need to use data analysis to solve them?

The more you understand a problem, the more you'll be able to solve it in an effective manner. To get at the root cause, you help yourself by asking questions (the 5 Ws and How), in order to collect as much info as you can, and then try rephrasing the problem in your own words. If the problem seems too complicated, break it into smaller, more manageable parts. The next step could be searching for similar issues that have been resolved before, either within your organization or in external sources, and talk to people who are affected by the problem or might have insights into it.

When your project encounters challenges and data analysis is essential for solutions remember, data analysis is not just about crunching numbers, it’s about deriving meaningful insights that drive informed decisions. ???? Here some steps to navigate the complexities effectively: - Understand the Problem: Begin by thoroughly comprehending the issue at hand. Identify the root cause, gather relevant context, and define clear objectives. This understanding will guide your data analysis efforts.

When facing challenges in a project that require data analysis to solve them, here's a systematic approach you can follow: Define the Problem: Clearly articulate the challenges or issues your project is facing. Identify the specific questions you need to answer or decisions you need to make through data analysis. Relevant Data: Collect all available data that may be relevant to the problem at hand. This could include internal project data, customer data, market research, or any other relevant sources of information. Clean/Prepare the Data: Cleanse and preprocess the data to ensure accuracy, consistency, and relevance. This may involve removing duplicates, handling missing values, standardizing formats, and transforming data for analysis.

The best type of challenges are those that can be explored and analysed using data. Though be careful, the data can become a trap. I've seen many organisations go "Look at all this great data we have, let's analyse it." A lot of effort can be used to find interesting facts though completely miss finding an answer. Instead, start with the challenge and understand it as best you can. Then, ask yourself what questions do we need to answer to understand the problem better or identify possible solutions. With the question in hand, what data is needed to answer the question. Then, and only then, look at what data you have. This will help identify what data you still need. Remember, question first, then what data is needed to answer.

50% of resolving a problems is about getting the problem right. Being able to correctly identify the pain points and root causes is vital for defining a strategy on how to tackle them. If your problem is not concrete enough, or well structure you wont be able to properly identify the cause, and therefore no action plan or amount or data will help you fix it.

Here some steps to navigate the complexities effectively: - Collect Relevant Data: Ensure you have access to meaningful data. Sometimes, the abundance of data can be overwhelming. Focus on collecting data that directly contributes to solving the problem. Data literacy is crucial here—train your team to recognize valuable data.

Some tips when collecting data with the goal of solving a problem: Diversify: Using a variety of sources (surveys, interviews, database) and analysis methods captures a wider range of insights, and gets you a true and fair view. Focus: While gathering data, don't lose sight of your goal and on what is more directly applicable to the challenge you're tackling. Stay Objective: avoid the temptation to select information that merely supports your assumptions. Always try to stay unbiased and try to challenge your preconceptions.

While analyzing data, consider the two approaches: 1. Precision bombing: When you are aware of the problem and looking for the data to support your hypothesis, set the objective clearly. Analyze all data points you have which can support/refute the hypothesis 2. Carpet bombing: When you are looking for the problem, analyze all data points you can get your hands on. Look at all cuts, expected vs actual behaviour, output to input ratios, benchmark to competitors etc. See what the data indicates. Look at the pattern and form a problem statement, supportive data and quantify impact.

Here some steps to navigate the complexities effectively: - Select the Right Tools: Choosing the appropriate data analytics tools is critical. Evaluate options such as Power BI, Tableau, or RapidMiner based on your business objectives, scalability, and integration capabilities. Seek professional advice if needed. - Consolidate Data from Multiple Sources: Data often comes from various channels—website interactions, social media, financial reports, and more. Combining and analyzing data from these disparate sources can be challenging. Leverage data integration tools or management systems to create a holistic view of your data. - Ensure Data Quality: Garbage in, garbage out! Cleanse and validate your data.

That's a pretty typical scenario for process improvement projects. Facing processes that frontline experts never had time or experience using data to explain, we often need to begin with data acquisition which means timing (right! with stop watches), sampling and running EDA. What's more, different industry looks at baseline data in different ways. We use Lean Six Sigma as a guide of our measures.

Here some steps to navigate the complexities effectively: - Upskill Your Team: The demand for skilled data professionals exceeds supply. Invest in upskilling or reskilling existing team members. Keep pace with emerging technologies and advancements in data science. - Build a Data-Driven Culture: Encourage a mindset where data informs decision-making. Train employees to use data effectively. Consider online training programs or coaching workshops. - Prioritize Data Security: Protect sensitive information. Implement robust security measures to safeguard your data during analysis and storage. - Visualize Insights: Communicate findings effectively. Use data visualization tools to present actionable insights for stakeholders.

As you assess each potential solution, progressively eliminate: - those that don't align with your context and constraints - those that lack feasibility - those without a high chance of success - out of those left, chose the one(s) that will have the most impact if successful