You have a lot of business data to analyze. How can you do it efficiently?

Efficiently analyzing business data involves maintaining up-to-date records, filling data gaps when possible, and leveraging tools like Microsoft Power BI for comprehensive visualization, aiding effective communication with team members and stakeholders.

I would expand on #1 and say "Know all organizational goals" and understand as many requirements as possible. What OKRs, KPIs, and metrics is the business trying to improve? Make sure there is a vision and strategic plan to move the needle and gain an ROI.

Beyond clarifying your goals, understand the skillset within your team. Identify any gaps and consider external assistance if needed. When managing multiple goals, streamline by organizing data and assigning specific analyses and tasks to the team members with the relevant skillsets you've identified earlier.

The one thing I have found most helpful is knowing what problem I am trying to solve and when solved, what the value of the outcome will be. Helps with understanding what data to look for and then how others will be looking at it to draw insights for change.

Well-crafted goals align data analysis to the business strategy and the knowledge of these goals sets analytics teams on the path to success. Analysis goals must prioritize and focus on the business strategic plan. SMART goals provide a basis for measuring outcome of data analysis through KPIs and other metrics that can be used to determine the success of analytics. The knowledge of analysis goals provides a platform to reflect on success or failure of analysis.

The choice of right tools is important because it determines the effectiveness of the analysis. Analysis for any organization is for now and the future, therefore future proofing the analysis process is key in ensuring that tools do not get obsolete in a flash. It is advisable to use tools that adapt to newer technologies such that the investment in these tools remain relevant and useful in the future. A decision can also be made around compatibility of tools with the current infrastructure. It is foolhardy to acquire tools that are incompatible with the current infrastructure as in entails more costs of setting up new infrastructure which is compatible with the new analysis tools.

Tool Selection: Select data analysis tools that align with your goals. Guided by the principles in "Cracked It!" and "The Effective Engineer," the right tools streamline the analysis, ensuring efficiency and accuracy. Neuroscience Insight: Utilizing tools that suit your cognitive preferences optimizes brain efficiency and decision-making speed. Practical Takeaway: Stay updated on emerging tools to incorporate innovations into your workflow. Conduct training sessions to ensure the team is proficient in using these tools.

Crossing a right tool is this case is very nice decision because huge data needs segregation, addition, facts, figures, analysis, and more and to make wish come true tools.

Data analysis tools. Utilize data analysis tools to crunch numbers and create visualizations and graphs. Tools like Tableau, excel, power bi, google data studio are excellent for performing data analysis and generating actionable insights. In case more experience use python.

Everyone will do their analysis in the tool they are most comfortable with, however consider how often you will need to do the analysis and who you are presenting it to. Many of us can analyse the world in an excel spreadsheet however doing so monthly is time consuming and we need to be the ones always doing it and at the end the presentation format may not be the most vibrant. This is where business tools and dashboards come into play where it is more beneficial to set that up once and it can be used monthly etc and management can access live data on apps instead of needing people to constantly update

Understanding the data is key. Fields are the fundamental building blocks of data. Knowing in depth, every field in your data goes a long way in cleaning and organising your data well. - It helps you align your goals to the data model. - It helps you understand the business process behind the data, thus helping you build strong reasonable hypotheses. - Clean and organise better. Data Analytics is not mere data crunching. To be able to do it smartly, understand the data well.

Data cleaning/ wrangling/ smoothing/ transformation/ scrubbing is a crucial part of the data pipeline in that it prevents data bias and inaccuracies. Reduced bias and inaccuracy lead to better analysis outcomes. Inaccuracy and data bias is reduced in data cleansing by outlier detection and removal, handling missing values and duplicates, identifying errors and correcting them and having a representative sample of any dataset that is fairly selected (without any bias).

Before starting the analysis, it’s important to clean up the data by uncovering and correcting any inaccurate or repeat records.

Certa vez, durante um atendimento de potencial churn, era necessário buscar informações de requisitos sobre uma determinada função de uma ferramenta desenvolvida para o cliente, porém haviam muitas informações em diversos lugares, então antes de qualquer coisa foi necessário juntar tudo, dividir em classes usando o Excel e excluir o que estava duplicado. A organização é imprescindível para um trabalho de sucesso.

Data Quality Matters: Invest time in cleaning and organizing data, echoing the emphasis on data quality found in "Measure What Matters." Quality data is crucial for accurate and meaningful insights. Neuroscience Insight: A clutter-free dataset minimizes cognitive load, allowing your brain to focus on extracting relevant insights. Practical Takeaway: Implement automated data cleaning processes to save time and enhance accuracy. Develop standardized data organization protocols for consistency.

Thorough Exploration: Dive into exploratory data analysis to uncover patterns, trends, and outliers. Drawing inspiration from "How Big Things Get Done," a meticulous exploration ensures a comprehensive understanding. Neuroscience Insight: Novelty-seeking neural circuits are activated during exploration, stimulating creative problem-solving. Practical Takeaway: Encourage a culture of curiosity within your team for comprehensive data exploration. Schedule regular brainstorming sessions to generate fresh perspectives.

It is easy to over complicate how we look at data. I would usually start using a pareto method and see what are the main drivers behind the goal I am investigating. Usually from these methods you already identify the key drivers of under performance etc. Or sometimes simply targeting the top/bottom 10 can again be effective depending on the metric

When you have a lot of data to analyze, you need to follow structure approach, ✔️Define Your Objectives ✔️Gather all relevant data sources ✔️Clean the data to remove errors and all ✔️Exploratory Data Analysis (EDA) Explore the data to understand ✔️Hypothesis Testing: If applicable Model Building: Depending on your objectives ✔️Interpretation and Communication ✔️Iterate: Review your findings, refine your approach, and repeat steps as necessary to gain deeper insights.

This is the key part of analysis and as an Accountant sometimes the hardest. You want to articulate what you have found, perhaps refer to a time where it used to be better so we can work out what may have changed, put a financial benefit to what the gap is worth again for incentive or to make sure we focus on the important issues. Try to use graphs and more interactive ways of displaying data remembering the audience will all have different levels of interrupting numbers

The culmination of your data analysis journey is akin to storytelling. Your audience may vary from technical experts to business stakeholders, each with their own language. Tailoring your narrative to fit your audience ensures your insights resonate and inspire action. For instance, while presenting to executives, focus on the impact of your findings on the bottom line, using dashboards for a quick overview. For technical teams, dive deeper into the methodologies and data intricacies. Remember, the power of your analysis is not just in the numbers but in how effectively you translate those numbers into compelling stories.

Effective Communication: Translate complex findings into actionable insights, echoing the storytelling principles found in "Data Storytelling." Use visualizations for clarity. Neuroscience Insight: The brain processes visuals faster than text, aiding efficient comprehension and decision-making. Practical Takeaway: Develop data storytelling skills to convey insights persuasively. Create concise reports with visually appealing charts and graphs.

Opinion matters for reviewing and improving the process. We can do it from our peers or anyone having similar knowledge. We can explore the new features of the tool and try to implement that in our process and we can then analyse if it creates the difference or not.

Continuous Improvement: Regularly evaluate your data analysis process. Inspired by "The Decision Book," identify bottlenecks and areas for improvement. Neuroscience Insight: Reflective practices engage the brain's metacognitive functions, enhancing learning and decision-making. Practical Takeaway: Foster a culture of continuous improvement to adapt to changing business dynamics. Implement feedback loops for ongoing refinement.

The field of data analysis is ever-evolving, with new tools, techniques, and best practices emerging regularly. Adopting a mindset of continuous learning and improvement is essential for staying ahead of the curve. This might involve seeking feedback from stakeholders to refine your analysis, exploring new data sources to enrich your insights, or embracing advanced analytics techniques like AI and machine learning to automate and enhance your analyses. Like a master craftsman continually honing their skills, the pursuit of excellence in data analysis is a never-ending journey.

For streamlining business processes, mapping tools visualize workflows, pinpoint inefficiencies, and guide optimization. Simulation tools forecast outcomes, allowing for risk-free experimental changes. Automation streamlines routine tasks, boosting productivity and accuracy. Improvement tools, aligned with lean and Six Sigma, focus on waste reduction and variability. Innovation tools foster out-of-the-box thinking for transformative solutions. Always consider tools' compatibility with current systems, scalability, and ensure they support ongoing refinement through insights-driven feedback for enduring efficiency.