How do you use business data to give strategic advice to clients?

One thing I have consistently identified is based on the data analysis, one is able to provide actionable recommendations for strategic decisions. These could include market expansion, product development, cost optimization, pricing strategies, and more

Understanding the client's objectives and problems is the first step in providing advice on a?strategy based on business data that is effective. deep into data analysis, uncover insights, and visually communicate them for easy understanding. Finally, make specific, useful recommendations that are based on the data. This method ensures that clients may make well-informed choices by combining professionalism with open communication.

Data Collection: Gather relevant data from various sources, including market trends, financials, and customer insights. Analysis: Analyze the data to identify patterns, opportunities, and threats, using statistical tools and data visualization. Insights: Extract actionable insights from the analysis, highlighting key findings and their implications for the client's business. Recommendations: Develop strategic recommendations based on the insights, aligning them with the client's goals and industry best practices. Communication: Present findings and recommendations to the client. Iterate: Continuously update advice based on new data to ensure ongoing strategic alignment.

Here's a straightforward method I use to identify customer-related growth opportunities within my client’s operations: 1. Generate a list of questions about the business, preferably from a customer's perspective (e.g., "what is the revenue generated from top the 10% customers?", "how many customers used product X?"). More questions, the better. 2. Organize: categorize and prioritize this list. 3. Identify the data needed to answer each question. 4. Systematically answer the questions; typically, around 70% can be addressed by analyzing 3-4 standard datasets. As you answer, new questions will emerge; apply steps 2 and 3 to these and continue the process. This method promotes a consistent and focused learning process.

Clear requirements from business as well as meeting with the business expert to confirm understanding of the requirements and regular check-ins will ensure that the correct data is sourced. Quite often reporting is done after ETL and data quality issues creep in which we have to deal with as well. Combining multiple sources of data can also provide many challenges, but these could be addressed with the regular check-ins.

For me, the extraction and transformation of data- 80%- informs the 20% which entails visualization and deriving insights. When an analyst gets the sources correct and ensures a high accuracy level while transforming the data set, the rest pans out way easier. Also, like everything, being an analyst entails growth. You'll grow into different ways of doing stuff until you hack it. Keep at it!

I have come across a couple of scenarios where standard data quality checks, and data cleaning procedures fail to get the desired result. It has to be an iterative process and involve the business stakeholder from the very start. Sometimes, visualizing a random data extract before ETL helps a lot with the process.

I found this useful as you should collect all the required data for your analysis, check the data and complete all the missing parts of your requirements, if data is high then it'll be easy to extract the results but don't take, or save the unwanted data for clear & better analysis

A significant challenge I've encountered in the realm of Data Analysis revolves around the process of collecting data and subsequently forging meaningful connections between different variables. So comprehensive and robust data source is consistently advantageous.

In recent ETL endeavor I realized how important it is to clean the data. It not only makes further processing easier but clears up the memory load at further stages. Of course the flexibility of every data point is lost as we cut short few unnecessary points, but until the project is complete the whole ETL pipeline is flexible and hence the control is really never lost. Moreover, cleaning and collecting the data usually is series of scripts and at each level the overview of the total solution also clears up in the mind. In other words, if the overall solution is an image, the ETL steps will enhance the resolution of that image by 10 times.

I developed a business report using a market-leading BI tool for a pharma client once. The report delivered on all use cases presented in the requirements. However, I got buried in support requests after deployment. Mostly due to a lack of navigation tips, helpful text, and glossary/documentation. Even if the tool is intuitive, it still helps to have additional usage directions or textual tooltips. I tend to add glossary to every report ( KPI definitions, static filters, assumptions, data update frequency)

Before diving into the presentation of your findings and recommendations, I suggest adopting a simple practice effective for any communication: clarify in your mind what you want your audience to know, feel, and, perhaps most importantly, do (named KFD) Writing down these three points is a priceless habit for storytellers (and yes, a good analyst is a storyteller). Ticking off the three points below can lead to making a significant impact, whether on your organization as an analyst or on your client as a consultant: 1. You can craft a clear and coherent story that accurately reflects reality (and keep analyzing until you get there). 2. You have passion for your story (trust me, your audience can tell). 3. You stay focused on your KFD.

I develop the dashboard through the pivot with slicers to explain the client's in easyway by keeping different variables in row & then explain to the HOD's where we are lacking & how we can track these parameters, also given Plan of Action & that works better

The tools like Power BI and Tableau or the libraries of Python like matplotlib, plotly, seaborn and dash will be your most valuable leverage to improve your data communication. Creating your own charts collection and dashboard to reveal the power of your data

Multiple times I have joined teams where documentation of core enterprise reports was incomplete or none at all. Bug fixing or updates in these cases is like shooting in the dark. BI documentation does not need to be comprehensive but its important to be regularly updated by the business analyst.

The world of data analysis is quite fascinating. Sometimes, you get so caught up in making cool charts and impressive dashboards for your team. But here's the thing: don't forget to jot down what you're doing. Documenting your steps is like leaving breadcrumbs on your data journey. It helps you keep tabs on what you've done, and guess what? It's not just for you, but also for your teammates who might tackle similar data down the road. Plus, the info you scribble down can answer big questions about your project over time. And for new data folks, it's like showing off your skills to future bosses.

Being an analyst, data security is the first part of your job which you can't tolerate in any conditions, you should take care of your clients, Stake Holders, Business Management Trust at any cost

I greatly admire how these experts have elegantly articulated their insights. Allow me to contribute and distill their wisdom. 1. Data Collection: Gather relevant data such as financial reports, market research, and customer analytics. 2. Data Analysis: Analyze the data to identify trends, patterns, and key performance indicators (KPIs). 3. Identify Insights: Extract actionable insights from the data, such as market opportunities, cost-saving measures, or customer behavior trends. 4. Competitive Analysis: Compare the client's data with industry benchmarks and competitors to identify areas for improvement. 5. Goal Alignment: Align data insights with the client's strategic goals and objectives.

In my humble opinion, Agile methodology is really useful in evaluating the outcome and collecting feedback. The advantage of Scrum lies in the fact that you can divide the total project into smaller modules and work on each one of them. Once a single module is completed, the intended clientele can review and provide their valuable suggestion. This helps in the next iteration saving a huge amount of cost and time.

When it comes to assessing outcomes and feedback in the realm of data-driven solutions, my approach centers on comprehensive evaluation. Utilizing suitable metrics, I gauge the impact and effectiveness of implemented advice. Continuous monitoring of evolving data trends allows for the identification of fresh opportunities and challenges. Valuing client satisfaction and feedback, I actively seek insights for continuous improvement.

I always believe that there are 3 levels of working in the world. Be it professional environment or Personal. 1. The bird eye view where you know the vision and the complete picture. 2. The detailers where a person populates every part of idea with detailed action, be it script writing for a program or running errands for a project. 3. The middle managers where one works with the visionary to make sure the detailers are in alignment with the bird eye view. It is only possible for a person to work at 2 levels and it is crucial for the team to share insight of the third property they lack. And this include clients as a part of system.