Modern Excel - 10 Powerful new Tips to boost your productivity

How Reporting and Analytics is Typically Done 

Reporting and Analytics are typically approached as follows, These steps highlight a systematic approach to reporting and analytics, emphasizing data preparation, strategic use of Excel, and effective communication of insights: 

1.  Data Connection: The process often starts by connecting to the data through an ODBC connector and building the data model. This step is crucial for automating reports. Excel is used for various types of reporting, including treasury, cash, and bank reporting. 
2.  The MODES Framework: This framework includes Mapping your data, Organizing your data, Designing your data model, Executing that design, and Storytelling. This framework is taught as a method for automating report generation in Excel. 
3.  Data Preparation and Analysis: The process involves cleaning and preparing the data, computing KPIs, and aggregating the data. Following this, the data is often populated into PowerPoint for presentation. The emphasis is on preparing and analyzing the data, and then presenting the analysis to management, usually on a weekly, monthly, or quarterly basis. After the presentation, there's an opportunity to brainstorm and act on the data. 

All through the webinar, David made sure to stress the importance of the mnemonic: "A good cook always dices vegetables rapidly" in the webinar, to enable the audience to remember the seven-category framework for solving any problem in Excel. Each first letter of the phrase corresponds to a category in the framework: 

• A for Acquisition 
• G for Guidance 
• C for Collaboration 
• A for Acquisition 

• D for Dices (Data Analysis) 
• V for Vegetables (Variation) 
• R for Rapidly (Repetition) 

The framework is designed to help categorize and approach problems effectively within Excel, reinforcing the idea that Excel is a versatile tool capable of solving a wide range of problems. In the webinar extract above David went ahead to explain the meaning of each acronym in steps that will boost productivity in Excel and they have been summarized below: 

Step 1: Acquisition: Data Collection and Importation 

In the webinar's seven-category framework is centered on the challenges and processes associated with data collection and importation. This step: Acquisition, is critical as it involves collecting and bringing in the data, which is considered the core problem to be addressed in Excel. Excel provides a wide range of functionalities to import data from various sources, making it a powerful tool for data acquisition such as text files, databases, other Excel files, web pages, APIs, etc using Power Query. This step is fundamental in the framework for solving any problem in Excel, underscoring the importance of efficiently and effectively gathering and importing data to start any Excel-based analysis or reporting process. 

Step 2: Guidance: Optimization 

This step leverages Excel's advanced tools like Solver, Goal Seek, Data tables, and the Monte Carlo simulation to tackle complex optimization problems, such as resource allocation and scheduling. The Monte Carlo simulation, in particular, involves running numerous simulations to determine the most likely outcome.  

Guidance is crucial when faced with multiple options, as Excel aids in conducting detailed analyses to identify the best and most optimized choice. This capability of Excel is especially valuable in business scenarios where strategic decisions need to be backed by robust data analysis, ensuring that choices are not just guesswork but are informed by reliable, simulated outcomes. 

Step 3: Collaboration: Collaboration and Security 

This framework step addresses the aspects of data sharing, collaboration, and security within Excel. This step focuses on the need to securely share Excel files and data, set appropriate permissions, and ensure data security. The webinar highlights the capability of Excel to collaborate effectively, mentioning the use of Power BI as a tool for publishing Excel content for broader access and enjoyment. This step underscores the importance of managing access to data, ensuring that only authorized individuals, like certain executives or sales teams, can view sensitive information. The emphasis is on the secure and strategic sharing of data, which is essential in collaborative business environments. 

Step 4: Analysis: Data Analysis 

This framework focused on data analysis, which encompasses problems requiring mathematics, statistics, logic, and logical reasoning to interpret data and derive insights. This step involves various analytical tasks such as data summarization, lookup operations, regression analysis, forecasting, ratio analysis, and more. It is an integral part of reporting, as most reporting tasks require some level of analysis to understand and make sense of the data. 

Step 5: Display: Visualization and Reporting 

The Display framework deals with how data is visualized and reported. This step addresses issues and tasks related to the visual presentation of data in Excel. It emphasizes the use of Excel for creating charts, graphs, and dashboards to present data in a visually interpretable manner. This category is crucial for data presentation and reporting, focusing on the effective display and visualization of data to convey insights clearly and effectively. 

Step 6: Variation: Data Organization and Transformation 

Variation revolves around data organization and transformation. This step is concerned with how you manipulate and structure data after it has been acquired. Key tasks in this phase include merging data from multiple sources, filtering, sorting, and generally transforming the data. Tools like Power Query, Power Pivot, and Pivot Tables in Excel are essential for these tasks, enabling users to effectively restructure and modify data to meet their analysis needs. This step is crucial for preparing data in a way that makes it ready for insightful analysis and reporting, highlighting Excel's strength in managing and transforming diverse data sets. 

Step 7: Repetition: Automation 

This final step addresses the need for automating repetitive tasks in Excel, which is a critical aspect of efficient data management and analysis. Excel offers a myriad of solutions for this, including advanced tools like Office Scripts, Power Query, VBA, Com add-ins, Web add-ins, Power Automate, and M. These tools are designed to streamline repetitive processes, making Excel not only a tool for analysis and reporting but also for automation, thereby enhancing productivity and reducing the potential for manual errors​​​​. 

Problem-Solving 

Problem-solving in Excel involves understanding which part of a specific framework is being used to address the identified problem. It requires identifying the nature of the problem (e.g., an optimization problem) and then determining how to solve it using Excel's tools. This process is not just about solving the problem but also about how to manage and automate reporting based on that problem. Key aspects include identifying data acquisition sources, how to get the data for reporting, and whether the data acquisition process can be automated. The ultimate goal is to have all the data organized and growing in one centralized location, which facilitates easier management and reporting​. 

In the webinar, the following points were made about Power Query and Power Pivot: 

1. Power Query: Power Query is a key tool in Excel, particularly useful for data collection and importation. It enables users to connect to various data sources, including text files, databases, and even PDFs, to bring data into Excel. Power Query's primary function is to acquire data and transform it, handling various tasks related to data manipulation. David emphasized the importance of learning Power Query as an essential tool for managing and structuring data in Excel​​. 
2. Power Pivot: Power Pivot, along with Power Query and Pivot Tables, is used for structuring and manipulating data, such as merging data from multiple sources, filtering, and sorting data. This tool is part of Excel's suite for handling data variation, organization, and transformation. It plays a crucial role in managing complex data sets and enabling efficient data analysis within Excel​. 

The model shown in Excel can typically be designed in two ways: 

1. Versatile, General-Purpose Model: It's possible to build an Excel model that is versatile and can be applied to various data sets. Such a model would typically include a flexible structure, allowing for different types of data to be inputted without needing significant adjustments. This approach is useful when you need a model that can handle a variety of scenarios or data types. 
2. Specific Model for a Single Report: Alternatively, an Excel model can be built specifically for a single report or data set. This type of model is highly customized to the unique requirements and nuances of that particular data set or reporting need. It might include specific formulas, data validation rules, and formatting tailored to the data it is meant to handle. 

The choice between these two approaches depends on the intended use and the variety of data the model is expected to handle. A general-purpose model offers more flexibility, while a specific model can offer more precision for particular tasks. 

The Q & A Session 

During the questions and answers session, David emphasized the importance of careful consideration in data acquisition, especially when dealing with new types of data like survey results. The key point was the need to connect the data to relevant parameters, such as who did what, where, and when. This connection is vital for automating reporting and effectively pulling out reports. David prompted the audience to think about the best methods for data acquisition, recognizing it as a commonly overlooked yet crucial part of the data management process​​. 

In this session, the following points were made about automation in Excel: 

1. Building Tools for Automation: The webinar discussed creating tools in Excel that allow users to input data, with the tool automatically providing answers. This concept extends to building larger tools for more complex data sets. Automation is an integral part of this framework, where VBA and other automation tools can be used to further process the results generated by these Excel tools. The analogy used was that "Excel is like Lego", emphasizing the idea of building and connecting different components for comprehensive solutions​​. 
2. Variety of Automation Solutions in Excel: The webinar also highlighted Excel's capabilities for automating repetitive tasks. It pointed out a range of solutions Excel offers for this purpose, including Office Scripts (a powerful and latest automation tool in Excel), Power Query, Power Pivot, Pivot Tables, and VBA. These tools are designed to help structure and manipulate data efficiently, streamlining processes that would otherwise be repetitive and time-consuming. 

Data validation typically refers to the process of ensuring that the data collected is accurate and appropriate for its intended use, which is a separate but complementary process to data acquisition. 

Later during the session, David was asked the question "How did you learn Excel?" during the webinar. This was his response: 

He explained that his learning process involved solving people's problems. By addressing real-world issues, he was able to apply and solidify his understanding of concepts in a practical context, rather than just theoretical knowledge. This hands-on approach to problem-solving allowed him to deepen his expertise and find practical applications for his skills. 

Another question "Can someone without a finance background learn financial modeling?", was raised and in response, David affirmed that it is indeed possible. He outlined that the essential prerequisites are: 

1. Basic accounting knowledge, which can be acquired relatively quickly, even within a week, through resources like online courses (e.g., Coursera).
2. Good Excel skills. 
3. Basic finance skills. 
4. Critical thinking skills. 

With these foundational skills, an individual can start learning financial modeling, regardless of their background in finance​. 

Reflecting on the question, "What Excel model is good for treasury and cash and bank reporting?", David highlighted that Excel is well-suited for handling treasury, cash, and bank reporting data. The key to effective reporting in these areas involves focusing on the data inputs, calculations, and outputs. While David mentioned a specific instance where he used Power BI for detailed treasury reporting for a large client, it was noted that Excel could be used similarly for such purposes. The emphasis was on Excel's capability to manage the necessary data for treasury and cash reporting effectively. 

 
Offering insights on the matter of whether Excel can be used as an inventory tool and if it's good for inventory and store operations, David acknowledged that Excel can indeed be used for such purposes. However, he emphasized the distinction between Excel and database software. While Excel is not a database, it can still be utilized effectively for inventory and store operations. The key point made was to understand the capabilities and limitations of Excel in comparison to dedicated database software. 

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