You’re a management consultant. How can you model data to optimize your client’s business?

• Understand client objectives and the business goals and what they are trying to optimise. • Collect data from diverse sources, ensuring quality and reliability of data. • Analyse data for patterns and trends, engineer features to enhance model performance. • Choose appropriate models based on data, train using techniques like cross-validation to prevent overfitting. • Assess model performance with metrics, deploy into production, monitor continuously for effectiveness, and refine iteratively based on feedback.

As a management consultant, model data by: 1. Defining objectives and KPIs. 2. Collecting and cleaning data. 3. Exploring data for patterns. 4. Testing hypotheses. 5. Using appropriate modeling techniques. 6. Training and evaluating models. 7. Optimizing strategies through scenario analysis. 8. Implementing solutions and monitoring outcomes. 9. Establishing a feedback loop for continuous improvement.

1. Understand the Business Define objectives (e.g., increase sales, reduce costs). Assess available data and identify gaps. 2. Data Preparation Collect and integrate data from various sources. Clean and prepare data for analysis. 3. Exploratory Analysis Conduct EDA to uncover patterns and form hypotheses. 4. Model Development Select appropriate modeling techniques (regression, classification, machine learning). Build and train the model. 5. Model Validation Test model accuracy on separate data. Refine based on performance. 6. Implementation Integrate model into decision-making processes. Provide actionable insights and recommendations. 7. Monitoring Continuously monitor performance. Update model and strategy as needed.

One thing which I have found useful is to begin by clearly understanding your client's business objectives. What are the key performance indicators (KPIs) that matter most to them? What specific goals are they trying to achieve through data optimization? Once you have understood Business Objectives then Identify relevant data sources. This could include internal databases, external APIs, third-party data providers, etc. Once you have collected the data then ensure data quality by cleaning and preprocessing the data. Address missing values, handle outliers, and standardize formats.

Using simple linear regression to forecast spending relative to an important independent variable, e.g., sales dollars or sales units, can provide an excellent starting point for developing a company's annual profitability plan. In this way, the forecast model can be used to prepare various scenarios (low case, high case, base case) such that the financial planning team can measure variances to plan at different levels of actual achievement of the independent variable (in this example, sales dollars or sales units). The linear regression model is simple to create using spreadsheets like Excel, and can provide much higher accuracy in profit planning than educated guesses based on management estimates.

Data modelling is critical to generate insights, reduce complexity and get to the point fast. The right data is essential to drive acceptance with the client. Often, this step is necessary as clients sometimes have a reluctance towards consultants.

Indeed! Just to add, the power of data modeling enables one in exploring insights through advanced analytics, Generative AI, complex visualisations, RPA and many more ways.

Here is an analogy that I often use to explain the importance of data modeling: In data modeling, think of it like organizing a library. By categorizing and linking books (data), you make finding information easier, enhancing decision-making. For example, a well-structured model can reveal hidden trends, much like a good index reveals a book's key themes.

Data Modelling is extremely crucial for generating insights and analysis which forms the bedrock of the recommendations for solving clients problems. The models build basis the data regarding the buisness problem helps us to integrate sensitivities which can help one visualize the impact of various external factors on the project economics. There can be different kind of models such as financial models using DCF analysis, Econometric models to understand the supply demand dynamics, Supply chain models to understand the critical levers which needs to be optimized.

A well-designed data model is the keystone of operational systems, business intelligence and analytics applications that create business value by transforming company data into useful information.

To apply data modeling effectively, start with clear goals. For example, use ER modeling to map client processes, or dimensional modeling for analytics. Tools like Excel and Power BI are versatile for these tasks. You must regularly practice and stay updated with trends are key to mastering this skill.

Effective application is paramount for optimizing one's client's business. Consider utilizing Entity-Relationship (ER) modeling to meticulously define and map entities and relationships within the business's domain. Dimensional modeling, on the other hand, proves invaluable for organizing and optimizing data specifically for analytical endeavors like reporting or forecasting. This technique streamlines data for meaningful insights. By strategically applying these data modeling techniques, you not only enhance the clarity of your insights but also provide actionable solutions that align with your client's objectives.

Follow business processes from delivery to client back to original ingredients. Link the roles (or agents) related to each step and find corresponding digital bread crumps in the applications databases. Redo the first conversations with the data and findings as input. Everybody has nice flowcharts somewhere on a wall. True story gets told by data and agents.

Predictive Analysis would be the best technique to employ because it is tied to the Voice of the Process. The Quality and Accuracy of data capture at the point of process performance is key to levelling processes to optimize yield. This is important by way of using Lean Six Sigma approaches and Kaizen methods, the quality of data and accuracy is highly enhanced. Thus the trend and partners help decision-makers make informed decisions and the organization becomes data-driven. Therefore Predictive Anlysis would be a good approach in helping management arrive at a good decision that gives them an edge over the competition. Why, because they plan to use their capability to meet a targeted market requirement, and sustain that supply chain need.

One thing I have found very helpful is 'start from micro model that is simple in implementation, easy for client's comprehension, focused on the actual client's needs/expectations'. It does not matter there are heavy ERP Apps or there are no its, the strong recommendation is 'don't touch working engine', even the client insists to have very soon low hanging fruits... As for illustration: select the subject for the micro model, develop transparent Structure and Logic (see above) with less details and breakdown for one preselected element, use working operational Apps to collect data as much as possible, develop technical tool based on G-Suite / MS-365 keeping the architecture is clear, guide the client through the micro model

There are many tools for data modelling. However, excel works the best even with large volumes of data. Access also is an apt solution in case of very large sets of data. These days, many AI led data analysis tools are present.

In data modeling, simplicity is key. Use tools like Excel for sorting and analyzing data, Power BI for complex tasks like interactive dashboards, and Visio for process models. For example, in a retail project, use Power BI to combine sales and customer data for targeted strategies.

Going on with the example (of a Benford Analysis), one can develop a visual model representing actual digit-distribution within data (compared with expected digit-distribution as per Benford’s Law). Should such a visual model show that an unexpectedly large number of transactions start with the digits “49” and should there be a company policy requiring a double-signature for purchases greater than 5.000 (whatever the currency), there may be an indication of potentially fractioned purchases aimed at circumventing the double-signature policy requirement.

To effectively use data modeling tools, start by defining objectives and selecting the right tool (e.g., ERwin, IBM Data Architect). Gather requirements from stakeholders, progressing from conceptual to logical and physical modeling. Use notations like ERD or UML, validate models with stakeholders, and iterate based on feedback. Thoroughly document models for clarity and future modifications. Encourage collaboration, implement version control, and integrate models with development processes. Provide team training, stay updated on tool advancements, ensuring efficient and accurate representation of data structures.

Again - simplify to amplify. ER/Studio is a good tool, but it can get bogged down and run more slowly if the data is too big. I wouldn't recommend it as an enterprise tool. The tools are going to help you create data structures - whether it be lists, arrays, records, trees, tables, etc. Determine if your client will need file structure or storage structure. If data is constantly changing, a linked list might be necessary.

A melhor forma de melhoras as habilidades é fazendo acontecer, juntando ao time a nova geração que lida bem com ferramentas quantitativas e não ter vergonha de perguntar. Considere que se você já é um executivo sênior você conhecer bem os fundamentos do negócio e a nova geração conhece bem os métodos quantitativos, ou seja uma boa partceria de complementariedade.

To enhance your data modeling skills, focus on real-world applications. Start with basic tools like Excel, progressing to more advanced ones like Power BI. Apply these in practical scenarios, for example, optimizing a retail client's inventory system. You should regularly seek feedback and stay abreast of new trends. Always remember, effective data modeling translates complex data into actionable insights, which is a crucial skill in management consulting.

As a Professor in Data Science and Data Modeling Practitioner, I strongly believe that continuous improvement of data modeling skills is paramount for management consultants. Understanding the fundamentals, best practices, and various data modeling techniques is essential, but practical application on real-world scenarios and seeking feedback from peers and experts is equally crucial. Embracing the latest trends in data modeling, such as cloud-based and big data solutions, is vital to stay relevant and effective in optimizing clients' businesses. Data modeling serves as the backbone for understanding, analyzing, and communicating complex information in a clear and actionable manner, making it an indispensable skill for consultants.

First, expected outcomes (say): - Higher revenue, profitability - Enhanced customer experience - Streamlined operations - Informed decision-making. so the simple steps are: 1. Understand stakeholder needs: Run interviews, workshops to understand objectives, KPIs and challenges. Identify improvement areas: in sales, customer & operations areas check market trends 2. Gather internal / external data, clean and pre-process to raise quality 3. Select appropriate model (techniques) and dev and test models 4. Model Implementation and Optimization - through model integration, and tracking track performance

In short: - Learn the basics and study database design principles - Work on real projects, avoid slideware - Always seek feedback from peers, mentors, ... - Stay Practical The more hands-on experience you have, the more confident and skilled you'll become :-)

Data modeling is needed to level the understanding of the context and current reality. Insights, are moving the performance, not the models. Good consultants provide insights and enable clients to own the models.

Based on my experience as a Management Consultant, here is what I recommend: In optimizing your client's business, consider the human element. For example, when using data models to predict sales trends, remember the sales team's feedback can provide invaluable insights that raw data might miss. Combining data with human experience leads to more robust strategies

Data modeling process has to enable clients to arrive at optimal decisions and not as an endless process to make all the parameters accurate to make the model right. The approach has to be iterative including right data modeling steps and gathering client feedback, focus should be to reach a stage where client owns the data and is confident to implement insights, test and tweak approach. Otherwise the process maybe endless with analysis fatigue and no decision.

Data modelling is one part of the equation, there are multiple tools available to clean, crunch and provide initial analysis. However what differentiates one business decision from another is the interpretation of that information and how to use it effectively to get the desired outcomes. As a management consultant, working closely with business leaders; it is the impact that the leader foresees of that data if executed right. For example launching a point of care diagnostic product for a cancer diagnosis, what does the data suggest and how, when and why the launch should be done. These are bigger questions from a business perspective than just be able to simplify large patient datasets! Business decisions must focus on value creation

It is best to build a data model in a top-down approach, from the overarching business requirements down to a detailed database structure. The main purpose of data modeling is to enable better and more efficient data management. Data modelling is a fluid process that can be adapted to the needs of the organization. However, most teams create a conceptual model first, followed by a logical and finally a physical data model. This allows your organization to move from a more general visualization of the data to a more technical representation of the actual database. Good data modeling breaks down silos and ensures consistent information across all departments, systems and databases.