As a management consultant, you often have to deal with complex and diverse data sets from various sources. How can you analyze them effectively and efficiently to deliver valuable insights and recommendations to your clients? Here are some tips to help you get started.
Before you dive into the data, you need to have a clear understanding of the problem you are trying to solve, the objectives you are pursuing, and the questions you are asking. This will help you narrow down the scope of your analysis, identify the relevant data sources, and formulate hypotheses to test.
As a young Analyst back in 2014 at PwC, we were taught to use one phrase so often that it became muscle-memory - "Completeness and Accuracy". Now as a more experienced Consultant, when faced with data to analyze, first thing I do is check if it is complete. Dealing with incomplete data is setting myself up for failure or at best, some additional rigorous hours of reworking my entire analysis. One way to check for completeness is by cross-checking against another source for the same data. Second, I check if my data is accurate, because why analyze false data? I could also do this by cross-checking against another source - document or personnel. When I am fairly certain my data is complete and accurate, then analysis can start!
It is always essential to understand “why” you are doing a task. This really helps define the problem and being crystal clear on what outcome you are seeking.
Critically important to start with the right problem statement, hypothesis or whatever you want to call it. It's a clear articulation of what's NOT happening that needs fixing. Next, brainstorm the data you need to provide insight to that problem statement. Diagram the inputs & key activity steps at the core of process & decide which metrics (think good, cheap, fast) give you the most insight. Don't be fooled by easily available data. Get the data you need, even if it's going to take longer, cost more, or ruffle some feathers. This is better than relying on "lazy data" that may lead you astray. Also, consider stratification factors (regions, business units, customers, etc) to help create compare & contrast factors for later analysis.
Co-creation and co-ideation with the client is key for the consultant to identify the key problem. Combining the outside in view with the actual problems the client‘s organisation face is key. Conducting workshops (as an investment) to narrow down possible industry problems and focus on the client needs is a great way to get started and gain goodwill and buy-in from the client
I have a slightly different take on this, sometimes as a management consultant the data that we get does not necessarily have a direct connection with the things that we are in discussion with. It may happen the other way round as well that there is no expectation articulated clearly but you need to explore the data and see where it takes you. So my first response to a data set even before hypothesizing the problem statement is to cleanse the data and visualize it such a way that you are comfortable enough to take bird's eye view on that. Mind you, this step is different from the usual structured approach that you take, this is about you being comfortable with the data. Post that you will have a clear understanding of the path to take.
Once you have defined the problem, you need to collect the data that will help you answer your questions and test your hypotheses. Depending on the availability and quality of the data, you may need to use different methods, such as surveys, interviews, web scraping, or secondary research. You also need to ensure that the data is accurate, consistent, and complete by performing data cleaning tasks, such as removing duplicates, outliers, and errors, filling in missing values, and standardizing formats.
In a world where so much data is available, it is absolutely essential to validate the source of the data and to ensure the data is de-duplicated and clean. Cleaning is a complex task that can often (but not always) be automated. It is also important to consider which data might be missing from the set and could augment the actual data set for completeness.
I am listening to the keynote speaker at HIMSS 2024 and they are talking about having clean data. Ensuring you have clean data will be imperative for AI and LLMs to work effectively. As a former CIO getting to clean and usable data needs to be an organizational imperative for the other steps to provide their full value to organizations.
As a consultant, the initial step entails meticulous data collection from both existing sources and, if necessary, through primary research avenues. The collected data undergoes rigorous assessment to ensure its quality, freshness, and relevance. Understanding the methodologies employed in data collection and upload is imperative to proactively identify any potential issues, thereby providing valuable insights into the data's usability. Care must be taken during the cleaning process to avoid loss or distortion of critical information. Leveraging multiple sources of data enables triangulation, allowing us to ascertain a more accurate depiction of reality compared to relying solely on a single source
- Document a logical ER diagram - Identify the data sources and respective connectivity - Design and create the required schema, tables and other database objects - Design and build the pipelines to load the data: Load the data from source to the RAW db and then perform transformations - Implement proper error logging and exception handling wherever applicable - Implement and document the business logic and data cleansing methodologies - Set proper data quality checks - Be mindful of the naming standard and other best practices
One thing I learned from experience is to ensure that you use the right data collection method based on data available, context , budget and time limitations and need for quantitative vs qualitative data.
After you have gathered and cleaned the data, you need to explore it to gain a better understanding of its characteristics, patterns, and relationships. You can use various techniques, such as descriptive statistics, correlation analysis, and clustering analysis, to summarize and describe the data. You can also use data visualization tools, such as charts, graphs, and dashboards, to present the data in a clear and compelling way.
Exploring the data to grasp patterns and assess its usability is a critical initial step in any analysis. It's imperative to approach the data with an open mind, free from bias or preconceived notions. Utilizing various BI tools for data visualization aids in this process. However, it's essential not to impose any predetermined narrative onto the data and to remain vigilant against confirmation bias. Qualitative data should be analyzed first, followed by quantitative data, to gain a deeper understanding of the underlying characteristics and relationships within and between datasets. This sequential approach ensures a thorough examination of the data and facilitates informed decision-making.
When crafting visualizations, prioritize clarity and brevity to effectively convey insights and conclusions. Opt for straightforward charts, graphs, or infographics that directly illustrate key findings without overwhelming viewers. Ensure that the visuals align closely with your conclusions and recommendations to enhance understanding and impact.
Before diving deep into complex analysis, explore the data to understand its characteristics and patterns. Use visualizations such as histograms, scatter plots, and box plots to identify trends, outliers, and distributions. This step can reveal unexpected insights and guide further analysis.
visualization can be done by examining the data to grasp its attributes, recognize patterns, trends, correlations, and anomalies. Visualization methods like histograms, scatter plots, and heatmaps can prove beneficial in this exploration
At this stage don't get distracted by further cleaning of data if needed. Focus on characteristics, patterns and relationships. Data visualization tools like Tableau, power BI..ect will indeed help to present data in a concise and engaging way.
The next step is to analyze and interpret the data to answer your questions and test your hypotheses. You can use various methods, such as regression analysis, hypothesis testing, and scenario analysis, to infer causal relationships, measure effects, and evaluate alternatives. You also need to interpret the results of your analysis in a meaningful and relevant way, by explaining what they mean, why they matter, and how they relate to the problem and the objectives.
This phase of management consulting is indeed exhilarating, as it involves delving deep into data and asking probing questions. The consultant's role is to continuously ask "why" to uncover underlying drivers and factors. It's crucial to resist the temptation to force-fit patterns and to remain vigilant against confirmation bias. If initial hypotheses don't yield conclusive results, it's perfectly acceptable to explore alternative hypotheses. Consultants must exercise caution when interpreting results, ensuring that they align with real-life examples before drawing conclusions. Ultimately, reconciling what the data suggests with real-world scenarios is essential for arriving at sound recommendations.
To analyze the data, begin with exploratory data analysis (EDA) to grasp initial insights using visualizations and descriptive statistics, identifying patterns and outliers. Formulate hypotheses from EDA findings, then conduct hypothesis testing through statistical tests to validate or refute these hypotheses. For more in-depth insights, optional advanced analysis techniques like regression, clustering, or machine learning can be employed based on project requirements.
As a management consultant diving into data analysis, the initial step is to thoroughly understand the dataset at hand. This involves exploring its structure, variables, and any inherent patterns. To start, it's crucial to examine the data's integrity by checking for missing values, outliers, and inconsistencies. Next, conducting descriptive statistics provides an overview of central tendencies, dispersion, and distribution of variables. Visualizing the data through charts, graphs, and plots is indispensable for gaining insights and detecting trends that might not be immediately apparent from the numbers alone.
Data is at the center of a fact-based recommendation. When analyzing and interpreting your data, always ask yourself the “so what, now what?” question. What does the data tell us, how is this useful, and what recommendation does it lead to? Data is just data if you cannot formulate insights from it. Without insights, you cannot get a step closer to developing the actions needed to solve your client’s problem.
This is the way which you can known what is the information the organization has and that you can handle, and what is missing, for trying to identify the problem to solve.
The final step is to communicate and present the data to your clients in a persuasive and professional way. You need to structure your presentation in a logical and coherent way, by using a clear storyline, a compelling argument, and a strong recommendation. You also need to use effective data visualization techniques, such as charts, graphs, and dashboards, to support your points and highlight the key insights and findings.
To first think about why you are analyzing the data in the first place. Ie start with the end in mind. Think about the question ( s) you are seeking to answer. Develop hypotheses as you go along and let the bad ones fall away. Present your findings conclusions and recommendations based on your analysis first versus endless graphs and trends etc. seek to deliver real value not just analysis. It is not enough to know what data tells you , it is important to know what decisions/ strategies might be useful based on that data analysis.
This step is the culmination of the consulting process, where the consultant communicates a compelling, persuasive message to the client. It's achieved by structuring the message logically, utilizing the MECE method and 'the Minto pyramid principle'. 'Less is more', with minimalistic messaging, clear storylines, engaging visuals, and compelling arguments. Strong, unambiguous recommendations should be included, outlining action items with quick wins for the client. Overall, successful communication and presentation of insights require a combination of strong analytical skills, strategic thinking, & effective storytelling. By doing so, consultants empower clients to make informed decisions and drive positive outcomes for their organizations
It's all about the business need. IT professionals should be aligned closely with their business colleagues. Our new CIONET cookbook is launched tomorrow. The third edition profiling 25 DIGITAL LEADERS from across Europe. I have authored all three editions.
The best advisors develop hypothesis and test against the data to arrive at insights, but the most important step is to weave those insights into a narrative story about the business. People won’t remember the specifics of what you told them a week later, but they will remember how you made them feel. Humans have been telling stories since we first were able to sting two words together, so we are genetically programmed to pass on and absorb information through stories. A narrative story that makes the audience feel the business in their bones will be much more likely to result in actions that create value.
I am strong believer that "less is more" when it comes to communication and presentation. I would certainly suggest that it is always helpful to see a very short executive summary capturing the salient points and conclusions, supported by key findings and evidence. Graph are always easier to read than tables, but they need to be labelled properly and the right graph is required for the right conclusion
The data analysis process is not a one-time event, but a continuous cycle of improvement and iteration. You need to monitor and evaluate the impact of your recommendations, by measuring the outcomes, feedback, and changes. You also need to update and refine your analysis, by incorporating new data, questions, and hypotheses, as well as learning from best practices and feedback.
For consultants, flexibility is key. Maintaining an open mind and being receptive to corrections are essential traits during consulting assignments Adjustments based on internal assessments and client feedback are essential. A considerable amount of thought and effort needs to be put into the design of feedback mechanisms. Don't hesitate to modify the approach as necessary. Iterative processes with built-in feedback mechanisms are optimal for achieving success. Documenting lessons learned is essential for future projects.
Based on the insights gained from the analysis, suggest improvements in the business processes. Also, it’s important to continuously monitor the performance and make necessary adjustments. This is an iterative process of learning and improving.
Throughout the process, it's essential to maintain a structured approach, constantly validating assumptions and methodologies to ensure the integrity of the analysis. Additionally, incorporating feedback from stakeholders and being open to refining the analysis based on new information or insights is crucial for continuous improvement and iteration of the data analysis process.
Deve ser tratada de maneira sistemática por meio do controle da coleta de novos dados e do processo de análise considerando o refinamento dos critérios adotados, além do aprendizado com as análises que podem sugerir melhorias como p ex a ado??o de solu??es de Inteligência Artificial.
It’s extremely important to triangulate insights from data with insights from experience. Data can be easily manipulated to tell the story you want to tell. Usually it leads to a confirmation bias. For example, if you’re doing sales forecasting. Do run your fancy forecasting models, but also ask for estimates from your on ground sales force who know the market closely. If you see significant gaps between the two numbers, brainstorm and then arrive at a final number. This combination of data with experience is the best recipe for making robust business decisions. Ignoring either of these two factors will lead to inefficient decisions.
During data analysis, maintaining a clear perspective on reality is crucial. Continuously validate assumptions and hypotheses as the data unfolds. Having a solid grasp of both systems thinking and reductionist thinking is beneficial for knowing when to apply each approach effectively. Utilizing the Pareto principle (80/20) can aid in prioritizing tasks during consulting assignments. Developing empathy for stakeholders involved is perhaps the most important aspect. It helps consultants make sense of the data better and ensures that recommendations are aligned with stakeholders' needs and concerns.
Assessing data is a constant in a Management Consulting career. Some suggestions to consider: 1 - Assess data without over-analyzing: it’s important to understand and be clear about the problem we are trying to solve. 2- Invite different approaches and ideas: consulting offers an amazing opportunity for a diversity of thoughts and ideas. This can lead to meaningful insights worth exploring. 3 - Establish context and leverage data to derive potential insights: a logical approach based on market practices can yield significant value if context and data are well understood. In a connected ??, there is an array of different approaches and perspectives while assessing data. Having a clear objective is paramount to avoid unnecessary work.
The task to ‘analyse data’ is but one process step in a far more complex value chain. In my experience, clients were often seduced but the expected benefits of investing in one of these steps, without really understanding how to derive value from outcomes, rather than analytical inputs. I worked with many data-driven organisations and the best way I found to explain this was the acronym ADIKIWI: Aperture, Data, Information, Knowledge, Insight, Wisdom Impact. Each stage requires specific infrastructure or human contributions e.g. Data is a collection, transport and storage issue; knowledge and insight are derived from analysis, Wisdom is based largely on human experience and judgement, as is deciding the best means of achieving impact.
To enhance the effectiveness of our approach, it's advisable to pinpoint the root cause by conducting a comprehensive analysis that integrates both qualitative and quantitative data. From this analysis, we should formulate targeted problem questions that will guide our investigation. Furthermore, it's essential to propose a set of recommended actions based on our findings, ensuring we address the core issues effectively.