How can you effectively gather data from multiple sources during a consulting engagement?

Effectively gathering data is one the most crucial steps in consulting, it involves: defining goals, identifying diverse sources (internal documents, interviews, industry reports), ensuring data quality, integrating information, using tech for efficiency, maintaining security, and communicating insights clearly to stakeholders.

Two important aspects of effective data gathering during a consulting engagement are defining clear objectives and prioritizing relevant data sources. Defining objectives provides a clear direction for the project, while prioritizing data sources ensures focus on valuable information.

Planning before collecting data is crucial. It helps focus on what you really need, where to get it, and how to use it wisely. A data collection plan, covering goals, methods, sources, and logistics, ensures a smart and effective approach.

- I agree, you also need to look at when you need it. This will enable you to choose a good method of data collection that matches your time frame.

Define Data Needs: Consultation Objectives: Understand the overarching goals of the consulting project. What specific outcomes or improvements does the client seek? Align data collection efforts with these objectives to ensure relevance. Key Questions: Identify the critical questions that need answers. These questions should be directly related to the objectives and should guide the data collection process. Scope and Focus: Define the scope of the project and the specific areas where data collection is necessary. This helps in targeting efforts effectively rather than gathering unnecessary information. Data Types: Determine whether quantitative, qualitative, or mixed-method data is required based on the nature of the objectives.

- In my experience, you also need to consider the language and education level of your subjects. Making sure to use a method that suits them, making sure that it won’t be difficult for them to understand by using “hard” English if they aren’t native speakers of English.

Selecting the appropriate data collection method depends on various factors like research objectives, available resources, nature of the research, and ethical considerations. Often, a combination of methods (mixed-methods approach) may provide a more comprehensive understanding of the subject matter. -Surveys and Questionnaires: (online or paper based) -Interviews -Focus Groups - Observation -Experiments -Case Studies -Digital Data Collection: Collect data from digital sources such as social media, website analytics, or online platforms. Extract insights from user behaviors, interactions, and trends.

The definition of data needs to start from the organization setup and structure. Any organization is either organized into departments or entities or both which handle a part of the transaction. The stakeholder in that part of the organization will be an expert in the data that need to be processed at his/her center of excellence(CoE) or domain. This will need to be identified and relevant questions need to asked to that person pertaining to the data. Once a consultant feels that he/she has a fair understanding of the setup of the organization then the complete story will have to be stitched up to understand the big picture. In this way the common theme of the data gathering exercise will come through.

Capitaliser sur les sources de contenu issus de précédents projets clients réalisés par votre cabinet pour identifier les axes clés d'analyse de l'existant et de restitution de vos recommandations et plans d'actions. Les IA Generative comme Microsoft Copilot for MS365 aident grandement dans cette phase de recherche et de synthèse.

One thing i've found helpful in my data collection process is to start by reviewing the client's Data Governance policy, process, any tools they're using. This will give you a heads up on the data quality, and what your next steps should be. If they don't have one, you may have some extra work. If they do have one, you will have a guide as to how clean the data is and what might be missing. Once I've collected the data, to ensure the quality I use metrics and a series of questions as a checklist to validate accuracy, completeness, and other facets centered around regulatory norms.

Address Missing Data and Outliers: Develop Protocols: Establish protocols for handling missing data or outliers. Determine whether to impute missing values or exclude outliers based on valid reasons.

I think ensuring data quality in consulting is like putting together a puzzle with pieces from different boxes. You check each piece to make sure it fits (data validation) and use the same rules for everyone (data governance). It's about cleaning up any dirty or damaged pieces (data cleaning) and making sure they all speak the same language (standardization). Integration tools help magically connect pieces, ensuring a complete and accurate picture for better decision-making. It's like being a careful puzzle master to create a reliable and clear final result.

Storing the metadata about the data is also powerful. Over time the metadata helps us analyse the effectiveness of the data we collected. An example would be storing client preferences, pshycographics, demographic, industry classifications etc.

Great set of pointers in the post.. Here's a some more I feel we can look at - Before starting clearly define the objective & outline the goals of data collection - Identify Sources including relevant databases, documents, experts, and stakeholders. - Identify which are the high-impact sources aligned with mentioned project objectives. - Are there tools for automating, data scraping & analysis to streamline the process. - Data Compliance, accuracy, completeness becomes key for reliability. Bad Data = Bad Results. - Involve more relevant stakeholders. - Continue to review and refine basis feedback and findings.

Effectively gathering data in consulting involves defining clear objectives, identifying relevant sources, and creating a comprehensive plan. Utilize technology, engage stakeholders, and ensure data security. Cross-verify information, employ techniques like interviews and surveys, and adapt to dynamic environments. Meticulously document and regularly review the data collection process. Thorough analysis and effective communication of findings contribute to informed decision-making and project success.

Define the problem statement/ develop the hypothesis. Define the information needs to validate the hypothesis or understand the root cause. Present the information list during the kick-off and understand the information client may or may not have. Agree on timelines and mechanism to receive the information either as documents or walkthrough of solution or gather information during meetings and publish the understanding from the meeting with the stakeholders.

Documentation and Transparency: Document Procedures: Maintain detailed documentation of data collection methods, cleaning processes, and any alterations made to the original dataset. Be Transparent: Provide transparency about data collection methodologies and any limitations or biases in the collected data.

Deeply study the scope, challenges, objectives etc. to identify and document the types of data which are needed. Then list down the names of various data sources (including tools, repositories, SPOCs for manually maintained data etc.) after discussing with all the relevant stakeholders. Proceed to gather data which should be stored in properly organized folders (named by data source or type or in any other logical manner) in a shared cloud repository like MS teams channel. If it is possible, it would be better to create this shared repository first and provide permission to folders representing their data source to appropriate stakeholders so that they can directly share their data there. This will help to speed up the organizing of data.