You're tasked with a market analysis project. How do you navigate using questionable data sources?

In instances where the source is questionable following points should be considered to mitigate risk of errors or ambiguity Scrutinize the data for signs of obvious errors such as outliers, incorrect formatting, and implausible values. Look at past data performance from this source to see if it has been accurate. Identify the proportion of missing data and analyze patterns to determine if the missing data is random or systematic. Look for missing periods, geographical gaps, or other incomplete segments that could affect the analysis. Identify any biases due to data collection methods, such as survey design or respondent selection. Research the history and reputation of the data source within the industry

In a situation where you had to rely on questionable sources, a few things to follow are: - Understanding the source's credibility and to what extent you can rely on the source. - Fact-checking the information and calculating the bias can ascertain how much can we depend on the source. - If there was a previous analysis done from the same source, evaluate the performance of the model or output. - If there is no option left but to rely on the source, then establish the fact before producing the results. We can only do so much to make the analysis reliable if the input is not.

To navigate a market analysis project with questionable data sources: Verify Sources: Cross-check data against reputable sources for credibility. Assess Methodology: Evaluate how the data was collected and its adherence to standards. Identify Bias: Look for potential biases that could skew results. Use Multiple Sources: Combine data from various sources for a comprehensive view. Document Limitations: Note any limitations or uncertainties in the data. Consult Experts: Seek input from industry experts to validate findings. Focus on Trends: Emphasize trends and patterns instead of relying on specific figures. These steps help mitigate risks associated with questionable data.

When dealing with questionable data sources, it's best to be cautious. First, check the credibility of the source and try to cross-reference the information with other reliable sources. Identify potential biases and validate the quality of the data by looking for errors or inconsistencies. If needed, consult experts to confirm accuracy. It’s wise to use a mix of sources to balance your analysis and clearly document which data is problematic. In short, handle questionable data carefully and base your conclusions on a thorough and well-grounded analysis.

If you do not have time or funds to complete the study again, doing a small sample with a clean data source can sometimes help you to understand the quality of the "questionable" data source. Most questionable data in my experience comes from poor audience targeting or sampling errors. For example, many times my voter polls were questioned, and I was brought in as the "consultant's consultant" to determine why someone else's poll was vastly different. With election polls, it was amazing how many times I would ask: "Did you sample only registered voters"? and they often had not. Additionally, with voting polls, best practices mean you measure your results with "likely voters" in addition to making sure you have "registered" voters

Em qualquer projeto de inteligência, a primeira etapa é o mapeamento e verifica??o das fontes de dados a serem usadas. Variáveis como intervalo de tempo, última atualiza??o do dado, formato numérico e de escalas, varia??es mensais, semestrais e anuais além da reputa??o das fontes que divulgaram o acesso aos dados é essencial.

When working in a large retail organization, with a sizeable database of millions of transactions per year. The first step I took in all sales analysis was to look to the "Bible" the Returns Book. This report broke down sales by various categories (sales division, merchandise division, etc.) by the last quarter and the last year. If the number found, matched the book, the data was correlated, and analysis (drill down on specifics) could begin without worrying about the accuracy of the data.

Confiar em uma única fonte de dados, especialmente se for questionável, pode ser arriscado. Diversificar as entradas, incorporando múltiplos fluxos de dados, oferece uma vis?o mais abrangente do mercado. Essa abordagem n?o só ajuda a validar dados duvidosos, mas também enriquece a análise, proporcionando uma compreens?o mais detalhada das dinamicas de mercado. Comparando e contrastando diversos conjuntos de dados, é possível identificar outliers e obter insights que poderiam ser ignorados ao depender de uma única fonte.

Abordagem excelente! Quando lidamos com dados questionáveis, consultar especialistas é uma a??o estratégica imprescindível. Profissionais veteranos do setor, especialistas em pesquisa de mercado e acadêmicos com profundo conhecimento no mercado em análise podem oferecer um contexto valioso, identificar possíveis vieses nos dados e sugerir fontes ou métodos alternativos para obter informa??es precisas. A expertise desses especialistas n?o só economiza tempo, mas também garante que sua análise de mercado esteja solidamente ancorada na realidade, minimizando o risco de decis?es baseadas em dados imprecisos.

Como etapa final do projeto é importante estabelecer um cronograma de revis?o contínua das fontes de dados utilizadas. Isso porque as atualiza??es podem variar de período, alguma variável pode mudar ou até mesmo o dado usado pode "caducar", ou seja, perder a relevancia no projeto. Com um processo de revis?o contínua estruturado minimiza-se muito análises falhas ou viéses que possam impactar nas tomadas de decis?o.

I once received over 250k records of payments for a particular medication. The client wanted a full analysis of payment types, amounts, where dispensed, etc. The first thing I did was CLEAN the data. Remove all negative amounts and zero amounts. Then with the remaining data removed the outliers. The final number of records was 56. I wrote the client that we needed more clean data which they could not produce. Sometimes, when the data is "questionable" you have to ignore it and get additional data.