Your client doubts the data accuracy in your analysis. How can you convince them of its reliability?

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A structured approach you can take to convince the client about data accuracy starts with explaining your process. Showing them specific workflows opens up transparency which then builds trust. You can show them the source of documents and how you maintain an audit trail for easier verification. Validation techniques like reconciliations and cross-checking help ensure data accuracy. What works for me is implementing quality controls. Regular review and audit of your work demonstrate a higher level of responsibility over the data. Most importantly do not forget to ask your client. Understanding their concerns allows you to address them directly and accurately.

It is really important to get the right data, and that is why it is important to choose the right staff or right company to work with. It should be explained to customers where, how and how long it took to obtain the data, and then it should be explained which analyzes are used and what, how and how long it will be done in the light of the data obtained. It should be shown what has been achieved with similar work you have done before and the customer should never be lied to about it, open and transparent is very important.

One thing I have found in my experience useful is firstly ensuring that the report/presentation covers the methodology used to gather data including the source of your data. One must always be careful of being biased and collect data based on conviction. Always right the limitations of the research in cases where they are missing evidence to your data.

Opening the door to having the methodology scrutinized allows for constructive feedback and improvement. If clients have specific concerns, one can address them directly by explaining how your chosen methods mitigate potential biases or errors. In some cases, one might even consider running alternative analyses using methods suggested by the client to demonstrate the robustness of your findings. This collaborative approach not only helps overcome immediate doubts but also fosters a relationship of trust and mutual respect. By being open to discussion and willing to explain your methodological choices, you demonstrate professional integrity and a commitment to delivering accurate, reliable results.

Firstly, it is important to conduct regular catch-up meetings with client so that you can explain your methodology in simple words to the client well before you do your analysis to avoid such situations and save a lot of time. By simple words, I mean your meeting should involve all the stakeholders and you can explain the methodology in business language rather than going into analytical formulas.

Even in a process that is complex from end-to-end, prefer many simple and easy-to-explain steps over few highly complex steps whenever possible. When highly complex methodologies are unavoidable, either due to the nature of the work or the quality of the output, focus on widely accepted methods first, and document any and all deviation from those accepted methods. A non-technical audience will find comfort in well established methods applied with transparency in mind.

Two perspectives are always better than one in science. Also makes the data more believable. A few things to show depending on the situation - IOA, IRR and a methodology clearly described to allow replication.

Actually, concentrating on what we know is far less valuable in most cases than focusing on we don’t know. Data/analysis in the public domain has little market value. Furthermore, quantitative models (regardless of the type) all contain error terms. The biggest and most undervalued information is contained in those error terms. Understanding those known and unknown risks and knowing how to incorporate them into one’s decision matrix is a skill that most have not developed. For example, while the error term of a regression has a zero mean, individual observations do not. Just a thought that more should consider before they share their results to the fourth decimal point.

We implemented rigorous error-checking procedures to ensure the integrity of our data. This included using cross-validation techniques to verify the robustness of our models. Additionally, we performed consistency checks by comparing results across different datasets and time periods. These processes help to detect and correct errors, significantly minimizing inaccuracies. By meticulously checking for errors, we provide strong evidence of the reliability of our results.

Must have comprehensive quality control measures in place, including automated data validation and thorough manual reviews. These measures must be designed to minimize the risk of errors. Walk through your client in these processes to demonstrate how we ensure the highest accuracy. Also maintain detailed audit trails to track data handling and any modifications. This ensures full transparency and accountability throughout the analysis process.

When sharing your work, explain how you checked for mistakes. Use methods like double-checking and making sure everything fits together. This proves your data is accurate and trustworthy, showing you've been careful in your work.

By checking your errors before going to the client, itself gives an evidence of your method of working which is accurate. To prove that, explain the formulas in the analysis that you’ve put and try to indulge the example you’re giving somewhat business oriented rather than using technical jargon. This way client will understand your analysis and at the end will be satisfied.

Una forma muy simple y efectiva es conciliar cada uno de los datos individuales, analizando su consistencia y exactitud, validándolos de forma cruzada si existe esa posibilidad o confirmando la integridad del mismo para luego alcanzar un resultado del análisis certero y confiable.

Mentioning peer review or collaborative efforts during the analysis is a good method to establish credibility. Inform your client whether additional analysts or specialists have looked at your work and offered suggestions. This external validation, which shows that your conclusions have withstood scrutiny from other experts in the field, can be a potent tool in persuading them of the validity of your analysis.

As someone who works with risk management, I can say that having controls is important. To safeguard you and your team from client doubts about data accuracy in your analysis, you need controls. One way to do this is to have team members, whether peers or leaders, double-check to ensure the appropriate methodology was followed and there were no issues in obtaining the data that could have compromised it. All of this should be appropriately documented.

By demonstrating a thorough peer review process, you can bolster the credibility and reliability of your analysis. Tell them about the detail review process: Explain the review criteria and feedback mechanism used. Address raised concerns: Clearly communicate how peer feedback was incorporated into the analysis. Showcase consensus: Emphasize agreement among reviewers on key findings and conclusions. Transparency is key: Share review reports or summaries to demonstrate the rigorous process.

Critical assessment helps to ensure that the data and analysis meet the standards, having your team look at the analysis minimize the bias you can't identefiy, and the feedback ensures the whole project will have a focused prespective on the subject.

One solution could be through providing detailed documentation and evidence to support our analysis. This includes (eg. raw data sets, analytical models, case studies) that underpin our findings. This transparency helps in verifying the accuracy of the data and addressing any doubts comprehensively.

Be cautious not to let historical findings and patterns influence your mindset to the point where you subconsciously aim to achieve the same results.

To further reinforce the reliability of our analysis, we draw parallels between our current findings and historical data or trends. Our results align closely with established patterns and predictions, which validates our analysis and demonstrates consistency with known industry behaviors. For instance, the observed market growth in our study mirrors the steady increase reported in previous years. Where discrepancies exist, we provide logical explanations, considering factors such as recent market disruptions or changes in consumer behavior. By situating our findings within this broader context, we help you see our analysis as part of a larger, consistent picture, further validating its accuracy.

Using historical data too can help and explaining to the client that it doesn’t imply that the past will repeat itself but it actually can guide in decision making

When you attempt to compare your analysis with historical data, you are implicitly accepting the accuracy of that data. However, this can be problematic if the systems and methodologies used to create the historical data are unknown. Let's say you wanted to check it by reproducing the historical data. Reproducing the same results using our own system could be time-consuming and might yield different outcomes. Additionally, today's conditions, systems, and methodologies differ significantly from those in the past. Therefore, comparing current findings with historical data may not be feasible and should not be included as a convincing argument for the client.

By establishing the historical accuracy of your data, you build confidence in its reliability for current and future analysis. But be cautious to not let the historical results influence the current findings. Clearly outline data sources and transformations over time. Align your findings with historical records, industry benchmarks, or previous studies. Demonstrate how your analysis aligns with historical trends and patterns. Highlight continuous data validation to ensure accuracy.

Based on my small experience and particularly, in today’s fast paced world, continuous learning is not just an option but, a necessity. By constantly seeking new knowledge and skills, I can adapt to the ever-evolving demands of the industry and stay ahead in my career.

It is important to ensure that the same data sources that the company uses for internal tracking and communicationa ia the same data streams you are using. While your methods can enhance their current data streams you shouldn't conflict or try to persuade the usage of your data. Instead align together to make data streams more cohesive and informative.

The term continuous improvement is misleading. While an approach to never stop innovating improvement brings to mind better solutions for all, it is not that simple in reality. If you only innovate improvement you lose the value of standard practices, which provide firm ground to innovate from. The term is better practiced in reality as continual improvement. This is brought about by budgeting in the time for improvement. This ensures decision makers are over watching and involved in the improvements and thus ensuring the improvements are aligned with their plans. This also brings out special skills from within to bring about these improvements.

Algo que acho super útil é o diálogo com seu cliente, esclarecer as suas dúvidas, mostrar de fato que você domina o assunto e estará disposto a esclarecer da melhor forma possível. Confian?a é algo primordial, assim como ter a clareza e seguran?a do que está fazendo. Sem dúvidas deixar o cliente seguro e satisfeito.

Take a logical broad level perspective to understand why the observation makes sense. Communicate the same to your client as to how the inference makes sense from a business standpoint. If a historical trend is observed, draw a parallel as stated earlier. However, if some new trend is noticed, mention how similar outliers have been noted in the past as well for some other analysis. Communicate the impact of the observation on the business.

The issue of data accuracy is very different for small and large businesses. I can connect on the latter. When it comes to data accuracy, the biggest risk in the setting of a global organization is that the same metrics may differ depending on the IT system (e.g. SAP or Procurement tool), geography, team (e.g. BU finance or global FP&A), and other factors. It is thus instrumental to establish (1) the ultimate data owner, and (2) the single source of truth. Add some reasonable governance around the data, and that would be an awesome first and second step towards data accuracy.

STOP trying to convince! Pause. Take a step back. Reconfirm the information previously provided and then show the calculations based on that information. Now, let's take a business valuation vs a business appraisal. There is a distinct difference! How? Based on who is using these values and for which purpose. A business valuation is for IRS valuation as well as for bank lending purposes. A business appraisal is for the sale value for a buyer. Both are important, but each have a distinct purpose and increase or decrease values accordingly. A business appraisal will look for more structure in operations, # of customers, and risk management, not just their revenue numbers. So be open to what you're trying to solve for and for whom.

Ask what concerns the client has.This could actually open up something that you had not realised was causing your client to have concerns.By asking a pointed question you can then resolve unanswered questions and thereby gain your clients trust as you have opened up dialogue between your client and yourself.Which in turn will help you gain your clients trust.