Your team member is hesitant about data verification. How can you ensure accuracy despite time constraints?

Something I learnt as young Captain in the military - "Nothing Checked, Nothing Done". I maintain the same philosophy now in my corporate team where a lot of data crunching happens on a daily basis. If time is really a constrain then at least a randomized dipstick test needs to be done. There's just no option about Not verifying data.

To ensure data accuracy despite time constraints, start by educating the team member on the importance of data verification for reliable outcomes. Explain how errors can compromise results and lead to costly mistakes. Offer quick, targeted training on efficient verification techniques and tools. Encourage the use of automated validation tools to streamline the process. Emphasize that even under tight deadlines, investing time in data verification is crucial for overall project success. Regularly review and discuss data quality issues to reinforce its significance and motivate adherence to verification practices.

I have found education happens in layers and levels. Sharing the why, how(impact), and when around data verification is invaluable. Additionally repetitive education/reminders during onboarding and over the first few project deliveries can be incredibly helpful and supportive. It's important to note that the skill that is being built is discipline, one of the hardest to build, however, it has the best outcome: trust

To ensure accuracy when a team member is hesitant about data verification and time is limited, primary focus should be on the most important data first. Use of quick checks and tools can help to speed up the process. One can talk openly with team to find ways to be both accurate and fast.

Lorsque le temps manque pour vérifier les données en détail, il est crucial de les visualiser au moins pour s'assurer de leur exactitude et de leur complétude. On ne peut exploiter les données sans les visualiser. Cette étape permet, même en l'absence de temps suffisant, de formuler des hypothèses basées sur les premières observations des données.

To effectively manage time constraints, emphasize the importance of setting priorities in data verification. Guide your team member to first identify the data sets that are most crucial to the project's objectives. This can be done by evaluating how directly each data set influences key outcomes or decisions. Encourage them to consider the reliability of the data sources, focusing on verifying data from less trustworthy or frequently changing sources first. Introduce a risk assessment framework, where data with higher likelihood of being inaccurate is flagged for immediate attention. By adopting this targeted approach, they can ensure that the most critical data is verified for accuracy, while less essential data is checked as time permits.

When dealing with multiple data points/sources, guide the team on the look and feel of the data i.e. the structure and the shape of the data. What does good like and what are the outliers. A quick overview before getting granular. For example, if dealing with date and time - look for date format, earliest and latest data. If your data indicates someone was born in 1800's then most likely they are dead and therefore is that correct and relevant to the analysis!

Instilling a culture of data quality demands significant initial investment, but the long-term benefits are immense. Skipping thorough data validation to meet deadlines can lead to costly mistakes and erode trust in data-driven decisions. Prioritizing data quality initiatives is essential. Focus validation efforts on critical data elements first to ensure reliability. Allocate resources efficiently by addressing high-risk areas without overwhelming the team. Implement a tiered decision-making system where high-stakes decisions require more rigorous validation to maintain efficiency and integrity.

If multiple sources of data are being used, setting priorities for data verification can be a great way to start. Start by verifying the most essential data, since that will be the backbone. Next go by the order of source credibility, from lowest to highest.

To prioritize data verification, start by identifying the most critical data sets for the project’s success. Assess their impact on key decisions and evaluate the reliability of their sources. Focus on data with higher risks of being incorrect or outdated. Allocate resources to verify these high-priority data sets first. Continuously review progress and adjust priorities as needed based on emerging information. This targeted approach ensures that the most important data is accurate, while less critical data can be verified as time permits.

Data verification must be conducted in periodic installments, by data stewards within the organization, and not just be an analysis driven endeavor. Completeness and accuracy of data are non-negotiable in high performing analytical teams. A variety of options exist to automate data verification: Python libraries (pandas, scikit-learn, great expectations, etc), SQL, No code tools (Alteryx, Rapid Miner, Tableau, Power BI), etc. You can get scrappy with it and use Excel power query as well. Additionally, build data lineage docs and data dictionaries to strengthen your data reliability.

Utilizar ferramentas automatizadas para validar dados não só acelera o processo como também reduz significativamente o esforço manual. É importante que, mesmo em prazos curtos, a equipe compreenda que investir na verificação de dados é essencial para o sucesso geral do projeto, evitando a correção de erros que demandam tempo no futuro.

Automation alone doesn't work all the time, it requires verification or proof read for all the required data fields too, considerations to be taken by data analysts, project engineers or QS to get the measurable quantities, duly vetted and signed by the respective team members and proceed.

Automation is a powerful tool for streamlining data verification, particularly when time is limited. Start by introducing your team member to software that can automatically validate data against specific rules or patterns—whether it's checking for consistency, detecting duplicates, or flagging outliers. Demonstrate how these tools can drastically cut down on the manual effort involved, allowing the team to focus on more critical, complex tasks. Walk them through the setup process, ensuring they understand how to configure the tools correctly to suit the project’s needs. Additionally, emphasize the importance of monitoring the automation outputs to catch any anomalies that might slip through.

I love automating as much of the checks as possible. Humans make mistakes, it’s part of life so that double check being automatic just supports life. This is the purpose of technology is to add to human nature, not fight it!

Simplifying the data verification process can significantly boost efficiency, especially under tight deadlines. Collaborate with your team member to break down the verification workflow and identify streamlining opportunities. Start by consolidating similar checks into a single step to avoid redundancy. For instance, if multiple checks assess data integrity, combine them into a unified process. Evaluate each step to ensure it adds clear value and consider eliminating or merging redundant steps. Encourage a critical review of existing methods and promote creative thinking to enhance the process. By refining and optimizing these steps, you make the verification process straightforward and quicker, improving overall efficiency and accuracy.

Consolidate Checks: Merge similar steps to reduce redundancy. For instance, at Tech Mahindra, I combined various data modeling checks into a single, streamlined process. Remove Redundancies: Eliminate steps that don't add significant value, as I did by automating repetitive tasks in supply chain analytics. Rearrange for Efficiency: Prioritize high-impact checks early in the process. Encourage Critical Thinking: Empower team members to challenge and improve methods. Automate: Use tools to handle repetitive tasks.

Streamlining workflows and removing unnecessary steps make it easier for teams to follow best practices. Start by mapping existing processes to identify areas for simplification, such as redundant steps or manual tasks that can be automated. Standardized procedures for data collection, processing, and validation reduce variability and ensure consistency. Clear guidelines and templates help teams follow the same steps every time, minimizing the risk of errors. Automating common best practices and centralizing this information contributes to a "single source of truth." Strive to have improvements to be consistently applied across the organization, enhancing data reliability and efficiency.

Alle, die an den Datenanalysen arbeiten, sollten die Augen offen halten für Verbesserungen und Beschleunigungen. Wenn möglich, sollte so oft wie möglich die gleiche Datenbasis verwendet werden, um eine Vergleichbarkeit der Ergebnisse zu gewährleisten. Wenn eine Datenaufbereitung als Basis für mehrere Auswertungen dient, sollte diese im Prozess nur einmalig durchgeführt werden. Alle, die daran mitarbeiten, sollten jederzeit Verbesserungsvorschläge einbringen können und dazu ermutigt werden.

Generally, taking a longer time to complete analytics work to maximise quality is in the best interests for all as opposed to getting it done quicker with lower quality. Its not always this simple though because getting 100% perfection may either not be possible or take way too long to achieve. Analytics work is not the same as software development or a helpdesk function where theres a clear definition of done. Work may be produced and it may tell a story or it may not and needs refinement. Stakeholders will prioritise both speed and quality so some judgement is needed to determine whether the quality threshold is good enough and this usually comes from experience as well as over communicating the assumptions and limitations.

Emphasize to your team member that balancing speed with quality is crucial, particularly in data verification. While it’s important to meet deadlines, remind them that prioritizing accuracy over speed ensures long-term efficiency. Rushing through verification can lead to errors, which not only compromise data integrity but also result in more time-consuming fixes later. Encourage them to focus on verifying critical data points thoroughly, even if it takes a bit more time upfront. Reinforce that a meticulous approach now prevents costly setbacks and rework, ultimately saving time and effort in the long run. This mindset shift from speed to quality will foster more reliable outcomes and a more efficient workflow.

Making sure the data is optimal is essential to obtaining the objectives sought out. While having the data quicker can be desired, having quality data is much more essential. Prioritizing speed over quality can end up becoming a very costly decision in the long run.

In large corporations, it is far better to re-checks and perform verification instead of trying to meet deadlines. Sometimes, error in data can trigger more menacing issues, such as payments to vendors can be incorrect or business strategies can be based on invalid assumptions. Thus one can make the effort to take one more day and bear the burnt of late submission rather than costing organisations and financial ruin.

In data-driven decision-making, the quality of decisions far outweighs the quantity. It's better to make fewer, well-informed decisions than numerous poor ones. Even with a sound decision-making framework, if the underlying data is flawed, the outcomes will be as well. This principle of "garbage in, garbage out" remains critical. Rushing to deliver analyses without thorough data validation compromises the integrity of decisions. Prioritizing rigorous data validation ensures that every decision is based on accurate, reliable data, leading to better, more sustainable outcomes for the organization.

Every data project is a learning opportunity. While data cleaning and verification can be boring, time-consuming and challenging, these should be viewed as crucial steps in improving future processes. Encourage a culture of documentation and learning. Encourage the Team to document challenges encountered, solutions implemented, and potential improvements. This documentation serves as a valuable resource for post-project reviews and identifying areas for process refinement. Implement a continuous improvement cycle. By analyzing past projects, the Team can identify recurring issues, develop standardized procedures, and invest in tools or training to address them. This iterative approach helps to optimize data quality processes over time.

Verification and validation should be part of the ongoing process and not a separate activity. If you make it a separate activity it will never happen. Having said that, data validation is important as visualization. What the point of fancy dashboards if the data it serves cannot be trusted