You’re managing a team of data analysts. How can you ensure their work is accurate and effective?

1. Define Clear Objectives: Start by setting explicit goals and defining the scope, outlining expected outcomes. 2. Establish Standards and Guidelines: Create standards and guidelines for consistent, high-quality analysis to maintain accuracy and effectiveness. 3. Encourage Collaboration and Peer Review: Foster collaboration and peer review to facilitate knowledge sharing and identify errors. 4. Provide Ongoing Training and Support: Ensure continuous training and support to keep the team. 5. Regularly Assess Results: Evaluate results against predefined metrics to identify areas for improvement. 6. Promote Open Communication: Cultivate a culture of open communication, where team members feel comfortable seeking help.

Do not discount the power of analytics. Data needs to tell a story. Figure out the key question first, then use the data to craft a narrative supported by the data. Do not try to "force" the data to meet a goal. The goal should be a measurement, not an absolute target.

Data analytics challenges such as privacy, data quality and lack of resources are key barriers. It is important to note that companies that successfully overcome these challenges will not only reap the benefits of data analytics, but will also be able to drive innovation and strengthen their competitive position. The first step, therefore, is for companies to open up in order to capture assets and data in the first place. It is also a matter of cultural change and enabling the collection and analysis of data as a first step. For this to succeed, the business and technology sides of the enterprise have to come together, too.

Very general question. Think of this like a cook in a kitchen. a) What needs to be done b) in what order? c) at what quality? d) by whom? e) when to check the food? f) how to balance quality control with efficiency?

1. Improve data collection and organization methods. 2. Ensure data is cleansed regularly. 3. Normalize (or standardize) data for inconsistencies, errors, and uniformity. 4. Establish a data management platform that will enable data to be integrated across departments and platforms. 5. Perform data segmentation to promote a more focused and detailed analysis of your data.

Standards and requirements on data integrity and consistency are necessary to be established prior to data management operations

It is important to have clear standards or something similar to standards, instruction books, a checklist to ensure that these points are covered in the report, points for review, periodic training meetings, and a debriefing meeting to ensure that the objectives are understood and implemented properly.

For Example: To ensure accuracy and effectiveness in analyzing sales data for marketing strategies, we can establish the following standards and guidelines: Data Quality Standards: Define criteria for data accuracy, completeness, and consistency. Ensure that the sales data collected is reliable and error-free. Analysis Methodology: Outline the steps and techniques to be used in analyzing the sales data. Specify statistical methods, software tools, and algorithms to ensure consistency and reproducibility in analysis. Documentation: Require thorough documentation of data sources, variables, and analysis procedures. Validation and Verification: Implement checks and validations to ensure the accuracy of analysis results.

in establishing standards and guidelines you would like to Identify and communicate the data analysis software, frameworks your team should use. This ensures everyone is on the same page and leverages tools that everyone is familiar with, which can facilitate collaboration and efficiency. then you need to establish data Cleaning and Validation Protocols among the team.

Too many organizations don’t establish clear governance and processes for data. Without this, functions will apply their own approach (or worse not have one) and it will hinder the organization from cross-functional insights and AI opportunities in the future.

While Data analysts tackle complex tasks, psychological safety is crucial. It's about creating the confidence that team members can take risks without fear of being penalized. Research shows that learning and innovation flourish in such environments.

I always enjoyed working with my teams as it related to data. It has to start with the analysts understanding what they are looking at. If you want to ensure great data, an analyst should be able to look at the information and determine instantaneously is it reasonable? Building a strong and collaborative team not only builds redundancy but allows for knowledge sharing and discussion. The makeup of the team is also critical - it should not only be technical but a mix of business personnel and technical to allow people to understand the why. I have always tried to make things fun .... peer reviews can be intimidating or it can be an opportunity to learn more and be innovative on how to present information. God luck.

En effet c’est impératif, les collaborateurs ont souvent bien plus de connaissances et de compétences que celles que nous connaissons. En les impliquant nous construisons un écosystème de compétences en interne, l’implication d’expertises externes est aussi essentielle car cela permet d’avoir l’état de l’art sur le sujet.

It is critical that the analysts understand the data sources, quality, and the intended purpose of the findings to improve quality.

Great point. Comunities of practice (CoP) are also an alternative to enhance performance of Knowledge Management in a Project and the colaboration among team members. Share points of view in pairs or in small groups could be a smart tool for team maturity growth, not only for the project itself but for the whole organization.

It's very easy to attract talent, however companies needs to focus in building "retention strategy". How to retain talent is key ...and training, support are both top priorities

Always, training and support for employees is key, not only in this context. - Without training and support you loose employees and teams.

Training and support is an obligatory requirement for anything and anyone should do for any stage in data analysis or any activity. The consistency helps the individual to achieve the perfection.

1. Link work to business goals. Understand the "why" behind each analysis. Aligning your team’s work w/ the company's strategy ensures impactful, high-quality insights. 2. Build trust. Learn about each member’s strengths, growth areas, ambitions. This builds trust, improves workload management, and strengthens team rapport. 3. Build relationships w / partner teams. Strong connections across the organization provide valuable context for your team's work and are essential for framing meaningful analyses. 4. Open communication. Regular check-ins and open dialogue about findings are crucial. An environment of accessible and constant feedback fosters collaboration and dynamism within the team. Also helps you w/ (2)!

Leading a team requires special effort to ensure that all delegated work is carried out in accordance with the needs, goals, specifications and expectations of the employer. Providing continuous training according to the gap between market demand and the team's knowledge and skills will help complete work effectively and efficiently, according to the target time. Like family members, the team works needs consistent support from upper levels of management so that they are able to work well and correctly. Providing training support is an investment. Also saves your time, energy and money!

Je recommande plus que surveiller et évaluer, d’insérer une approche d’amélioration continue qui va au-delà mais permet d’intégrer une culture de la performance et de l’amélioration. Une impératif pour conduire vers l’excellence.

Data analysis does not stand still, it is something alive and constantly evolving. Monitoring via continuous monitoring and evaluation using previously defined KPIs is one of the keys to success. Data is mobile and dynamic, and this makes the manager's role essential in monitoring reports, as these are undoubtedly increasingly the subject of strategic decision-making. The world today is more dynamic than in the past and more and more senior executives are making quick decisions with the help of data.

Monitoring and review are such important steps to continuously improve, remain relevant and stay up to date. Find ways to monitor and review automatically, by using systems to do the work and provide you with insightful exceptions, knowing that the rest of your data is performing as it should. Also book time to frequently review otherwise life will provide you with a lot of other reasons not to get around to this and not focus on continuous improvement.

Monitoring and controlling outputs from your data analysis team would ensure you deliver with minimal errors. This is achieved by doing a deeper analysis of the data results against data input/source in relation to the target goals.

There’s no polite way to say it, but monitoring data analytics (both done by others and yourself) requires putting in a lot of tripwires. Tripwires, in turn, require redundancy. A lot of extra information has to be processed in order to be able check if the right things add up, and frankly, a lot of time needs to go into figuring out what flags to build into the system.

The effectiveness of data analysis is often linked to the story that can be told from the insights gained. It’s all too common to see projects such as these abstracted away from the context in which the results need to be used!

When managing a team of high performers like data analysts, it’s important to remember to start with the end in mind. Each member of the team has to understand the end goal and desired outcome so that they can tailor their outputs accordingly. In addition, when leading a team like this it’s important to ask questions vs making statements, ensuring they have freedom on how to think vs being told what to think. A culture of awareness, intentionality and accountability will ensure the team consistently over delivers on desired outcomes

Practical Tips: (1) ensure the data analyst team fully understand WHY are they analyzing the data, and link it to the decisions that the outcome will support. (2) Balance out the 3 V's of data (volume, velocity , and variety). (3) Train the team on the business model (or function) so they know what insights will add value.

Data analysis entails several steps. A lot could go wrong in any of the steps. The error can cascade down and often amplify as one reaches the last step to generate results/output. While it is important to follow the best practices outlined in this article at each step, I often do ‘logic checks’ on the output to make sure the results are not grossly off the mark. For example, if the analyst has done a forecast of air traffic 3 years out, I would calculate on my own 2-3 other key metrics (e.g., number of aircrafts needed to cater to this traffic) and check if these metrics are within an established range as per the industry standard. If they are not, I would go deeper into investigating and questioning the assumptions, process, etc.

While managing teams, especially data analysts end-to-end intervention is required. Starting from setting a clear picture of the goals, the team should be encouraged towards the planning-implementation-monitor cycle through regular interventions of realtime feedback and learning when required.