You're facing data quality issues with stakeholders. How can you effectively communicate and address them?

?? C'est l'heure de jouer les détectives avec vos données ! Faites un diagnostic précis des erreurs—pensez incohérences, données manquantes. Expliquez les soucis sans trop de blabla technique, juste les faits. Un bon vieux rapport visuel peut grandement aider à clarifier les points sans endormir votre audience.

To tackle data quality issues, start by identifying and understanding the root causes. Conduct a detailed analysis to spot inaccuracies, inconsistencies, or missing data. When sharing these findings with stakeholders, use clear, non-technical language to ensure they grasp the key points. Presenting a concise overview not only shows your understanding but also sets the stage for collaborative problem-solving. Effective communication is key to aligning everyone on the path to improving data quality.

You must first fully understand the data from the stakeholders, more likely than not they may either already be aware of the issue with reason or not be aware. If the problem is the latter, then you should start discussing and analyzing by asking questions like (Is this the proper source...., Why is the data like this..., How can we go back and fill it in... Can it be filled in, is it needed). This should start to get you on the right track

Identifying data quality issues is the first and most critical step. Conduct a comprehensive audit to detect inaccuracies, inconsistencies, and gaps in the data. When presenting these findings to stakeholders, use data visualizations like charts or heat maps to make the issues more accessible and easier to understand. Avoid technical jargon and instead, focus on how these data flaws could potentially impact business decisions or outcomes. By providing clear, visual examples of the issues, you can ensure that stakeholders fully grasp the significance of the problem.

By experience, it often comes from non-analytics focused business rules, semantics or bad input (for manual workloads, mostly). By addressing the bias and bad quality information poor quality data would provide stakeholders with, they might not resist the understanding that it’s their responsibility to produce good quality data. Data is a product like any other product the company produces and, therefore, should be treated like so.

?? Montrez leur ce que ?a co?te, pas juste en euros, mais en opportunités perdues ou en décisions bancales. Un petit exemple concret ? Si les données client sont erronées, adieu upselling efficace ! Impliquez les avec des histoires qui résonnent, pour qu’ils saisissent le bazar que peuvent créer de mauvaises données.

The scope and size of the problem may not be inherently obvious to the stakeholders. You should take initiative and guide them through the envisioned process and how this problem will affect that vision.

Engage stakeholders by demonstrating the real-world impact of data quality issues on their specific roles and the organization as a whole. For instance, show how inaccurate sales data could lead to poor forecasting, or how inconsistent customer information could affect marketing campaigns. Use relatable scenarios to make the consequences of poor data quality tangible. Hold a workshop or meeting where stakeholders can see firsthand examples of how data quality issues have affected past projects or decisions. This involvement fosters a sense of urgency and personal investment in finding solutions.

By engage them one should see it as “making them an active part of the data production lifecycle”. I had a really messy issue with a team once, regarding the data quality of legacy data input systems, and fortunately the processes I’ve put into place helped a lot. I literally showed the results and powerful insights that good quality data could give them (and how that could make them look good in front of the board xD) and put observability processes into place to help me with the auditing. Since then, they became almost advocates for good data quality input.

Often times, you'll need quality checks from business users, as they'll understand their data the best. Bringing in these users should be done to not only validate data and modeling, but also so that they understand the problems and what the impacts might be. If they rely on a model that has bad data in it, you need to communicate with them that the modeling is currently unusable and work on either a solution to the underlying problem, or figure out what else could work in the meantime while development is underway.

??? Maintenant, on passe à l'action ! Présentez des solutions simples et directes. Que ce soit une nouvelle routine de vérification des données ou un petit investissement dans un outil malin, assurez vous que chaque solution est taillée sur mesure pour vos enjeux spécifiques. C'est comme choisir la bonne clé pour le bon verrou.

Start small give them an MVP or demo of the project or a presentation that highlights how it would look and feel to get them hooked. But make sure that this is feasible before presenting otherwise you run the risk of losing the stakeholder's trust, which is more important in the long run.

After clearly communicating the issues, it's crucial to propose actionable solutions. Tailor your recommendations to the specific needs of the stakeholders, whether it's implementing data validation checks, adopting new data management tools, or providing additional training for data entry staff. Ensure that each solution is feasible and aligned with the organization's resources and goals.

? Soyez clair sur le temps et les efforts nécessaires. Certaines choses sont des quick wins, d'autres, plus des marathons que des sprints. En étant transparent sur les étapes et les défis, vous construisez une confiance solide et vous évitez les mauvaises surprises en cours de route.

Think big, Start small! In data analytics, starting small is key, especially for organizations that are maturing in this area. Remember, the complexity of obtaining, cleaning and structuring data is considerable. Often, Data comes from a variety of sources, enriching and transforming it correctly, is essential to ensure accurate insights and decisions. A gradual approach allows you to refine processes, identify problems, gain experience and demonstrate value, before taking on larger projects. As the organization progresses, it improves its ability to handle complex data, ensuring that decisions are reliable and effective. This progressive growth is essential for sustainable and successful developments.

This is a great time to under-promise and over-deliver. Knowing that someone will almost certainly be upset over this miss, it's a great opportunity to make someone happy. By beating the deadlines that you've set, you'll move towards reestablishing the trust that should be there between coworkers. It's also a great time to work with stakeholders to ensure that this doesn't happen again!

Each evolution in the process should be shown like a real milestone for them, like the process of positive reinforcement. Also, bad data should set an alert for them. Data quality is part of a data driven culture, and for many people generating data it isn’t a straightforward process. Teaching with patience and a clear/honest understanding of the whys behind bad data is key, otherwise they will keep losing their focus.

Depending on what the analysis is for involving the stakeholders in more often can help make sure the delivered item is the intended one whilst also fostering a good connection with them which is better in the long-run.