How can you avoid risks with aftersales data and analytics?

→ Make sure your data is accurate and consistent by regularly checking for mistakes and fixing them. →Set clear rules for how data should be handled to keep it reliable. →Use tools that automatically spot and correct errors in your data.

- Implement robust data security protocols. - Conduct routine data audits. - Anticipate potential issues in data through predictive analytics.

In my experience, it’s important to have a snapshot of the data after a report is published. Archived data can always help to check for errors and restore correct data if need be.

One thing to add maybe is how long do you retain data. Depending on the nature fe.online vs demographic data you might want to considering different retention dates which then again have an impact on storage cost.

What I notice in so many databases of both small to large enterprises is duplicate data, the incompleteness of data and no correct data on install base. Quite some of these challenges can be addressed with tools besides the data governance practice. The impact is huge for after sales as well as before sales

→Only let authorized people access sensitive information. →Keep data encrypted so that even if someone tries to steal it, they can't read it. →Check for weak points in your security regularly and make them stronger.

Additionally you can have a compliance department to monitor logs, activities of the users and handlers, and identify potential threats involving PCI breach internally.

it is essential to have a Robust IT team who must ensure the collection of data frequently and adapt blockchain system to ensure the safety and security of key insite or infromation regarding the customer personal information data. Also access management on collective information who can see what .

- Always comply with your local data privacy regulations at all cost (e.g. GDPR, CCPA, LGPD, etc). - Perform a periodic assessment of any gaps vs the local regulation, as your not only fraudster might exploit it, but as well your competition.

→When combining data from different places, use the same rules and formats to avoid problems. →Follow a careful process when moving and changing data to prevent mistakes. →Keep the meaning of the data consistent when you put it together.

A customer data platform can help integrate your data and centralize it. To harmonize your data a good datamodel could help integrate it. It's not just about integrating datasources it also how do you store depending on what type of data it is. Hot or cold data. I think integrating data to and making it ready to use for all sort of business activities from marketing, to service to sales, is still one of the biggest challenges companies are facing

→Double-check the accuracy of your data analysis to make sure it's right. →Keep your analysis methods up to date so they stay helpful. →Watch out for unfair influences in your analysis and fix them.

Making sense of data is for me the first part and indeed bi, ai... can help you. But how do you activate those insights. How you operationalize your models, how do you make them ready and understandable for business and integrate them in business users their daily activity is very important. I see Data science team developing models that are never used, because business doesn't understand the outcomes and trust their gutfeel

→Share your data results in a clear and simple way, using pictures and stories if needed. →Explain the background of your data findings so people understand them better. →Listen to feedback from others to improve how you share data.

Remember, it's important to always keep an eye on these things to make sure your data and analysis are trustworthy and useful.