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Organisations with big data should learn to find ways in order to manage a massive volume of varies data. With the help of data modeling you can complement the data curation process by creating a framework. This framework guides how data sets can efficiently and accurately be integrated into new analytics applications.
Following are the 8 effective tips for better Data Curation:
- 1. PLAN FOR ACCURACY AT THE SOURCE:
In order that the accuracy is maintained, you need to validate the data at the source rather than to assess its accuracy later. Different practices should be used for data gathered in-house and data from other sources.
- 2. ANNOTATE AND LABEL:
If the data sets are annotated and labeled it becomes easier to manage data sets and troubleshoot problems.
- 3. MAINTAIN STRONG SECURITY AND PRIVACY PRACTICES:
You need to maintain strong security and privacy practices. Since large curated data sets can pose a risk if they are attacked by hackers or insiders. Separating personally identifiable information from the rest of the data is what enterprises should consider. You need to create strong and effective governance model in order to provide stronger security.
- 4. LOOK AHEAD:
Managers need to have an idea regrding how analytics and machine learning apps are using data sets and work backward in order to improve how the data is aggregated.You should also build repeatable, transparent processes for how you clean the data.
- 5. BALANCE DATA GOVERNANCE WITH AGILITY:
There should be a balance betweeen data governance and business agility which organizations need to strike. There are ways to strike this balance which include engaging users, sharing experiences and also focusing on the most-used data first.
- 6. IDENTIFY BUSINESS NEEDS:
Data will hold value only if it is able to satisfy a business need. Therefore,it is important to identify business needs.
- 7. BALANCE ANALYTICS USERS AND DATA STEWARDS:
Apart from having a centralized data governance effort, you also need to include the analytics users as part of this process. With the help of centralized data stewardship roles it can help to complement the data governance process.
- 8. PLAN FOR PROBLEMS:
If you wish to have a robust data curation process and data modeling strategy, than admins are required to account for imprecision, ambiguity and changes in the data.