To ensure accuracy & manage a massive dataset: 1. Data cleaning 2. Data validation 3. Data normalization: standardize data formats. 4. Data transformation: convert data types. 5. Use data visualization tools 6. Statistical analysis 7. Machine learning algorithms for pattern detection. 8. Data quality checks 9. Document data processing steps. 10. Version control: track changes. Additional strategies: - Divide dataset into manageable chunks. - Utilize data management tools - Collaborate with data experts. - Automated data processing scripts. - Regular data backups. To stay afloat: - Break tasks into smaller steps. - Set realistic deadlines. - Prioritize tasks. - Take breaks. - Seek support.

Large datasets can be difficult to maintain and utilise effectively if they are not in a consistent format. You can ensure that your data is in a usable format through data cleaning processes such as the removal of duplicate rows and checking that the data types of your columns are appropriate, efficient data cleaning can be done in tools like excel and R with packages like "janitor". Also, you should regularly refresh your data from the source if appropriate to ensure you have the most up to date position. Additionally, data validation processes should be done to ensure that your dataset is high quality, accurate and doesn't have common issues such as missing rows, a common validation process is the comparison of different time periods.