The management of data throughout its lifespan is data curation. The data lifespan lasts as long as the information is of interest to analysts and researchers or as long as it may be utilized and repurposed to provide value. As curation is the process of maintaining, caring for, and displaying physical items in a specific manner, data curation is the same for digital assets.
Data Curation helps not only in management but also in processing the entire lifecycle of your company. The techniques quickly expanded beyond research data management to various other sectors, where data scientists, analysts, and consumers refined them. Data curation activities might vary depending on the field in which they are used.
Data Curation Process:
- Data preservation typically involves collecting, storing, and managing data to ensure it is not lost.
- Data cleaning is the process of removing mistakes and inconsistencies from data.
- Data integration may include normalisation and transformation to combine data from disparately structured databases.
- Metadata management is the process of maintaining and producing data about curated data, will make it easier for consumers to filter information and identify essential data points.
Why Need Data Curation in your Organization?
1. Potential ML Models:
Machine Learning algorithms have made significant progress in comprehending the consumer market. AI composed of “neural networks” interact and identifies patterns using Deep Learning.
On the other hand, humans must intervene initially to lead algorithmic behaviour toward effective learning. Curations are places where humans can add their expertise to what the machine has automated. As a result, organisations are better prepared for insights by preparing for intelligent self-service processes.
2. Ensuring Quality Control:
A large amount of data is pointless. Data selection is the key to determining which information is pertinent to a company or organisation and which is not. Data curators serve as quality control, ensuring that only good data is retained in the system. Because of this, it will be simpler for both current and future users to believe the data and utilise it effectively.
3. Dealing with Data Swamps:
Many organisations store data even when they are still determining its utility and currently have no prospects for it. This data is usually unstructured and stored in what is known as data lakes. But when there is no data governance, categorisation, or plans for valuable data, data lakes become data swamps. In these swamps, any remaining valuable data may be lost forever.
The data curation process helps to turn data swamps back into data lakes. After curation, data becomes more organised, with metadata added to categorise it, thus making valuable datasets more easily discoverable.
Conclusion:
Data has transformed from an auxiliary result of corporate operations to a potent strategic asset as business intelligence and advanced analytics emerge as major facilitators of better strategic decision-making. Data curation mitigates this risk by ensuring that data is structured, documented, cleansed, enriched, and protected before it reaches the data lake. Furthermore, data curation approaches may assist in restoring data swamps to data lakes.
