Nowadays, data plays an important role. Before starting something or any business, if you have data, you are one step ahead to achieving success. Because you can use that data to get analytics for your product, results of your product users, and many more data-driven operations, including business finance.
Efficient data curation can help businesses better manage their financial information, leading to improved financial decision-making and overall performance. Incorporating data curation as a part of your overall business strategy can further enhance your decision-making process and increase your competitive advantage.
Data curation is nowadays one of the important factors in data management. Businesses can use data curation for their purposes, such as maintaining important information and research. It organizes data well and gives you clear data sets for later uses. In this article, we will understand what data curation in data science is and how it is useful.
📌 Quick navigation
- What is data curation
- Why data curation is important (benefits)
- Main steps of data curation
- Data curation tools
- Future of Data Curation in data science
- Final words
What is Data Curation?
If the term “curated” refers to collections of items chosen and managed to satisfy the demands of a particular group, then “curated data” refers to a collection of datasets chosen and managed to fit the needs and interests of a particular group of individuals. It should be noted that the emphasis is on accessible files, tables, etc., datasets that can be examined.
The slight but important distinction between “collections of data” and “collections of datasets” should be noted.
To fulfill the requirements and interests of particular groups of individuals, data curation entails managing and organizing a collection of datasets. Dataset collection is merely the first step. We do just that when we store data in data lakes or warehouses. The core of data curation, however, is organization and management.
Data curation’s goal is to make datasets simple to identify, comprehend, and access; this goal necessitates thoroughly defined datasets. Data catalogs are a crucial component of data curation technology, which is the activity of managing metadata. Data catalogs are quickly evolving into the new “gold standard” for managing metadata, making it understandable and useful for non-technical data users.
Why is Data Curation important?
There are several reasons why data curation is important.
Let us understand those benefits that make data curation much more important.
1. Provide Control of Data of a Business
Utilizing data curation can give businesses more control over their data. This is because they can inform data curators about their preferred data collection methods. For instance, a business might divulge specifics about how it prefers to organize, clean, and transform data.
2. Makes Data Understandable
Data curators ensure that all formatting is clear and there are no errors. This makes it simpler for experts unfamiliar with a research area to interpret a data set.
For instance, professionals at the company who are unfamiliar with this research may better understand the data set if researchers clearly communicate the information if data curators execute data curation on the organization’s target audience.
3. Organizes Data that Already Exists
For a business, data scientists frequently work with vast amounts of data. Because businesses produce so much data might occasionally need a defined framework. For instance, an online clothes retailer may gather information whenever a customer clicks on a page, adds something to their cart, and completes a transaction.
To help businesses better understand vast amounts of information, data curators assist in organizing existent data into data sets.
4. Gives You High-Quality Data
High-quality data typically has few errors and is organized in a simple way to understand. A company’s research and information can continue to be of a high standard because the data curation process entails cleansing the data. Ensuring the research is brief guarantees that the data sets are organized properly.
What are the main steps in Data Curation?
Numerous tasks that make up the curating data sets can be divided into the following steps.
Step 1. Selection of the Topic
Make your research objectives clear before you start the procedure. Understanding the subject matter thoroughly may help you decide more effectively which sources to consult for information. When choosing your research topic, consider the following queries:
- What’s the aim of my study?
- What components of my study topic should I take into account?
- What field should this study be conducted in?
Step 2. Start Identifying the Data
To start the data curation process, you can identify important data sources. Choose the best sources you think will help you understand your research topic. For instance, you might gather information from a college campus if your study examines the proportion of college students who regularly attend classes.
Step 3. Start Collecting the Data
You can start collecting data once you’ve determined potential sources for it. To gather your data, you can employ a variety of methodologies, such as conducting surveys or making observations. Data curators frequently take data sets from previously published research, so they may only need to collect data if they are studying a subject for which there isn’t any data available.
Step 4. Start Cleaning the Data
After you’ve gathered the necessary information, you must clean the data to make it understandable and suitable for inclusion in data sets like tables, graphs, and charts. Correcting spelling errors, tracking down missing values or numbers, and spotting inaccurate data entries are all parts of cleaning your data. Cleaning data can reduce the likelihood that your data sets contain errors and guarantee that your information is understandable.
For instance, you might convert each long decimal to a percentage to fit your data into a chart better. Additionally, data curators may eliminate any irrelevant or unnecessary data at this time.
Step 5. Start Migrating the Data
Putting your data into a different format than originally is the last step in the data curation process. Businesses may require a specialized spreadsheet or particular chart as the format for data. Data transformation typically entails changing documents that contain information into documents in a different format.
Data migration from the old system to the new one necessitates data transformation, a common business practice. Data curators might transfer information to new software, ensuring that it stays the same if a company, for instance, adopts new software. Let’s discuss the data curation tools that we can use.
What are data curation tools?
Data curation tools optimize the pre-processing steps of data management. These tools ensure data usability and integrity. These platforms validate metadata using AI and ML, creating insights into the reliable repository. Lets us discuss what are the data curation tools.
- Stitch Data: Stitch Data is regarded as a superb tool because it is a cloud-first and developer-focused platform for a solution for fast-moving data.
- Informatica: Informatica stands out because it offers a wide range of products for ETL, data masking, data quality, data replica, data virtualization, master data management, etc.
- Alation: If you’re seeking a platform that everyone in your company can use to find the data they need to collaborate, this is the best tool for this purpose. Alation will perfectly catalog your data.
- Talend: The open-source program Talend is fantastic to use for data curation. It offers top-notch software packages for data integration that may be used to integrate, purify, mask, and profile data.
📌 Further reading
Future of Data Curation
Businesses and organizations are still using big data concepts. The importance of data in extending the previously unrealized potential for business operation and success has been shown by data. Organizations will increasingly engage in data curation as data volumes increase to streamline processing and analysis, improve operational efficiency, and generate superior results.
Successful businesses can be distinguished from unsuccessful ones by their capacity to monitor and evaluate data on their own promptly. The most prosperous people will outperform their industry rivals and become data curation experts.
Organizations can value and consolidate their data repositories with the help of data curation in data science. Utilizing a clever data curation platform guarantees that a business is fed with accurate, beneficial data to get a competitive edge and take the lead in the market.
In this article, we have discussed data curation in data science. We have discussed why it is important and the main steps of data curation. We have also discussed some of its benefits. So basically, Data curation organizes data, making it discoverable and available.
Users in the business world can also follow the history of data. The procedure classifies data according to several attributes, such as whether it is protected, private, or public.