Data is considered an asset for any organization, including financial, airline, e-commerce, or universities. However, even with the availability of data at one’s disposal; It is only viable unless and until the data is organized, managed, and retrievable with little efforts. Big Data is around for decades; however, its rolling in the mainstream for the last 5 to 10 years.
Table of Contents:
- Why Data Curation
- Data Accuracy
- Security & Privacy
- Annotation and Labeling
- Storage Architecture Engine
The organization got revolutionized with the decisions made over the availability of data. Data Curation is often referred to as managing data through its life cycle. Data Curation starts from creation and initial storage, consumption, archiving upon obsolete, and deletion. The data, during its lifetime, passes through numerous phases of transformation. Data curation aims to provide surety that the data is stored in secure, reliable, and efficiently retrievable. The idea behind data curation is to ensure that organization data is reliably retrievable for research purposes or used for external tasks.
Why Data Curation
Today’s high-value organization invests a big budget into data analytics. As of 2019 alone, according to Gartner, an estimated 44 Billion Dollars were alone were spent by the organization. The spending was performed to integrate big data analytics into their existing databases with only aim to implement data curation. Harnessing and leveraging data to resolve critical business problems is a top priority of influential organizations today. In a single given time, organizations only utilize 20% of their data, and the rest of it is scattered across several data storage units.
Data volumes are growing at an exponential rate and spread across heterogeneity of data sources. The data is required at an appropriate time has become a relatively costly and time-consuming process. Data Curation involves the process of manual cataloging and data sources integration before analytics tools do their part. Then, the challenges include eliminating empty fields and duplicated data, misspellings issues, columns split, and enriching data from external third party sources to provide more context to the data.
Let dig deep into the Challenges that organizations usually face with having tons of data and best-recommended practices that are required to be adapted to have the data secure and efficiently secured within the prescribed time limit.
Data Accuracy is one of the mightiest challenges faced during the entire data life cycle. If the primary source data is not accurate, the whole building block build over the data will fall like Jenga blocks. The wrong decisions performed over the data will prove to be a disaster for any management, and this is what the majority of the organization faces in current times. Data Inaccuracy can ripple the data during creation, acquisition, cleaning, and even during the data annotation. For example, if a healthcare data for old age persons has inaccurate information regarding medical history, a wrong decision based on the wrong history could lead to severe consequences.
Duplication is a challenge faced when the same set of information exists over the different data sources. The data transformation might change one source and leave the other, and using the wrong data, later on, can turn out to be another disaster in the waiting.
Personally Identifiable Information or PII means the personal information available within the data. This is to be used for later data curation. Once a clear understanding of PII is available, various approaches have to be applied to separate personal information from the rest of the data. There are usually two approaches to perform De-Identification. It is either scrambling the personal information where the data gets replaced with a gibberish date or to remove the data physically.
Security & Privacy
Security & Privacy in this digital age plays a vital role in any organization. With hacking, data infringements, and data break-ins on the rise, the company’s management loses their sleep to protect the data. Encryptions do a viable job to keep the data protected even in cases if it falls into the wrong hands to ensure the security & privacy of the data.
Encrypting data is always a point-scoring to prevent any data breach. Even with secure firewalls in data centers, there are chances of data getting but without proper decryption technique, and the stolen data is of no use. There are possibilities to decrypt the data with immense computing power and brute force algorithm, but the question arises; how long will it take to break an encryption key.
Annotation and Labeling
Annotation and Labeling is a good practice that enriches the data by adding metadata. The accurate tagging will lead to proper transformation and processing of data for later stages. With vast volumes of data spread across various locations, tagging and indexing data with appropriate metadata use allows data curation to efficiently and effectively.
Storage Architecture Engine
Storage Architecture Engine should in place from the very first day. The anticipation of data volume generated over time usually is kept insight to ensure the optimal performance to store and retrieve the managed data. With the latest open-source tools like NoSQL, Kinesis, and Kafka; it is not challenging to build a highly scalable distributed data engine. With a state of the art in-memory caching Hierarchical Storage, it is made sure that data gets optimized for retrieval at all times. As data created once; however, it is retrievable most of the time during its lifetime; the architecture is to be built to utilize both cache and in-memory databases.
In conclusion, The stakeholders have a precise set of information regarding graphs, charts, and diagrams generated through the curated data. The usage of BI tools over the cleansed data gives the organization an edge to manage the data repository by providing a precise set of information understandably. The organizations ensure higher data protection, data integrity, and accuracy. This is done by implementing best practices for data curation, leading to better decision-making and avoiding usual challenges.