Data Vault is generally adopted by Enterprises, but thanks to banks, an individual can avail such facilities too

Since the past few years, banks have been on the verge of achieving digital disruption. Powered by abilities of artificial intelligence and machine learning, banks today can fend themselves from security breaches, prevent fraud, enable hassle free credit scoring and lending and underwriting and more. Banks like DBS Bank, Standard Chartered Bank have also employed artificial intelligence based chatbots (Kasisto's KAI in this case) to handle sophisticated questions about finance management, assist in international transfers, block credit card charges and so on. Now, banks are planning to include storage facilities for data too i.e. data vaults. If implemented successfully, this can open new chapter in both banking and data management.

Big data has played a significant part in determining the future of business intelligence solutions. It has transformed various industries with its key offerings. However, companies have to pay attention on how source data is modeled and also to the ETL (extract, transform, load) process. Moreover, the data architecture of an enterprise must be flexible and adaptable to fast changing market conditions.

What's data vault?

Enter data vault. It is an innovative, hybrid approach that combines aspects of agile, BEAM requirements gathering, CMMI, TQM, Six Sigma and Data Vault Modeling; to enhance the quality and speed of business intelligence projects. It was proposed by Dan E. Linstedt as an alternative approach to the popular Ralph Kimball's Dimensional Modeling schema and Bill Inmon's 3NF data warehouse. Data vaults allow historical storage of data, integration of data from different operational systems, and tracing of the origin of all the data coming into the database. For lower levels, data vaults can help in handling rote tasks like adding data attributes, slices, new sources, augmented sources, historical tracking, deprecating sources and source structure changes at both the ETL and Modelling phases.  Additionally, it also addresses the demand for increased level of security in big data.

Data vault is comprised of three basic components or entities viz., Hub, Link, Satellite. The Hub represents the primary key or business keys of the subject. Link tracks all relationships between Hubs to provide transaction integration between them. Satellite provides the context of the Hub and Link by housing any attributes related to a Link or Hub and update them as they change.

Why Banks?

Banks have a huge advantage of opening data vaults. Just like the money and assets like gold, lease papers, etc., which are owned by us, but banks take responsibility to manage and secure them, data vaults will contain data belonging to an individual or institute but banks will manage it on the behalf of users. Users can load critical information in their assigned data vault and segregate them into various categories. They are also able to remove or modify this data anytime. Also, they can be offered to adjust the setting preferences and permissions for their data stores so that their privacy can be protected to some extent.

Apart from that, banks are naturally immune from the reach of tech companies that mine and exploit personal data. So, data vaults in banks can prevent users' personal data from wandering around the web as various data collectors. Besides, banks have more trusted clientele than other sectors, especially compared against big tech companies. Banks are already dealing with data in terms of user accounts, credit score and more. Therefore, banks are well positioned to make best of these lead-points to start offering facilities like data vault. At the same time, banks also need to take initiatives to foster greater maturity on data science and the understanding of what data ecologies can mean in the banking sector. It wont be far, when banks will be acting as data repositories for data, especially personal data.