Customizing Data Storage
Data storage is at the core of any data-driven organization. The right data storage solution empowers institutions to extract meaningful and actionable insights from the massive amounts of data available, while at the same time maintaining a high level of information security and providing the records and logging necessary for audits. This is especially important in the detection of money laundering and terrorist financing: not understanding your data and using outdated and ineffective solutions can lead to massive fines for your financial institution.
A Changing Data Landscape
With the rapid changes in the data storage landscape that have taken place over the past 20 years, financial institutions often face severe difficulties in designing and implementing a data strategy. However, these changes have all been extremely positive in terms of technological capabilities, even if they have significantly increased the complexity of the available options. After all, it is now possible, at a very reasonable cost, to store and mine petabytes of data – something that was not even thought feasible when most financial institutions were first implementing their data strategies in the early days of digitization.
In order to take advantage of the technological achievements in data storage, the initial approach should be to identify the specific needs of your financial institution, and then design a system that is customized to those needs. There are generally two major areas to be considered: how will data be ingested, and how will data be retrieved. One consideration that is significantly less important than in former days is replication of data, or the total amount of data storage needed: advances in storage technologies have made it much less expensive on a per-byte basis, so it is not necessary to try to optimize for the smallest storage size possible at the expense of fast ingest, fast retrieval, or granular security.
Data ingest and retrieval are often associated with two paradigms: schema on read and schema on write. Schema on write, which is the traditional method of relational database systems, requires the data structure to be defined before ingesting data: this slows down the on-boarding of new data sources, but queries that are consistent with the schema tend to be quick. Schema on read, often used in key-value data stores, applies structure to the data when it is retrieved, allowing unstructured data to be stored. This makes on-boarding easy, but it pushes the work of understanding and structuring the data to retrieval, potentially causing slow queries and missing data errors.
Designing for Your Needs
Modern approaches, pioneered and used by the tech giants to satisfy their big data requirements, combine these two technologies into a hybrid system, initially landing data into large key-value stores that then feed purpose-built relational databases for specific business tasks.
Adopting these pioneering ideas from the tech sector will allow you to get the best return on your investments in data, optimizing your storage costs and the actionable information your analysts can extract to improve your business. And not only can these databases be customized for your business needs, you can also customize the stores for your security and audit requirements.
There are many options in data storage and usage today. However, by taking an approach that prioritizes understanding what your needs are, you can use these amazing technological advancements to give your institution a tremendous advantage over your competitors.
At Clovis Technologies, we work with a wide range of data technologies, giving us the capability to help our customers understand how to maximize their investments in this crucial area. Contact us to find out how we can help your institution get more out of your data.