Why the data vault approach to data transformation works
If the data assets of the organisations are used effectively, data and analytics can help transform the organisation to be competitive in the new digital business reality in which we find ourselves.
When correctly handled, data can enable and accelerates the following transformations:
• New opportunity: With consumers and their financial services migrating online, a host of digitised new services become possible. This is not just a game the agile Fintech players can play but established players can capture new market share and share of wallet.
• Risk Mitigation: Financial services is one of the most regulated industries in the world and for good reason – compliance to mandated requirements is essential. Not having a flexible system to identify and mitigate risk can result in punitive penalties from regulatory bodies and losses due to shifting global crises impacting the financial sector as it did in 2008.
• Customer insights: The biggest organisations in the world today use masses of customer data to predict and influence behaviours. It gives them immense competitive advantage. Data is essential to identify changing patterns of consumption and attitudes and to understand the voice of the customer and thereby offer new services that are infused or driven by data.
• Digital readiness and dexterity: It is essential for banks to fully transform their people with the skills required for digital transformation. AI, process automation, and robotics are all trends that will impact the financial sector in the coming years. Data underpins all these technologies, so the data literacy of staff provides the basis for capturing opportunities.
Start with the data first:
Although there are one thousand insights and analyses that can be done on the flood of data available to the organisations, what is required is a platform that provides clean, analytics ready data, this is the first essential step towards allowing you to accelerate the acquisition of insights form the data.
Disparate and incomplete information stands between events and actions, whereas a versatile data engine is needed and that is one that breaks down the complexity of disparate data, and delivers analytical insights to those who need it, instantly. Accurate information drives better decisions and profitable outcomes. Out traditional batch approach to business is outdated and needs to be modernised to keep up to the speed of business and our empowered, connected digital customers.
What are the best practice data methodologies available to the financial services sector?
Data Vault is a detail-oriented data modelling approach designed to provide flexibility and agility when data volumes grow, and/or when they become more distributed and sophisticated. Businesses that can address these challenges in their data model, are better placed to make faster, more informed business decisions.
Data Vault architecture is an innovative, hybrid approach that combines the best of 3rd Normal Form (3NF) and dimension modelling. This data modelling technique enables historical storage and integration of data from different operational systems and traces the origin of all the data coming into the database.
Data Vault Methodology
Conceptual points of this methodology include:
• Stores single version of the facts.
• Not one single version of the truth - because the truth is subject to change as businesses change.
• All data is stored in the Data Vault.
• Extraction of the truth is done by extracting data from the vault and transferring to information ‘marts’.
• Each mart has its own version of the truth.
Is Data Vault modelling a good choice for your organisation? The answer to this is that it depends on your environment and your specific use case.
Here are some of the major benefits and limitations that will help you decide if a Data Vault would meet your specific data architecture needs.
• Increased agility and flexibility in the overall data warehouse model.
• Supports new real-time data loads.
• Start small and easily adjust the model to fit new data sources /business requirements.
• Lends itself to automation of the data model and extract, transform and load (ETL).
• Automation can be applied to any of these architectures for ETL code generation.
• Increased usability by business users as a Data Vault is modelled after the business domain.
• High performance.
• Data Vault supports near-real-time loads as well as batch loads.
• Terabytes to petabytes of information (Big data).
• Decouples key distribution which enables a high degree of parallelism, due to a reduction of ETL dependencies.
• Historical traceability.
• Supports isolated, flexible, and incremental development (organic growth).
• Dynamic model can be incrementally built, easily extended.
• No rework is required when adding additional information to the core data warehouse model.
• Supports business rule changes with ease.
• Composes automatically and applies Data Vault Principles.
• Composes and automates distribution of data to the Data Vault via ETL Generation.
• Composes and automates ETL for delivery to data marts.
In today’s hyper-competitive business climate, real-time insights and analytics ready data are critical. Users need robust data integration and agile analytics solutions, to make decisions fast.
This element of DataOps, outlined in the foregoing, seeks to bring improvements to data integration by focusing on the practices and technologies for building and enhancing data pipelines to rapidly meet business analytics needs.
In my third article in this series, I will explain how to unlock data value.