With the business world in a constant state of flux, flexibility is more important than ever for organisations of every stripe. Data-driven organisations have fared best; those with an enterprise data architecture that allows them to understand change and adapt to the volatility of current markets and supply changes are more resilient than their counterparts.
However, most dimensional and normalised data modelling techniques aren’t designed to respond to fast changes like this. Data Vault modelling, on the other hand, helps to address this – equipping organisations with greater speed and flexibility for their analytics needs.
Origins of Data Vault modelling
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.
The Data Vault approach, created by Dan Linstedt in the 1990s, was designed to make these benefits accessible to everyone. It was followed by Data Vault 2.0 in 2013, offering a suite of enhancements centred around NoSQL and Big Data as well as the introduction of integrations for unstructured and semi-structured data.
Linstedt’s aim was to enable data architects and engineers to build a Data Warehouse faster i.e. with a shorter implementation timeframe, and in a way that more effectively addresses the needs of the business.
What are the business benefits within a Data Vault approach?
The main benefit here is self-evident: the shorter an implementation cycle, the more time and money saved. Shorter cycles also help business requirements for the Data Warehouse and ongoing enhancements (through the introduction of new sources, for example) to stay valid up until completion, avoiding shifting goal posts that can impact budgets.
Many organisations will also opt for a Data Vault approach because of the flexibility and scalability that it offers. The agile approach to project management is very popular, and closely aligned to the concepts that underpin Data Vault modelling. Combined, the two can offer a real nimbleness to the data strategy of any business, eliminating the cost implications of having to expand data storage and processing capabilities by scaling as needed.
Parallelisation is a point to consider too. Loading data into the Data Warehouse means that it needs to be synchronised at fewer points. This means faster data loading processes, a huge help in tackling big data volumes and real-time data inserts.
The historical tracking of data inherent in the Data Vault approach also means that data models can be audited without unnecessary complications. The structure of a sophisticated Data Warehouse means that this data can be audited easily and can offer built-in security mechanisms that make compliance with data security requirements simple.
What are the challenges?
While these strengths are a major draw, like other data modelling approaches, Data Vault also has some limitations that organisations need to consider.
The most obvious is the sheer amount of data objects compared to other approaches – for example, tables and columns. This is because a Data Vault approach separates information types.
As a result, the up-front modelling effort can be bigger and there can be larger numbers of manual or mechanical tasks involved to establish the flexible and detailed data model with all its components.
These challenges need tackling specifically if organisations are to avoid time-consuming manual labour during the modelling process. The key to this is automation.
How can automation solve them?
Within the Data Vault, there are layers of data:
- Source systems, where the data will be created or originate;
- A staging area that receives the data from the source system, and models it according to its original structure;
- A core data warehouse containing the raw vault, a layer that allows data to be traced back to the original source system data;
- A business vault, essentially a semantic layer where business rules are implemented;
- Data marts, structured to the requirements of the organisation. A finance or marketing data mart, for example, would hold relevant data for specific analysis purposes.
The staging area and the raw vault are the layers best suited to automation. Implementing automation here can save data architects a lot of time and improve the overall efficiency of a Data Vault approach.
How do businesses build on the Data Vault approach?
Data inefficiencies shouldn’t be holding organisations back anymore. It’s now possible to build a sustainable data ecosystem, integrating technology and software, that supports the overall data strategy for many years. Tools that complement a chosen data modelling technique can be a real catalyst for improvement when it comes to the work of analytics teams and individual experts who are reliant on a performant data environment for their day-to-day work.
Data Vault modelling can prove an integral part of that environment. With a robust approach designed to maximise the benefits that a Data Vault approach offers, those at the coal face will benefit from vastly improved performance when running analytical models or workflows – enabling organisations to optimise the value of their data at speed. Data experts can rest assured that their data can be audited at any point, they can load large volumes of data without any problems, and that they can reproduce historical queries as needed. This will enable organisations to make informed business decisions that will lead to better outcomes for the business and the customers it serves.
