Engaging Data Explains:
Big Data and DataVault
Knowing how and where to find the needle more easily, and where in the specific haystack it resides
Big Data has been a hot potato topic for more than a few years now, and this phenomenon will play a central role in the future of commerce. Collecting, collating and comprehending Big Data will no longer be a matter of commercial interest; it will instead increasingly become a commercial imperative.
It should come as no surprise then that investment in technologies related to Big Data is already becoming almost ubiquitous. A report from NewVantage Partners, which collected executive perspectives from 60 Fortune 1000 companies, found that 97% of them invest in Big Data and AI initiatives. NewVantage also discovered that the vast majority of this investment (84%) was focused on deploying advanced analytics capabilities to enable business decision making.
Big Understatement
And when we use the term ‘Big Data’, it’s reasonable to conclude that ‘big’ is an understatement! For example, in 2018, Internet users generate approximately 2.5 quintillion bytes of data every day. That’s 912 quintillion bytes every year! And 90% of this data has been generated in just the last five years. The rate of growth and development of this curve is exponential.
Thus, it’s one thing to recognise the importance of Big Data, and quite another to be prepared for it. We’re talking about a veritable avalanche of information! In many cases, utterly unstructured information. Indeed, Forbes noted in 2019 that 95% of businesses cite the need to manage unstructured data as a problem for their business. Which, given the sheer scale of Big Data, is hardly surprising. Making the most of Big Data is not so much searching for a needle in a haystack; more like looking for a needle in a universe entirely comprised of haystacks.
This reality means that implementing the best business intelligence solutions will become essential. Dealing with the sheer volume of Big Data will demand this. And data warehousing is one element of this process that will be critically important. The analytical qualities delivered by this aspect of the overall Big Data management process will prove critical in the success of the efforts of companies to benefit from the information explosion.
Data Vault 2.0
That’s where Data Vault comes in. Data Vault 2.0 comprises a raft of sophisticated architecture and techniques that enable businesses to both store current and historical data in a singular and easily accessible location, along with the ability to create analytics based on this information. Data Vault is effectively a unique design methodology for large scale data warehouse platforms, ensuring that Big Data is dealt with more quickly, more efficiently, and more effectively.
Data Vault offers several advantages over competitors. The first reason for this is that it’s possible to convert any system to Data Vault determinations. This means that existing objects can be translated to Data Vault entities, and every single item will have a corresponding match in the new Data Vault architecture. Every main definition can then be mapped by hubs and every relationship between these via links. This means that the whole operation is more flexible and user-friendly.
Another significant advantage of Data Vault is its enhancement of agility. This is particularly important, as the ability of network software and hardware to automatically control and configure itself makes it easier to deal with the almost unfathomable scope of Big Data.
Smaller Pieces
Data Vault makes it possible to divide a system into smaller pieces, with each individual component available for separate design and development. This means every constituent part of the system can have its own definitions and relationships and that these can be combined at a later date by related mapping. This makes it possible to develop a project steadily yet still see instant results. It also makes managing change requests much more straightforward.
Another asset of the Data Vault approach is that it applies to numerous different systems. This means that separate sources can be transformed into Data Vault entries without any laborious procedures being involved. It is particularly advantageous in the contemporary climate, as almost every enterprise system relies on several different data types from various data sources.
The Data Vault modelling technique is thus adaptable to all types of sources, with a minimum of fuss. This makes it much more feasible to link different data sources together, making analysis more joined-up and holistic. It is well-known that being the entity that is the most adaptable to change is vital across a wide variety of niches, and this applies in the rapidly evolving data analysis environment.
But possibly the most compelling reason to choose Data Vault is that our offering provides companies with a method of standardisation. With Data Vault implemented, companies can standardise their entire DWH system. This standardisation enables members of the company to understand the system more easily, which is undoubtedly advantageous considering the innate complexity of this field.
Meeting the Needs
It is commonplace for complex and sophisticated solutions to be delivered to business users, which nevertheless fail to understand and adapt to the company’s actual requirements in that area. Everyone wants to show off their fancy piece of kit, but often developers aren’t as keen to listen! This can manifest for a variety of reasons. Still, the important thing to note is that Data Vault is designed to meet the requirements of the business, rather than requiring a business to reorganise itself to comply with the needs of the package.
This is important at a time when the dynamic complexity associated with data is escalating. Enterprise data warehouse systems must provide accurate business intelligence and support a variety of requirements. This has become a critical reality in a business marketplace in which the sheer volume of data being generated is overwhelming.
Data Vault solves these problems with a design methodology that is ideal for large scale data warehouse platforms. With an approach that enables incremental delivery and a structure that supports regular evolution over time, Data Vault delivers a standard for data warehousing that elevates the whole industry.