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Anyone who has dealt with an Enterprise Data Warehouse understands the pain of managing and interacting with them. Loading, validating and managing data in an ever-changing business environment is an ongoing, yet specialised and complex, task. This means it is costly, frustrating, and laborious for both the business and IT.

Enterprise Data Warehouses (EDWs) offer a highly governed and structured approach, but this structure makes them ‘personnel heavy’ and slows both data ingestion and ad hoc business reporting. The larger the business, and the older the warehouse, the greater the complexity.

Faced with today’s big data challenges, many traditional EDWs are struggling to keep up with the constantly changing demands of the businesses they serve. However, there is another way.

So why is Data Vault 2.0 different from other approaches?

Data Vault 2.0 is a unique system of data warehousing that was created from the ground up to deal with the real-world data challenges that most businesses are facing today. Data Vault 2.0 delivers improved Total Cost of Ownership, greatly enhanced operational agility, and traceable data governance. Specifically designed to deal with business change without re-engineering, Data Vault 2.0 can automate the ingestion and management of data and allow valuable resources to focus on delivering the insights the business needs to stay competitive.


Discover how to create a data lake with easier access to data, faster data preparation, enhanced agility, and more trustworthy data.

No one contests the exponential growth of data; the stats are mind-boggling. More data has been created in the past few years than in the entire history of the human race. Managing, housing and, more importantly, leveraging this data is essential to creating data-driven success. 

For a lot of organisations, Data Lakes are an easier and more flexible alternative to building a data warehouse. They are typically being deployed using one of the many flavours of Hadoop or big data platforms and often get deployed either as a place for ad hoc data discovery or as a data store for the terabytes of data now generated by an organisation. The advantages of data lakes are many, including the ability to store many types of data and to load that data in real-time.

The ability to avoid the cost and effort of defining data structures upfront seems like a significant advantage. You can achieve huge time and cost savings by pushing this task down to the point of consumption - usually the Data Scientists doing the analysis.

Unfortunately, data lakes are not a magical solution. Poorly implemented and unmanaged lakes can quickly become data swamps. Organisations must pay attention to data quality. To create a successful data strategy, you must start with data stewardship, ensuring that you address the organisational structures, processes and cultures that support basic information management principles and allow you to keep your lake clean. By just promoting a minimum practice of information classification upfront, to capture basics like the source, owner and intended purpose, you can ensure data loaded into your lake can be reused by others and support maintenance functions to help keep the waters clean.

In addition to the data veracity problem, big data also poses potential security risks. Too much data with little knowledge of what it contains can be catastrophic. Data is easily duplicated and personal and sensitive data can be potentially exposed to business users that should not see it, let alone from a regulatory or data leakage perspective. A data governance solution ensures that organisations not only protect their information assets but also their customers. 


In the traditional approach, data was governed as it was discovered or brought into the enterprise. It was very much a waterfall process: ingest data, transform and govern it to the highest required standard (or reject it), load it to a central repository, and build a set of data marts for consumption. This process was slow and, by default, all data was elevated to the highest level of governance.

The new approach to governance is all about agility. Aligned with Data Vault 2.0 methods, it’s about: profiling the data, understanding what it will be used for, and then determining the required level of governance. By applying agile data governance, organisations can ensure appropriate controls without inhibiting delivery speed and flexibility of access.

Governed data is reliable, secure and ready to use, while data from an ungoverned terrain has little value for automated analytics and operations. Trillions of gigabytes of information are available in today’s digital world for those who are truly prepared to leverage this as an opportunity to significantly improve organisational decision making.

Certus has deep expertise in Information Governance - Software, Services, Strategies and Solutions.



Meet James

James Hartwright is Certus Solutions Director for AI and Big Data, and has 25 years’ experience designing, delivering, and supporting data-driven insight solutions for major enterprises like across publishing, finance, retail, high tech, and utilities.