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Key Acronyms you should understand to survive in the Disruptive Digital Marketing arena: DVT

PUBLISHED ON 24 FEBRUARY, 2015

The definitive recipe for success in the contemporary marketplace is to stay in the loop of emerging technologies that are obstinately disrupting the way we do business. But if you, like myself, lack some substantial technical background, you might find yourself struggling with some of the key concepts.

Don’t panic – you’ve come to the right place, where even the toughest cookies of the digital marketing world are made easily digestible for the common folks like you and I.

Previously, we enabled you to show off in front of IT guys with concepts like Real-Time Bidding (RTB), Data Management Platforms (DMP) and Dynamic Creative Optimization (DCO). This time around, we would like to analyse the enigmatic letters DVT.

Forrester defines Data Virtualization Technology as follows:

“Data virtualization technology adds an abstraction, or services, layer to IT architectures that enables information from multiple, heterogeneous data sources to be integrated in real time, near real time or batch, as needed.”

Let’s break this seemingly complex definition apart:

 

“information from multiple, heterogeneous data sources”

It’s a well-known fact that even small businesses obtain their data from a variety of sources, let alone large enterprises. This data may proceed from CRM, BI, data warehouses, social media, offline, and etc. The traditional model for monitoring and utilising big amounts of disparate data is ETL (Extract, Transform and Load), which consists of:

1. connecting to different sources and pulling the data out of them;
2. cleaning and standardising this data to bring it to a unified format;
3. and, finally, storing it in a new warehouse.

While this data processing model stands out due to flexibility in data crossovers, it nevertheless leaves us confronted by a number of problems:

• maintenance: ETL requires physical space to store data, which can become quite costly in the case of large enterprises;
• processes: it takes a lot of time and effort to define how the data sets will be extracted, saved, related to one another, etc. Moreover, as original data sources evolve over time, an extensive amount of work is required in order to keep the initially defined processes and rules for data extraction and transformation up to date;
• privacy: storing delicate information may have additional complexities, such as forcing organisations to deal with licenses and legal framework.

“adds an abstraction, or services, layer to IT architectures”

Data Virtualization Technology, contrary to ETL and data warehousing, does not physically store any information. Instead, it connects to disparate data sources, pulls out the necessary data straight from there to then facilitate momentary data crossovers.

It is important to point out the abstraction layer provided by DVT. Of course, there are infinite technical processes running behind the scenes, such as standardisation and cleaning of the data, but the big advantage is that they are invisible to the end user. What they get in the end is exactly what they need – combined information coming from disparate sources.

“integrated in real time, near real time or batch”

Another advantage of DVT is their immediacy. Without the need to process and store information, it can be accessed and combined on the fly, accelerating data discovery and eliminating the unnecessary intermediate steps.

Furthermore, the list of the benefits provided by DVT also includes saving money, since the costly installation and maintenance of complex IT equipment is no longer required. However, make sure to check with your DVT provider that all data standardisation processes are defined in a way that meets your company’s needs; otherwise, all the benefits will be lost to heaps of unconnected and irrelevant data blocks.

So what is the actual practicality of this technology? For the end user, namely, a large enterprise with an elevated amount of data sources, DVT could in the long run come to substitute a data warehouse. Its proven advantage of data standardisation ensures there are no information inconsistencies, which is a common problem when using multiple data warehouses. This, combined with the ease of use facilitated to the end user and the immediate availability of data, turn DVT into the definite business path for data discovery and analysis initiatives.

Conclusion

Without any fundamental alterations to our current work system, DVT facilitates data discovery process by seamlessly connecting to our disparate data sources and leaving technical details irrelevant to the end user out of sight. This way, DVT grants us quick access to all our business assets stored across various systems, and thus enables agile and effective insight management.

We’d love to hear from you! Does your company utilise Data Virtualization Technology or the time-proven ETL model?