Business intelligence has gained considerable traction over the past decade. The explosion of data and the importance it now has on every modern business has led to a dramatic rise in BI vendors offering various solutions to improve the ways in which a company stores, collects and analyses its data.
Modernising your company’s BI infrastructures and practices is of the utmost importance for improving decision making, discovering new business opportunities and dramatically reducing costs. With so many BI vendors offering to do a better job than the other, however, how do you differentiate which one is best for your particular business needs?
To help you with this decision, we have launched a new series to dispel the myths around the topic. This edition will focus on a German Startup called datavirtuality.
History
datavirtuality started in the corridors of the University of Leipzig under the directorship of Dr. Nick Golovin. The company has raised a total of EUR 2 million in funding since March 2012 and specialises in serving medium-sized businesses, BI consultants, and fast growth companies. Existing clients include: OUTFITTERY, Deutsche Post DHL, Windeln.de and more.
Key Offerings and Case Studies
For the majority of companies considering a new BI product, there are two considerations that are of crucial importance: a) cost and implementation of the product, and b) cost and ease of maintenance.
Relating to the former, datavirtuality has an incredibly fast implementation process, with customers generally waiting less than a week. For example, windeln.de, Germany’s largest online retailer for baby products, called upon datavirtuality to implement their BI solution. Within seven days datavirtuality integrated all relevant data sources (SQL, NoSQL, Google Adwords, Postal tracking, inventory data, etc) and reduced the highly manual process of data collection windeln.de previously had.
The solution with datavirtuality also gave windeln real-time access to their data, automatic pre-structuring of data and a central data interface for various front-ends. As such, unlike other BI vendors, where a lot of capital and time is needed to upgrade existing BI infrastructures, datavirtuality’s unique selling point is its quick implementation.
Relating to cost and maintenance, the pricing plan for datavirtuality is flexible – available on both a rent and buy model. Costs of implementation, maintenance and refactoring (in case of changes of the data landscape) are far below a traditional DWH system, because datavirtuality builds most of the database structures automatically. First, a virtual layer is created around all data sources, which allows users to start working with the data immediately. The system then observes how the users work with the data and automatically arranges the data structures in the fastest way. The system also allows user to create its own data models and manage them centrally.
For Springlane, datavirtuality were able to implement a data warehouse and integrate all data sources (including ERP, Redshift, Google, and web shop) in just 2 days. In this instance, the benefit was that datavirtuality were able to reduce the medium-long term costs at Springlane by ensuring their data warehouse automatically adjusted to changing data structures. Similarly, Outfittery also saved up to 90% on the time needed to maintain their data warehouse by implementing datavirtuality’s product – ensuring it automatically adapts to changes of the data landscape. In both use cases, the companies experienced a huge reduction in administration costs.
The two key benefits to datavirtuality’s product, therefore, is its rapid installation process, and the low maintenance it requires.
Limitations
Datavirtuality is a middleware – companies therefore need a front end (like Excel, Tableau or QlikView) to analyze their data with. All common front ends in the market can be connected to datavirtuality.
To benefit from the flexibility of datavirtuality, it’s best to have direct access to the data sources. It is not a problem to connect to data exports such as cvs, xml or json format, but the best flexibility is achieved when datavirtuality has direct access to the data sources and APIs, because in this case the effort to export the data and import them can be saved.
This post was sponsored by DataVirtuality.
