Democratising data is about putting more data in the hands of more staff and employees. The belief being, by distributing data and the ability to analyse that data as widely as possible across a workforce, people can apply their own unique experience, perspectives and skills to that data to drive insights into every corner of a business.
However, collating all business data sources into a unified repository that can be accessed by analytics tools is a complex and costly endeavour. And beyond the cost and complexity of building data warehouses, it’s very difficult to add new data sources once the architecture has been designed and built.
Data virtualisation is a way to solve this cost and agility problem. Thinking of data virtualisation as a software-defined data warehouse may not be technically accurate, but from a conceptual point of view, it encapsulates the idea quite well. Data virtualisation creates a unified data pool from all and any data sources, using federation to make it appear like one accessible pool of data and abstraction to transform that data from complex IT structures to more analytics friendly data assets. Because of the “software-defined” nature of this approach, adding in new data sources is far simpler.
The net effect is that data virtualisation is a catalyst for data democratisation. Simply put, companies that implement data virtualisation are able to put a much larger and varied amount of data into the hands of more staff. Ultimately, a workforce that is armed with data and insights is the key to delivering business decision-making agility. In essence, it’s the catalyst for becoming a data-driven company.
Here are the steps by which data virtualisation leads companies down the path to democratising data across their workforce.
Providing Complete Information – Data virtualisation enables analysts to access all relevant data to build a complete picture of any area on which they are focusing. Large businesses today literally have thousands of data sources spread over hundreds of data silos, both internally and externally. Data virtualisation is the easiest way to get a consolidated view of such varied, expanding data sources. Putting such a complete picture in the hands of analysts increases their ability to make better-informed decisions.
High Quality Information – It goes without saying that democratising low quality or erroneous data is not going to deliver business decision-making agility. It will cause procrastination and reduce confidence in decision-making processes. Data virtualisation uses a number of techniques to improve data quality, and thus providing more accurate and consistent data for the analytics applications that use it.
Actionable Information – Time-to-action and time-to-decision can be the most powerful aspects of democratised data. The more current the data you analyse, the more powerful it is in making real-time actionable decisions. Data virtualisation gives access to original source data, in effect giving analysts access to completely up-to-date, real-time data. This actionable information allows decisions to be made on the fly in response to events as they happen.
Adaptable Information – This is the final part of the jigsaw for modern, data-driven businesses. The data sources that we need to put in the hands of our staff are no longer static. They change and increase with alarming rapidity. Typical methods of gathering data for analytics can’t cope with that variety and change. Data virtualisation can and does accommodate new and varied data sources with ease, ensuring that the right data continues to find its way to the right people.
Data democratisation is nothing more than an executive level decision to support data enabling the workforce. Once the direction has been set, companies like TIBCO are using data virtualisation technology today to turn an executive aspiration into a functioning reality.
You can read more about Data Virtualisation here